COMMUNICATION AND INTERACTIVITY IN B2B RELATIONSHIPS
by
Micah Murphy
A Dissertation Submitted to the Faculty of
The College of Business
In Partial Fulfillment of the Requirements for the Degree of
Doctor of Philosophy
Florida Atlantic University
Boca Raton, Florida
December 2013
Copyright by Micah Murphy 2013
ii
ACKNOWLEDGEMENTS
I would especially like to acknowledge my dissertation chair Dr. C.M. Sashi for
his intellectual insights, patience, and encouragement. He has provided me with
guidance, advice, and support along every step of my research. I would also like to
acknowledge the members of my dissertation committee: Dr. Pradeep Korgaonkar, Dr.
James Gray, and Dr. Allen Smith. Dr. Korgaonkar is responsible for my interest in
communication and interactivity and I am grateful for his support and encouragement.
Dr. Gray has kept me focused on my goals and grounded in reality. Dr. Smith has
provided me with feedback and ideas that helped me to have greater confidence in my
research. I am also grateful to Dr. Shaw, Dr. Iyer, Dr. Castro and Dr. Mishra who shared
their knowledge with me and gave me the tools to be a confident and capable researcher
and instructor.
Without the help of the marketing department support staff, my journey in the
PhD program as well as this dissertation would not have been to completed, so I’d also
like to thank Kathleen Basille and Peggy Cresanta. I would like to express my gratitude
to my Grandparents Don and Maxine Springer who were a great comfort to my family
after we moved so far from familiar faces. Finally, I would like to thank my parents
Michael and Sue Murphy for all of their support, encouragement, and patience. I am
beginning to have the kind of confidence in myself that you have always had in me. iv
ABSTRACT
Author:
Micah Murphy
Title:
Communication and Interactivity in B2B Relationships
Institution:
Florida Atlantic University
Dissertation Advisor: Dr. C.M. Sashi
Degree:
Doctor of Philosophy
Year:
2013
This research explores the impact of interactive communication on business-tobusiness (B2B) relationships. In the past decade the internet and especially social media
as a mode of communication has grown rapidly in both consumer and business markets.
Drawing on marketing channels and communications literature this paper identifies the
dimensions of interactive communication and develops a theoretical framework to
examine their impact on satisfaction, commitment, and advocacy. Media synchronicity
theory and the concept of the internet as an alternative to the real world are used to
distinguish between digital and non-digital modes of communication. Relationship
marketing is used to identify the dimensions of interactivity: rationality, social
interaction, contact density, and reciprocal feedback. The framework developed is used
v
to explore the influence of face-to-face (F2F), digital, and traditional, impersonal
communications on the dimensions of interactivity.
Hypotheses linking the mode of communication: personal, digital, and
impersonal with the dimensions of interactivity and relational outcomes are empirically
examined with data from the commercial printing and graphic design industry.
Confirmatory Factor Analysis is used to analyze the measurement and structural model.
Personal, F2F communication has the greatest impact on social interaction, reciprocal
feedback, and number of contacts. Digital communication has a weaker effect on these
dimensions and impersonal communication has the weakest effect. Personal and Digital
have equal impacts on rationality and rationality is the only dimension of interactivity
positively associated with relationship satisfaction. Contact density has a negative impact
on relationship satisfaction and this negative impact is greater with personal
communication that it is with digital. The study shows that affective commitment leads to
advocacy in a B2B channel, but trust and calculative commitment have no impact on
advocacy. The findings of the study have implications for both managers and researchers
regarding the mode and content of communications in B2B relationships.
vi
DEDICATION
I dedicate this manuscript to my wife Jesi and our wonderful children Gracie, Mary, and
Reagan. They have encouraged and supported me from the moment we packed up our
belongings and moved from our longtime home to Florida. We have celebrated
achievements, shared in setbacks, made new friends, and enjoyed sharing our lives with
each other during this process. Thank you for trusting me, loving me, and for being so
fun. You guys are awesome.
The best preparation for the future is the present well seen to, and the last duty done.
George MacDonald
COMMUNICATION AND INTERACTIVITY IN B2B RELATIONSHIPS
LIST OF TABLES ....................................................................................................... VIII LIST OF FIGURES ........................................................................................................ IX CHAPTER 1: INTRODUCTION ....................................................................................1 Communication Model ....................................................................................................... 2 B2B Markets ....................................................................................................................... 4 Relationship Marketing....................................................................................................... 5 CHAPTER 2: INTERACTIVE COMMUNICATION.................................................15 Rationality and Social Interaction ..................................................................................... 27 Reciprocal Feedback ......................................................................................................... 29 CHAPTER 3: ADVOCACY IN B2B RELATIONSHIPS............................................33 CHAPTER 4: METHODOLOGY.................................................................................45 Survey Instrument ............................................................................................................. 45 Pilot Study......................................................................................................................... 50 Structural Equation Modeling ........................................................................................... 55 Data Collection ................................................................................................................. 55 Sample............................................................................................................................... 56 CHAPTER 5: RESULTS ................................................................................................61
vii
Assessing non response bias ............................................................................................. 61 Measurement Model ......................................................................................................... 62 Convergent Validity .......................................................................................................... 65 Construct Reliability ......................................................................................................... 66 Discriminant Validity........................................................................................................ 68 The Structural Model ........................................................................................................ 72 Hypotheses Testing ........................................................................................................... 73 Personal, Digital, and Impersonal Communication .......................................................... 74 CHAPTER 6 DISCUSSION AND IMPLICATIONS...................................................83 Summary of Study ............................................................................................................ 83 Discussion ......................................................................................................................... 84 Limitations ........................................................................................................................ 88 Implications....................................................................................................................... 89 APPENDIX 1 ....................................................................................................................93 REFERENCES...............................................................................................................101 vii
LIST OF TABLES
TABLE 1: COMMUNICATION RESEARCH IN MARKETING..................................11
TABLE 2 CONSTRUCTS.................................................................................................46
TABLE 3 MEASUREMENT SCALE ITEMS..................................................................47
TABLE 4 INITIAL EFA OF NEW SCALES .................................................................511
TABLE 5 MODIFIED SCALES .......................................................................................53
TABLE 6 LOYALTY AND CALCULATIVE COMMITMENT EFA ..........................534
TABLE 7 SCALE RELIABILITY AND MODIFICATIONS ........................................544
TABLE 8 TESTS FOR NON-RESPONSE BIAS. ........................................................622
TABLE 9 CFA RESULTS...............................................................................................667
TABLE 10 CHI SQUARE DIFFERENCE BETWEEN THE CONSTRAINED AND
UNCONSTRAINED PAIRWISE MODELS. ...................................................................71
TABLE 11 STRUCTURAL MODEL FIT STATISTICS .................................................73
TABLE 12 PAIRWISE COMPARISON OF PATHS .......................................................74
TABLE 13 TOTAL AND INDIRECT EFFECT PATHS .................................................80
TABLE 14 SUMMARY OF RESULTS............................................................................81
viii
LIST OF FIGURES
FIGURE 1 THE INTERACTIVITY OF DIFFERENT MODES OF
COMMUNICATION.......................................................................................................166
FIGURE 2. INTERACTIVITY AND THE DIMENSIONS OF INTERACTIVITY.......22
FIGURE 3 B2B INTERACTIVE COMMUNICATION ..................................................39
FIGURE 4 INTERACTIVE COMMUNICATION IN B2B RELATIONSHIPS ............44
FIGURE 5 CONSTRUCT CONSTRAINED TO UNITY ...............................................69
FIGURE 6 CONSTRUCT FREE TO VARY ....................................................................70
FIGURE 7 THEORETICAL MODEL AFTER CFA ......................................................722
FIGURE 8 PERSONAL COMMUNICATION PATHS ...................................................75
FIGURE 9 DIGITAL COMMUNICATION PATHS .....................................................766
FIGURE 10 IMPERSONAL COMMUNICATIONS PATHS ........................................777
FIGURE 11 DIMENSIONS OF INTERACTIVITY PATHS ..........................................78
FIGURE 12 PERSONAL AND DIGITAL TOTAL EFFECT PATHS ............................79
ix
CHAPTER 1: INTRODUCTION
Web 2.0 communication technologies such as Twitter, LinkedIn, Facebook,
Chatter, Facetime, and Google Docs are all relatively new forms of interactive
communication. The use of these social media has increased rapidly over the last decade.
For example, the social media site LinkedIn was founded in 2003 and by 2013 had 225
million business people including executives from each of the Fortune 500 companies
registered on the site (Hempel 2013; LinkedIn 2011). Besides increasing the number of
alternatives available to managers, these new media can enable greater information
sharing within as well as between organizations. This information sharing may include
instrumental information like delivery times or product availability (Joshi 2009; Sheng et
al. 2005), but it can also involve more social interactions and stronger social bonds (Berry
1995; Palmatier et al. 2006b). Using theory from marketing channels(Duncan and
Moriarty 1998; Joshi 2009; Macneil 1981; Mohr and Nevin 1990; Moriarty and Spekman
1984) and communication (Daft and Lengel 1986; Dennis and Valacich 1999), we
develop a framework to examine social media’s impact on business-to-business (B2B)
relationships. This study investigates the influence of interactive communication on
relationship satisfaction, affective and calculative commitment, and advocacy.
The relationship marketing literature indicates that communication is one of the
most effective relationship building strategies (Palmatier et al. 2006a). While the use of
1
digital media appears to be increasing; academic scholarship on its use in business
relationships is sparse. Effectively listening and responding to customers can have a
dramatic impact on a firm’s ability to compete (Duncan 1972; Ramani and Kumar 2008)
and this listening and responding can increasingly be done on the internet and social
media sites. Communication has been included as a variable in theoretical e.g. (Duncan
and Moriarty 1998; Mohr and Nevin 1990) and empirical work e.g. (Palmatier et al.
2006a) in relationship marketing, and it has been shown to be a key determinant of
outcomes in B2B relationships (Anderson and Narus 1990; Grewal et al. 2001; Mohr and
Spekman 1994; Mohr et al. 1996; Palmatier et al. 2007; Palmatier et al. 2006a; Palmatier
et al. 2006b). With the advent of increasingly interactive, two-way communication,
understanding the dimensions of interactive communication and their impact on relational
exchange can aid managers in developing competitive advantage.
Communication Model
A commonly used model of communication was developed by Lasswell (1948).
Several researchers in marketing have further developed this model to investigate
communication (Andersen 2001; Duncan and Moriarty 1998; Mohr and Nevin 1990;
Mohr and Spekman 1994). According to the model, communication is initiated by a
source and sent through a medium or channel to a receiver. The goal of an efficient
communication process is to have the information that was sent be identical to the
information that was received. The source encodes a message into a medium and
transmits it to a receiver who reacts to the message. Based upon the cybernetics literature,
feedback was added to the model in order to measure a recipient’s reaction to a
2
communication (Wiener 1988). Feedback in this sense could refer to a purchase or a
change in attitude or a message sent from the original recipient to the source. The model
was developed to investigate how a message could be transmitted with the least amount
of “noise” or distortion. Previous research has demonstrated the importance of each of
these five elements: source, receiver (Moriarty and Spekman 1984), content of messages
(Mohr et al. 1996), media used (Hoffman and Novak 1996), and feedback (Joshi 2009).
This study will develop a comprehensive model of interactive communication that
includes all five elements of Lasswell’s model.
When the feedback is provided in the form of a communication which refers to a
previous message, the communication becomes interactive. The computer mediated
communication (CMC) research focuses on the interactivity of a web site with two-way
communication being the key variable. CMC describes the dimensions of interactivity as:
user control; two-way communication; and responsiveness (Cyr et al. 2009; McMillan
and Hwang 2002; Mitrega and Katrichis 2009 ; Mollen and Wilson 2010; Song and
Zinkhan 2008; Yadav and Varadarajan 2005). One proposed definition of interactive
communication is the “transmission of a message associated with previous exchanges”
(Song and Zinkhan 2008). Internet enabled communication platforms, like social media,
have increased the capabilities for interactive communication (Hoffman and Novak
1996).
In consumer markets, communication research has focused on advertising
(Schultz 1998; Tellis 2004) and promotion (Gardener and Trivedi 1998). Advertising
3
scholars have examined the mode of communication based on its reach and synchronicity
(Liu and Shrum 2002). Tellis (2004) has discussed the source of information based on
credibility, the receiver of information based upon demographics such as age or
nationality, the content of a message like a comparison or emotional content, the
feedback based upon behavior or behavioral intentions, and the frequency of exposures to
a message. In a review of the advertising literature, Vakratsas and Ambler (1999) show
that emotional content of an ad enhanced consumer preferences. Duncan and
Moriarty(1998) discuss the importance of communicating a consistent message through
advertising as well as with suppliers and other stakeholders. Following Duncan and
Moriarty’s suggestions, Reid (2005) shows that integrating communication messages
across all stakeholders including suppliers, customers, and employees has a positive
impact on sales and customer satisfaction.
B2B Markets
We distinguish between consumer and business markets based upon the nature of
the transaction. B2B markets can be defined as markets where intermediate transactions
take place (Sashi 1990). Intermediate transactions are those exchanges that take place in
order to add value or to aid in adding value to a subsequent output market transaction.
The intermediate transactions are always followed by subsequent market transactions.
Final transactions refer to transactions where no subsequent transactions are expected
(Sashi 1990; Sashi and Stern 1995) . For example, if a firm purchases fabric t-shirts in an
input market adds graphic designs and then sells them in a subsequent output market
transaction, the purchase is an intermediate transaction because it is used in a subsequent
4
transaction. T-shirts purchased by a household, on the other hand, are a final transaction
in the B2C market because these T-shirts are not sold in a subsequent output market
transaction. This distinction holds even if the final buyer were to embellish the design of
the t-shirts because there is no subsequent output market transaction. B2B markets are
those markets where intermediate transactions take place.
According to Sheth (1973), compared to a B2C market, B2B market transactions
take longer, cost more, and require more input from buyers who must engage in joint
decision making. In B2B markets, the buyer is frequently a group or buying center,
rather than an individual (Anderson et al. 1987; Johnston and Bonoma 1981), and the
composition of this buying center plays a key role in determining purchasing behavior
(Sashi 2009). Cyert says that “organizational decision-making requires a variety of
communication activities that are absent when a decision is made in a single human
head” (Cyert et al. 1956 p. 246). Members of the buying center can have different
expectations and occupy different roles in the organization as well as the buying process.
Organizational roles refer to a position within the company or a particular department
while a buying center role is the position within the buying center such as user or
influencer (Moriarty and Spekman 1984; Sashi 2009).
Relationship Marketing
Communication’s importance stems from its function in coordinating channel
activities (Anderson and Narus 1984; Mohr and Nevin 1990), increasing trust and
commitment (Morgan and Hunt 1994), enabling rational decisions, enhancing positive
emotions (Anderson and Narus 1990; Mohr and Spekman 1994), driving brand value,
5
identifying customer’s needs (Duncan and Moriarty 1998), and reducing uncertainty
(Moriarty and Spekman 1984). Increased levels of communication aid in the development
of group benefits (Peters and Fletcher 2004) and change the focus of exchange from
transactional to more relational (Mohr and Nevin 1990). According to Groonroos (1996)
interaction is the set of joint activities that take place in a relationship. Interactive
communication is a two-way or joint-activity (Duncan and Moriarty 1998) and the back
and forth dialogue “merges speaking and listening” (Rafaeli and Sudweeks 1997 p. 1).
Managing interactions has the potential to increase seller profitability as well as customer
satisfaction (Ramani and Kumar 2008), which can lead to interactions being a source of
competitive advantage (Rayport and Jaworski 2004).
The core of relationship marketing is interaction and a planned communication
process can support relationship development (Groonroos 2004; Prahalad and
Ramaswamy 2004; Vargo and Lusch 2004). Morgan and Hunt defined relationship
marketing as “marketing activities directed toward establishing, developing, and
maintaining successful relational exchanges” (Morgan and Hunt 1994, p 22).
According to Gummesson, “networks are sets of relationships, and interaction refers to
the activities performed within relationships and networks” (1996 p 32).
Communication is an interactive relationship building strategy (Anderson and Narus
1990; Mohr and Nevin 1990). Frazier and Sheth (1985) and Frazier and Summers
(1986) show that higher levels of interdependency results in greater information
exchange and fewer direct influence attempts indicates that closer relationships are
associated with less direct influence. Anderson and Narus (1990) show increased
6
communication is positively associated with cooperation. Several other researchers also
show that communication enhances cooperation in B2B relationships (Lui and Ngo 2005;
Morgan and Hunt 1994; Sivadas and Dwyer 2000).
Communication has been one of the primary drivers of relationship marketing
(RM) outcomes. Increasing the understanding of RM’s primary drivers can improve
businesses’ returns on investments and aid researchers in developing comprehensive
models of RM’s impact on business performance (Palmatier et al. 2006a p 136). Within
the marketing literature, RM efforts are assumed to enhance sales, profits, and growth
(Anderson and Weitz 1992; Berry 1995; Dwyer et al. 1987; Morgan and Hunt 1994;
Parvatiyar and Sheth 2000). In their examination of social, structural and financial
relationship investments, Palmatier et al (2006) found that social investments resulted in
the highest customer specific return, followed by structural relationship investments
combined with a high degree of interaction. The findings show that social interaction and
communication is valuable to an organization. According to Mohr (1990) the
combination of high frequency, bidirectional, informal, and indirect influence is
associated with more relational exchange, while lower frequency, unidirectional, more
formal, and more direct content is associated with comparatively less relational or market
exchange. Macneil (1981) says that in the presence of more contacts the social
component of interactions will become more important and exchange will become more
relational.
7
Due to the time and effort required to develop a shared language and shared
patterns of communication, interfirm communications are a relationship specific
investment (Williamson 2008). Developing these communication patterns requires
mutual idiosyncratic investments which reduce opportunistic behavior (Heide and John
1992). Comparing dealers that were company-owned, franchisees, and manufacturercontrolled meaning that the dealer had acquiesced decision making authority to the
manufacturer, Mohr (1996) demonstrates that the beneficial impact of communication is
reduced in the presence of greater integration. Jap (2011) demonstrated that F2F
communication makes opportunistic behavior more difficult to identify and it also makes
opportunistic behavior more likely when compared to other media. Since digital media
usually contain storage and retrieval capabilities, opportunistic behavior can be more
easily identified and is less likely (Rindfleisch et al. 2010).
Examining B2B relationships, Mohr and Nevin (1990) develop four facets of
communication: (1) frequency, which refers to the amount or number of communications;
(2) direction, which refers to the degree of bidirectionality in communications; (3)
modality, which refers to whether the media used is formal or part of standard operating
procedure and (4) content, which refers to the direct or indirect influence attempt in the
message. Mohr and Nevin (MN) then propose that greater frequency, more bidirectional,
more informal, and more indirect communication will result in enhanced cooperation and
satisfaction. Utilizing the facets they developed, Mohr et al. (1996) demonstrate that
bidirectional communication, informal influence attempts, formal communication and
frequency contribute to collaborative communication, which has a positive impact on a
8
dealer’s satisfaction, commitment, and coordination with a manufacturer. In another
study, Mohr and Sohi (1995) show that communication quality, formality,
bidirectionality, and frequency are positively associated with satisfaction, but only
communication frequency is positively associated with communication quality.
Sivadas and Dwyer (2000) show that frequent communication across functional
areas is a core competency in the development of new products (Roy et al. 2004).
According to Vargo and Lusch (2004), individuals in buyer and seller organizations need
to interact and adapt to each other and it is through this interactions that co-creation takes
place. Ballantyne and Varey (2006) suggest that dialogue or conversation is the only way
learning can take place and is the best way to identify opportunities and new ideas. One
of the benefits of this joint learning is the development of shared values (Morgan and
Hunt 1994) and expectations (Meyer and Rowan 1977), and regularly meeting these
expectations can lead to a long-term relationship.
According to Slater and Narver (1995), removing functional barriers improves
learning in organizations. Information is dispersed throughout an organization and
participation increases decision quality (Jones 1997; Lawler 1999; Moriarty and Spekman
1984), and online groups have been shown to support participation in decision making
(Kiesler and Sproull 1992). Since F2F communication has the constraint of needing
everyone at the same place and time (Daft 1983), it is unlikely that large groups will
consistently be able to communicate this way.
9
Using theory from marketing channels (Duncan and Moriarty 1998; Joshi 2009;
Macneil 1981; Mohr and Nevin 1990; Moriarty and Spekman 1984) and communication
(Daft and Lengel 1986; Dennis and Valacich 1999), we develop a framework to examine
social media’s impact on B2B relationships. The purpose of this paper is to: (1) identify
and distinguish digital media from other modes of communication (2) identify the
dimensions of interactive communication; (3) develop a theoretical framework for
examining interactive communication’s influence on relationships; (4) examine how
interactive communication influences satisfaction and calculative and affective
commitment in B2B relationships and; (5) examine how these relational outcomes
influence advocacy. The research is organized in seven chapters. Chapter 2 makes a
distinction between digital and non-digital communication and further distinction of nondigital communication as either personal or impersonal. Chapter 3 develops the
dimensions of interactive communication: buyer and supplier contacts; social interaction;
rationality; and reciprocal feedback. Hypotheses linking the dimensions of interactivity
with personal, digital, and impersonal are developed. Chapter 4 develops the outcomes
of communication: satisfaction; affective commitment; trust; loyalty; calculative
commitment; and advocacy. Hypotheses linking these outcomes with the dimensions of
interactivity are developed. In Chapter 5 the methodology used for data collection is
described and an empirical analysis of the data is presented. Chapter 6 presents the
empirical results of the hypotheses tests and in Chapter 7 we discuss the results and
implications of the study.
10
Table 1 is an overview of the marketing literature as it relates to interactivity and
communication. The table outlines the aspect(s) of communication the study proposes to
measure, the author, whether the work is conceptual or empirical, and the findings.
Communication has been a key variable in B2B research and it has been shown to have a
positive impact on relational outcomes such as satisfaction, commitment, trust,
cooperation and coordination. Communication has a positive impact on relationships
(Mohr and Nevin 1990), but not all communication has the same impact. The web is
unique in its ability to facilitate many-to-many communication, and beginning with
previous research on communication this study will develop a theoretical framework to
empirically investigate interactivity’s impact on B2B relationships.
Table 1: Communication Research in Marketing
Communication
Measurement
Author
Type
Findings
Number of
participants
MacNeil
(1980)
Conceptual
The number of contacts (+)
relational exchange
Quantity, scope,
mode
Roy et al.
(2004)
Conceptual
Information
Sharing,
bidirectionality,
formality, influence
attempt
Mohr, Fisher,
and Nevin
(1996)
Empirical
Interaction (+) radical and
incremental supply chain
innovation
High levels of control and
integration weaken the positive
effects of collaborative
communication. Formal
communication is a dimension
of collaborative communication
11
Frequency,
Bidirectional,
Content,
Mode(channel)
Mohr & Nevin
(1990)
Bilateral, Amount,
Quality
Palmatier et al.
(2006)
*Two-way
communication,
Reaction,
Interaction
Song (2008)
Information
Sharing
Sivadas &
Dwyer (2000)
Empirical
Communication, Trust, and
Coordination together form
Cooperative Competency
which is (+) to NPD but (n.s.)
for mode of governance.
Information
Sharing
Duncan &
Moriarty
(1990)
Fisher, Maltz,
and Zaworski
(1997)
Empirical
Communication is (+) to Trust
and cooperation (+) Satisfaction
Empirical
Bidirectional communication
(+) norms of information
sharing, and relationship length
*Two way
communication,
Synchronicity,
Active Control
Liu and Shrum
(2002)
Conceptual
Synchronicity, Active Control,
and Two way communication
are + related to User
satisfaction
*Two way
communication,
Synchronicity
McMillan and
Hwang(2002)
Empirical
Users expect internet ads to
provide real-time, two-way
communication similar to F2F
communication
Action and
Reaction
Lui and Ngo
(2005)
Empirical
frequency,
Bidirectional,
Content,
Mode(channel)
Sindhav and
Lusch (2008)
Empirical
future research should focus on
interaction patterns during
cooperation
Communication is + related to
Coordination and identification
Bidirectional
Conceptual
Communication (+)
coordination, satisfaction, and
commitment AND these are (+)
with performance
Informal communication (+)
relational exchange
Meta-analysis Communication (+)
commitment, trust, relationship
satisfaction, relationship quality
Empirical
Interactivity is + related to site
effectiveness, Personalization is
+ related to perceptions of
interactivity
12
Instrumental,
Social,
formalization,
centralization,
participation
Sheng, Brown,
and Nicholson
(2005)
Empirical
**Bi Directionality
Peters(2004)
Empirical
Economic, Social,
and Structural
Bonds
frequency,
formality,
information
sharing
HarrisonReview
Walker, Neeley
(2004)
Paulraj et al
Empirical
(2008)
Online CRM may raise
customer expectations
regarding responsiveness
Communication is (+) to buyer
and supplier performance.
LTO, Network Governance,
and Information Technologies
(+) to performance is mediated
by communication.
Quality,
Information
Sharing,
Participation
Mohr, and
Spekman
(1994)
Empirical
PersonalImpersonal and
Commercial- Non
Commercial
Moriarty and
Spekman
(1984)
Empirical
Communication Quality (+)
satisfaction and Dyadic sales.
Information Sharing (-)
satisfaction with profits,
Information sharing (+)
participation.
Innovative companies rely
more on personal and
commercial sources. Greater
use of a number of different
communication channels (+)
coordination and synergy in
promotion
Communication
Anderson and
Narus (1984)
Anderson and
Narus (1990)
Dahlstrom et
al. (1996)
Empirical
Communication
Participation,
formalization
Empirical
Empirical
13
Centralization, Participation,
Formalization and trust (+)
Instrumental communication,
Centralization (-) instrumental
communication, Participation
and trust (+) Social
communication.
Bi directional communication
(+) team cohesion, formality (-)
frequent communication
Communication (+)
Cooperation
Communication(+) trust and () conflict
Communication
(+)performance
Channel, Feedback, Duncan and
Moriarty
Information,
(1998)
Signals,
interactivity
Conceptual
Communication based
Relationship Management may
enable companies to drive
brand value and identify and
prioritize contact points.
Communication is a facet of
Cooperative Competency along
with trust and Cooperation.
Inter and intra-firm Cooperative
Competency is (+) with NPD
Collaborative Communication
(+) continuous supplier
improvement, supplier affective
commitment, and supplier
knowledge.
Communication
Internal /external
Sivadas and
Dwyer (2000)
Empirical
Frequency,
reciprocity,
formality, influence
Joshi (2009)
Empirical
Information
Sharing
Morgan and
Hunt (1994)
Empirical
Many-to-Many
Hoffman and
Novak (1996)
Conceptual
14
Communication (+) termination
costs, Relationship benefits,
Shared Values, commitment,
trust, cooperation.
Communication is (-)
Opportunistic behavior,
Propensity to leave, uncertainty
The internet facilitates manyto-many communication
CHAPTER 2: INTERACTIVE COMMUNICATION
In this chapter we distinguish between different modes of communication.
Different modes of communication allow different degrees of interactivity. The degree to
which communication is two- way is the degree to which it is interactive and the mode of
communication determines the level of interactivity that is possible. The medium or
mode of communication refers to how a message is transmitted. Modes of
communication have been investigated based on their ability to transmit rich information,
speed of transmission, synchronicity, ability to support two-way communication (Dennis
and Valacich 1999), and whether the mode is formal or informal. Our model examines
the mode of communication based upon the level of interactivity with the most
interactive being personal, which is F2F, followed by digital, and finally impersonal,
which is traditional non -digital communications. In addition to the mode of
communication, our model includes dimensions developed from relationship marketing
that focus on how B2B relational ties develop. The dimensions of interactive
communication developed are: (1) buyer contacts; (2) supplier contacts; (3) rationality;
(4) social interaction, and; (5) reciprocal feedback.
Figure 1 is a schematic representation of the level of interactivity that provided by
each of the three modes of communication. Interactivity is the highest with personal
15
communication. It remains high with digital, which can mimic F2F, and is lowest
with impersonal communication.
Figure 1: The Interactivity of Different Modes of Communication
Previous research has primarily focused on the formality of the communication
mode. Formality is the degree to which a communication channel is routinized and
operates under formal procedures. Formal communication may aid in reducing the
cognitive load of participants by decreasing their options or the number contacts, it also
means that shared meaning would need to be created in a structured and official manner.
However, unplanned and informal communication has been shown to be faster and more
reliable (Crampton et al. 1998) and reliability is an important aspect in increasing the
accuracy and value of predicted outcomes of an interaction (Sunnafrank 1986). Informal
F2Fcommunication allows participants to adjust meanings and interpretations in order to
find their common ground. Within interpersonal relationships, informal communication
is the norm, while in a discrete transaction, such as the spot market, we would expect to
see only formal communication. Despite this consideration, Mohr, Fisher, and Nevin
16
(1996) suggest that more formal communication is associated with collaborative
communication, while Anderson and Wietz (1989) say that informal communication is
associated with goal congruence and reducing role ambiguity. In relational exchange both
types of communication are necessary; formal communication is essential to carrying out
established routines while informal is necessary for developing these routines. Formal
establishes legitimacy while informal contributes to trust (Anderson and Weitz 1989;
Mohr and Nevin 1990).
The MN model utilized the formal/informal distinction “because it had been
widely used in empirical and conceptual research” (Mohr and Nevin 1990 p. 39).
According to their distinction, formal communication is “somehow connected to the
organization in a structured routinized manner” and “generally” refers to written channels
but may include formal meetings. The research shows that both are necessary, but the
distinction appears ambiguous. For example, items for measuring formal and informal
channels are described as “regularized, structured modes versus casual, informal, wordof-mouth modes” (Mohr et al. 1996 p. 113). It is not clear whether a survey respondent
would consider a company paid lunch between a purchasing agent and sales rep to be
formal or informal just as it is not clear as to whether an email or even a phone
conversation to discuss and plan for an upcoming meeting would be considered formal or
informal. Previous research suggests that informal communication is associated with
more relational exchange while formal is a dimension of collaborative communication
(Mohr and Nevin 1990; Mohr et al. 1996). Examining sources of information Moriarty
and Spekman say, “the distinction between personal and impersonal sources of
17
information is discerned rather easily and is based exclusively on F2F versus any other
type of communications vehicle.”(1984 p. 138) We follow Moriarty and Spekman’s
example and refer to F2Fcommunication as personal communication and impersonal
communication as traditional documents including reports, correspondence, memos, and
sales literature.
Media richness theory aims to explain which channel or mode of communication
is best utilized under different conditions (Daft 1983; Daft and Lengel 1986). According
to the theory, if the information being exchanged is ambiguous and does not lend itself to
being easily codified, a richer method of communication will be required. The richness
of communication depends on the number of additional cues present. F2F communication
provides the greatest level of richness by providing the greatest number of cues in
addition to the actual words used for communication. These cues are capable of
communicating hedonic emotion, surprise, gratitude, anger, and confusion. Examples of
cues include: tone of voice (Scherer 1986), posture, head nods (Wallbott 1998), and
facial expressions (Ekman 1999). Daft did not make the formal/informal distinction based
on whether a meeting was scheduled or part of a structured policy, but instead, his
distinction was based on the richness of the media. However, according to Daft, formal
communication was primarily text-based letters and documents, while informal referred
to F2F meetings. In addition, as the media became more informal, it also became richer.
Daft (1983, p.19) argues that manager’s prefer and use rich communication channels
because they “ provide insight into the intangible, social dimensions of the
organization.” Referring to Weick (1979), Daft and Lengel (1986) state that when
18
managers perceive a change or event in the environment, they do not immediately know
how to interpret the event. Instead, they discuss it and come to an agreement or mutually
informed interpretation of its meaning (Day 1994). This sensemaking may include
several lateral and vertical discussions (Johnston and Bonoma 1981)
Other aspects of the media that have been examined are the level of synchronicity,
the level of permanence and the ability to maintain context or refer to past messages.
These studies also highlight the importance of feedback channels and the ability to
interrupt (Oviatt and Cohen 1991; Whittaker et al. 1991). Synchronicity refers to the
amount of time it takes to send and then receive a message (Song and Zinkhan 2008).
Synchronicity is a feature of live dialogue or conversation that is now available through
real time computer mediated systems. F2F communication requires synchronicity, digital
can be synchronous or asynchronous, and traditional communication is always
asynchronous. When restricted to non-digital media such as a letter, or F2F the choice of
media determines the level of synchronicity. Digital media frequently allows users to
determine their level of synchronicity. For example, when sending an email or an instant
message, the delivery of the original message can be immediate and responses can be
immediate or postponed. According to Media Richness Theory, this type of
communication would result in misunderstandings, but this may depend on experience
(Carlson and Zmud 1999). In some cases digital is preferred and more productive than
F2F communication (Hollan and Stornetta 1992). One reason for this is that digital
communications, like instant messaging, are less intrusive and disruptive to an
individual’s workflow than F2F communications (Nardi et al. 2000).
19
The ability of digital communication to allow multiple conversations between
multiple senders and receivers has been investigated using media synchronicity theory
(MST) as an alternative to media richness theory. MST employs two constructs to explain
media choice. Convergence refers to the ability to enhance mutual agreement and
Conveyance refers to the ability to process information (Dennis and Valacich 1999).
These two constructs depend upon five characteristics of the media. First, is the
Immediacy of Feedback, which is the ability of the media to support fast, bidirectional
communications. Second is the Symbol Variety, which refers to the cues a medium can
transmit including formats such as voice, text, and video. Third is the Parallelism or
number of concurrent, bidirectional messages the media can support. Fourth is the
Rehearsability or the capability to edit the message before sending it. Fifth is the
Reprocessability, which is the capability of the medium to support reprocessing of a
message while retaining the context. According to Dennis and Valacich (1999), digital
media like email and groupware can support multiple conversations at one time, which is
referred to as Parallelism, and still allow for reprocessing of information. At the same
time, traditional communication like F2F is low on both Parallelism and
Reprocessability. Using digital communication, Reprocessability allows interactive
communication to take place while offering both sender and receiver control over the
timing of a response or feedback. F2F requires immediate feedback. This control over
feedback means users are able to interact and keep the original context of the interaction
over time (Oviatt and Cohen 1991; Whittaker et al. 1991). This further allows users to
take part in multiple dialogues at once. Some of these conversations may require
20
feedback the same day while other conversations can be resumed weeks later. Digital
communication is unique in its ability to: (1) support Parellelism or multiple two-way
conversations ;( 2) allowing for Reprocessability or control over the timing of a response
without losing the context.
Hoffman and Novak advise that “marketers must carefully consider the ways in
which advertising and communication models can be adapted and reconstructed for the
interactive, many-to many medium” (1996p. 65). We distinguish between digital and
non-digital communication based upon Hoffman and Novak (1996) and Dennis and
Valacich’s (1999) Media Synchronicity Theory. According to HN the internet can be
viewed as a place where individuals are present and capable of interacting in a way that is
similar to F2F interaction. Hoffman and Novak (H&N) define the hypermedia
environment of the internet as “a network, potentially global in scope, together with
associated hardware and software for accessing the network, which enables consumers
and firms to: (1) provide and interactively access hypermedia content and; (2)
communicate through the medium” (Hoffman and Novak 1996p. 53).
Based upon the concepts of MST and the work of Hoffman and Novak the
interactive, many-to-many capabilities distinguish digital from non-digital
communication. Digital communication is the exchange of information between two or
more individuals through a multimedia device connected to the internet and it can mimic
personal communication as well as written impersonal communication. When an RFP is
emailed to a list of prospective suppliers or posted on a website, digital communication is
21
impersonal. When some of these potential suppliers respond with questions the
communication becomes interactive and the ensuing dialogue becomes more like
personal sales. The ability of digital communication to allow many of these dialogues to
take place at one time increases the number of interactions beyond what could be
accomplished with F2Fmeetings.
We have made a distinction between digital and non-digital modes of
communication and a further distinction between personal and impersonal modes of nondigital communication. While the distinction between video and email may be clear,
distinguishing the use of a specific medium based on its capabilities is becoming
increasingly difficult with Web 2.0 technologies. Platforms such as Facebook and
Salesforce.com’s Chatter enable multiple forms of communication such as chat, email,
video, mobile short messaging, and document sharing from a single platform. Nondigital communication, though, can be examined based upon the distinction between
personal and impersonal communication. Personal communications are F2Fwhile
impersonal is all other traditional communication.
Relational ties are formed through interaction and these ties or social bonds play a
role in moving B2B relationships beyond a calculative focus on price and product to a
focus on the intangible benefits of the relationship such as cooperation, commitment, and
coordination (Berry 1995). Internet technology has increased the amount of interactive
communication between firms (Yadav and Varadarajan 2005), and while it can increase
decision quality; it can also result in information overload (Ariely 2000). Determining
22
how to best manage these interactions requires a better understanding of the dimensions
of interactivity and their influence on relationships. Figure 2 is a schematic representation
of the framework being developed. The model developed investigates the impact of these
dimensions on satisfaction, loyalty, calculative commitment, affective commitment, trust,
and advocacy in B2B relationships.
Figure 2: Interactivity And The Dimensions Of Interactivity
Supplier and Buyer Contacts
The source and receiver are conceptualized as supplier and buyer contacts,
respectively. The number of contacts is important because it increases the opportunity for
information exchange and the likelihood of relational exchange (Macneil 1981).
Increasing the number of relational ties may increase the likelihood of identifying profit
opportunities (Palmatier 2008) and the development of shared expectations (Meyer and
Rowan 1977).
Studies of the impact of source characteristics examine how salesperson attitude
and attractiveness influence their ability to persuade customers (DeBono and Harnish
23
1988). Other characteristics, such as credibility and expertise, have also been shown to
enhance the ability of the source to persuade (Harmon and Coney 1982; Wilson and
Sherrell 1993). There are few B2B studies that investigate source or receiver effects on
communication. The exception is the work of Moriarty and Spekman (1984) who
investigate receivers based upon their functional roles and positions in an organization’s
hierarchy. They classify receivers as members of the (1) using department, or members
of the (2) top, (3) middle, or (4) junior management and sources of information as
commercial or non-commercial and personal or impersonal. They show that using
departments are likely to seek out sources of information that are personal and noncommercial and to utilize these sources throughout the purchasing process and that
receivers who are higher in organizational structure of the firm will rely on impersonal,
non-commercial sources of information and that they may have access to a larger pool
information sources.
Involving more contacts in communications improves information sharing and
learning. Vargo and Lusch (2004) say that individuals in buyer and seller organizations
need to interact and adapt to each other and it is through these interactions that cocreation takes place. Mohr and Spekman (1994) found that higher rates of participation
improved information sharing and that communication had a positive effect on sales and
satisfaction. According to Slater and Narver (1995), removing functional barriers
improves learning in organizations where membership in an organizational group is
informal and its structure is the result of interpersonal communication patterns (Spekman
and Stern 1979). Sivadas and Dwyer (2000) demonstrate that frequent communication
24
across functional areas is a core competency in the development of successful new
products regardless of whether the innovation is radical or incremental (Roy et al. 2004).
Information is dispersed throughout an organization (Hayek 1945; Jensen and Meckling
1995)and participation increases decision quality (Jones 1997; Lawler 1999; Moriarty
and Spekman 1984). Since F2F communication has the constraint of needing everyone
at the same place and time, it is unlikely that large groups will consistently be able to
communicate this way. Online groups, connected through social media, have the benefit
of being able to increase the number of participants who can contribute from different
locations at different times. These online groups have been shown to support
equalization and participation in decision making (Kiesler and Sproull 1992).
Macneil (1980) describes non-primary relations as existing only in relatively
discrete transactions, such as the spot market. In these relationships, it does not matter
who the buyer or supplier is. Primary relations, on the other hand, are those with
relational aspects where the person matters. Although contractual relationships may not
become as close as family relationships, they are much closer than non-primary
relationships. Numbers are important in this distinction because large numbers “tend to
preclude the non primary relationships upon which the discrete transaction depends”
(Macneil 1981 p. 1035). According to Macneil, stronger relationships are necessary
when more individuals are present. As the number of contacts between firms increases,
relational ties become necessary (Macneil 1981). The capability to share information
across functional boundaries both within and between firms is a necessary feature of the
market-driving firm (Day 1990; Duncan and Moriarty 1998). Rather than a firm
25
broadcasting or customizing messages aimed at individuals, an interactive process takes
place involving multiple departments and individuals in both the buyer and supplier
organizations. The more contacts the more likely finding pertinent information will be,
and this increased participation leads to a more relational association.
Sources and receivers of information are indistinguishable when using interactive
communication.
An individual may begin as a receiver but as soon as he encodes a
message and responds he becomes a source. Separating source from receiver in a
conversation becomes problematic during two-way communication. The roles alternate
based upon which party is speaking or sending a message and which party is listening or
receiving a message. According to Rafaeli (1997), “interactivity merges speaking with
listening.” Regardless of which party is the source or the receiver, when we look at a
relationship between two firms, we know that each will be a receiver and each will be a
source at some point in their interactions. Rather than source and receiver, we
differentiate between the two parties based on whether they are the buyer or the supplier.
These are the supplier contacts who communicate with the buyer and the buyer contacts
who communicate with the supplier. The importance of contacts has been shown in the
literature on participation and suggested by the early relationship marketing literature
(Macneil 1981; Moriarty and Spekman 1984) as well as more recent literature from
network theory (Palmatier 2008; Slater and Narver 1995).
Palmatier (2008) investigated the impact of contact density on customer value.
Contact density referred to the number of interfirm relational ties, and the research
26
demonstrated that the number interfirm contacts had a direct, positive impact on customer
value. According to network theory, more interorganizational ties is positively
associated with enhanced communication efficiency (Rowley 1997), greater coordination
and cooperation (Oliver 1991), and the development of shared expectations (Meyer and
Rowan 1977). As the number of interpersonal ties between organizations increases,
uncovering and sharing information increases. The benefits of increased ties are
available to both buyers who are able to communicate their needs and suppliers who able
to uncover more profit opportunities (Palmatier 2008). As buyer and supplier contacts
increases, the number of relational ties will increase as well. At the same time there is a
point at which interactions are perceived to be inefficient and a buyer’s perception of
inefficient interactions is positively related to the propensity to switch (Palmatier et al.
2008). More buyer and supplier contacts will increase participation and the number of
relational ties improving decision quality and customer value.
Rationality and Social Interaction
The content of a message is what is said or communicated. Content has been
examined based on the influence attempts such as the direct or indirect and coercive or
non-coercive content (Frazier and Rody 1991; Mohr and Nevin 1990). Direct content
refers to specific behavior requests and does not attempt to check another participant’s
understanding or expectations. Indirect content is focused on information exchanged in
order to influence by developing a shared understanding. This is an influence attempt
aimed at changing beliefs or attitudes rather than focusing on immediate action. Indirect
communication is considered to be more relational (Frazier and Summers 1986; Mohr
27
and Nevin 1990). This dichotomy assumes that all communication is an influence
attempt (Duncan and Moriarty 1998), which leaves little room for joint problem solving
or communications concerned with group or relationship benefits.
Another way to examine message content is the instrumental vs. social content
(Sheng et al. 2005). Instrumental communication is communication related to business
objectives and tasks, and this communication is important to contractual relations but the
complexity involved often requires a social component as well (Macneil 1981). This
social component aids in the development of shared values and the instrumental aids in
the development of shared expectations. Both will be necessary for long term
relationships. According to Macneil;
“Market exchange in the utilitarian model is exchange in discrete
transactions in which relations between the parties are seemingly
assumed not to exist…….Not only has market exchange always been
heavily embedded in social relations, but discrete (relatively) exchange
patterns have always occupied only limited sectors of market
economies……most aspects of distribution have always been carried on
almost entirely in ongoing relationships.” (Macneil 1986p. 591)
One method of examining the instrumental content of communication is
rationality, which is providing the rationale for particular decisions without a direct
attempt to influence (Joshi 2009). We investigate message content based upon
rationality. Rationality is an indirect attempt to influence and it provides information that
can be used to justify a particular course of action.
Relational or social communication entails sharing information that may not be
directly related to tasks but also include a social component. Within relational exchange
28
“Commonly accepted social values serve as media of social transactions” (Blau 1954).
Besides instrumental or economic payoffs, in the effort to find common ground and
shared understanding, social payoffs are obtained as well. While a B2B relationship
requires instrumental communication to deal with goals and tasks, the social component
of communication also has a significant role in aligning perceptions, setting expectations,
and improving coordination (Anderson and Weitz 1989; Etgar 1979). In their
examination of social, structural and financial relationship investments (Palmatier et al
2006) found that social investments resulted in the highest customer specific return,
followed by structural relationship investments combined with a high degree of social
interaction. The findings show that social interaction is valuable to an organization.
These investments are special cases of interaction or information exchange such as meals,
events, or special status such as ‘preferred customer.’
Rationality is instrumental and aids customers and suppliers in making and
justifying their own decisions while social interactions aid in developing trust, as well as
positive feelings for a partner. Rationality and social interaction both contribute to
relational exchange. The former is necessary to acquire information for decision making
while the latter can increase understanding and confidence in that information.
Reciprocal Feedback
According to Dwyer, relationships cannot be maintained or formed without
bilateral communication (Dwyer et al. 1987). Previous conceptualizations have
investigated feedback as the direction or bidirectionality, which refers to the degree to
29
which communication is bidirectional as opposed to one-way (Mohr and Nevin 1990).
This is a measure of the amount of communication from seller to buyer, as well and from
buyer to seller and finally a comparison of the two. Two-way communications is a sign
of mutual agreement or support and greater shared meaning (Duncan and Moriarty 1998;
Mohr and Nevin 1990; Sunnafrank 1986). Rather than unilateral action, two-way action
or co-creation requires customer empowerment and one method of empowering
customers is to allow them to connect and collaborate with the firm (Ramani and Kumar
2008).
Bidirectional communication can still be found with one-way communication
patterns (Joshi 2009). For example, an email with promotional material sent from a
supplier to a buyer followed by a Request for Proposal (RFP) sent from a buyer to a
supplier. These messages are not necessarily connected unless the RFP is in response to
the promotional material or vice versa. Feedback is a response to a message and includes
references to previous messages or responses (Song and Zinkhan 2008). When
responding to email for example, all of the previous messages in the conversation are
often recorded and sent with the response. Instead of bidirectionality, Joshi (2009)
recommends reciprocal feedback as a measure for the interactivity of communication.
Reciprocal feedback measures frequency of responses to previous messages rather than
frequency of initiating communications. While this type of communication is likely in a
F2F meeting it may also be frequent in a digital setting where past messages are often
automatically recorded and can be responded to at any time.
30
The mode of communication has an impact on the interactive dimensions which
are the buyer and supplier contacts, rationality, social interactions, and reciprocal
feedback. Our model posits a typology of communication mode based upon the level of
interactivity supported by each distinct technology. Personal communication is always
synchronous and interactive, digital communication offers some control over the degree
of synchronicity and interactivity, impersonal communication is asynchronous and often
one way. Research has demonstrated that communication is positively associated with
social interaction (Sheng et al. 2005, Palmatier et al 2006), rationality, reciprocal
feedback (Joshi 2009), and contact density (Palmatier 2008). Communication will have a
positive impact on these dimensions of interactivity we will examine whether different
modes of communication have different impacts on these dimensions. As the
interactivity of the communication technology increases, the number of buyer and
supplier contacts, the frequency of social interaction, reciprocal feedback, and rationality
will increase as well. Therefore we offer the following hypotheses:
H1: The positive impact of personal communication on (a) social interaction, (b)
rationality, (c) reciprocal feedback, (d) buyer contacts and (e) supplier contacts
will be GREATER than the impact of digital communication.
H2: The positive impact of Digital communication on (a) social interaction, (b)
rationality, (c) reciprocal feedback, (d) buyer contacts and (e) supplier contacts
will be GREATER than the impact of impersonal selling.
31
H3: Impersonal communication will have a positive effect on (a) social
interaction, (b) rationality, (c) reciprocal feedback, (d) buyer contacts and (e)
supplier contacts.
32
CHAPTER 3: ADVOCACY IN B2B RELATIONSHIPS
Word of mouth (WOM) communication refers to interpersonal communication
regarding an evaluation of a product, service, or brand (Arndt 1967). Positive word of
mouth including referrals and recommendations are an important outcome of relationship
marketing efforts (Reichheld 2003) . WOM is positively associated with customer value
(Kumar 2007), sales, and repeat purchases (Zeithaml et al. 1996). This research will
focus on positive WOM or advocacy, which includes recommendations and referrals
(Harrison-Walker 2001; Zeithaml et al. 1996). WOM researchers have investigated its
use by decision makers (Arndt 1967; Moriarty and Spekman 1984) as well as it’s
likelihood of occurrence (Anderson 1998; Harrison-Walker 2001). This chapter explores
advocacy’s association with relationship marketing constructs and interactive
communication.
Buyers take part in advocacy in order to share information or persuade other
buyers. The content of their communications is a response to a purchase experience
(Zeithaml et al. 1996). These responses may be positive or negative. Negative word of
mouth has been found to negatively impact a firm’s idiosyncratic stock returns (Luo
2007), and has a negative impact on purchase intentions (Arndt 1967). Advocacy, on the
other hand, has been shown to have a greater impact on purchase intentions than
advertising (Keller 2007) and to positively impact sales (Chevalier and Mayzlin 2003).
33
Buyers become advocates when they are pleased with a service or product and
develop emotional bonds with them (Fullerton 2003). Advocacy, though, requires effort
on the part of the advocate. Before the widespread use of digital communication, WOM
required speaking F2F or on the phone with another person. The internet allows advocacy
to take place anonymously on discussion boards or in a one-to- many environment like
posting on a social media site or even sending out an email to multiple recipients. This
change in technology may decrease the effort required by the advocate and allows large
scale WOM (Dellarocas 2003).
According to Moriarty and Spekman (1984), WOM is an important source of
information to B2B customers and aids potential buyers in their decision making process.
Buyers rely on this information when making a purchase decision because they find
referrals and recommendations to be more reliable than information from commercial
sources (Arndt 1967; Herr et al. 1991; Moriarty and Spekman 1984; Sheth 1971). In
addition, word of mouth may decrease the resources and time necessary to make a
decision (Dholakia et al. 1993). One practitioner’s study on B2B word-of-mouth found
that for IT purchases, WOM recommendations reduced the number of alternatives
considered as well as the time required to make a purchase decision (Nicks 2006).
Besides aiding buyers in making decisions, advocacy is also valuable to sellers.
Kumar (2007) compared Customer Lifetime Value (CLV) and Customer Referral Value
(CRV) and found that some customers’ referral value is much higher than their CLV.
Those with high CLV could not be counted on to have high CRV. Those with the highest
34
CRV did not have high CLV. He referred to the High CRV customers as Advocates and
Champions, Advocates have the highest CRV and the second lowest CLV of the four
customer groups identified. According to Kumar, the best advocates are not the most
profitable customers based on CLV, but they may be when based upon the total of
referral and lifetime value. The study suggests that companies rethink their tactics in
order to focus on repurchases as well as referrals. Advocacy is a desirable outcome of
relationship marketing efforts; however, its association to other outcomes such as
satisfaction and affective and calculative commitment is not clear.
A better understanding of the drivers of advocacy will aid managers in where to
allocate efforts and resources. This understanding may also aid managers in determining
the returns of their relationship marketing efforts, which cannot be measured solely by
customer value, but may be better estimated by the total of customer value and referral
value.
Advocacy is considered to be an outcome of successful relationship marketing
efforts (Reichheld 2003) and it frequently occurs in the presence of other loyalty- type
behaviors, such as repeat purchasing (Zeithaml et al. 1996). In the relationship marketing
literature, satisfaction (Geyskens et al. 1999), trust and commitment (Morgan and Hunt
1994) are key constructs indicating relationship quality and longevity (Doney and
Cannon 1997; Geyskens et al. 1996; Wetzels et al. 1998). Previous research shows that
satisfaction and commitment are associated with advocacy (Anderson 1998; Dick and
Basu 1994) and customers become advocates only if they are satisfied.
35
Satisfaction is a measure of the overall evaluation of an exchange relationship
based upon past performance. This evaluation includes economic and non-economic
dimensions (Geyskens et al. 1999). It does not imply that a relationship will become
relational, but it is necessary to develop relational bonds (Sashi 2012) because customers
want to maintain relationships with those organizations that they perceive as delivering a
better value relative to other organizations (Morgan and Hunt 1994). Without
satisfaction, the relationship may dissolve, but once satisfaction exists, the possibility for
the development of commitment exists. Commitment is multidimensional and considered
to be reciprocal. It can lead to stable long term relationships by enhancing the confidence
of the participants. Calculative commitment is the decision to continue and maintain a
stable relationship for economic reasons. Affective commitment is the desire to continue
the relationship because of liking or enjoyment. When customers are satisfied with
previous transactions repurchase is more likely and affective or calculative commitment
may develop.
Albert Hirschman’s(1970) examined responses to dissatisfaction or decline. He
proposed that customers have two very different methods of dealing with dissatisfaction.
Hirschman and Arrow (1963) proposed that when the market fails to offer the most
desirable alternative; buyers will develop non market social institutions in order to
promote change (Hirschman 1970). Voice is the political or social institution designed to
influence a firm to change. The exit, voice, loyalty model proposes that in the presence of
dissatisfaction, buyers will exit the relationship and in the presence of loyalty and high
levels of dissatisfaction, buyers will complain rather than exit. Loyalty is a lack of
36
complaining in the presence of dissatisfaction (Ping and Jr 1993). Hirschman’s work
examines how loyalty impacts a desire to encourage change when there is dissatisfaction.
He examined voice as a response to dissatisfaction. We are examining advocacy as a
response to satisfaction. We look at how loyalty, calculative commitment, affective
commitment, and trust impact advocacy when there is satisfaction.
Hirschman like Alderson (1957) postulated that some customers have a
preference for a firm or product even in the presence of a decline in quality. This
preference can be due to personal taste or structural constraints such as a lack of
alternatives. The structural constraints are an economic evaluation of the costs and
benefits of maintaining the relationship and lead to loyalty and calculative commitment.
Personal taste though is affective rather than calculative. Meeting buyers’ expectations or
satisfying customers can lead to loyalty and calculative commitment. Both loyalty and
calculative commitment are based on structural constraints. Calculative commitment is
related to the perceived switching costs while loyalty refers to avoiding confrontation and
maintaining the relationship because of its importance. Loyal customers in this sense do
not rock the boat (Hirschman 1970; Ping 1993). Affective commitment, on the other
hand, is driven by liking, reciprocity, and trust (Morgan and Hunt 1994; Sashi 2012). It
is a sign of shared values and an emotional attachment. Calculative and affective
commitment are not completely independent but linked. An initial evaluation leads to the
choice to conduct a transaction because it is the best alternative economically. The
interactions that take place because of calculative commitment increase the opportunities
for developing long-term relationships. Calculative commitment is not directly
37
associated with advocacy or even profitable customers (Fullerton 2003), but the
interactions that result from loyalty and calculative commitment may lead to trust and
affective commitment. Calculative commitment may be important in a new relationship,
or when an industry is new or growing and there is a lack of specialists or alternatives
(Sashi 2005). As the market becomes more competitive, the switching costs decrease
(Jones and Sasser 1995), and the levels of loyalty and calculative commitment also
decrease. Satisfaction is necessary for loyalty and calculative commitment to develop
and they provide the opportunity to develop trust and affective commitment, which aid in
maintaining long term relationships even in the presence of low switching costs.
Advocacy is considered a personal communication channel, while traditional
advertising is non-personal. These personal communications can have a significant
impact on product or service evaluations (Herr et al. 1991), and they can aid decision
makers not only in determining what to purchase but also in reducing their decision
making time (Dholakia et al. 1993). The value of a customer is not only in the dyadic
exchange but value also exists in referrals. Satisfied and committed customers have a
positive evaluation of an exchange relationship and reward their suppliers through
advocacy.
A conceptual model of the relationship between satisfaction, loyalty, calculative
commitment, affective commitment, trust, and advocacy is presented in Figure 1.
Satisfied buyers will continue a relationship due to loyalty and calculative commitment
but if these buyers develop trust and affective commitment they will be more likely to
38
become advocates of the supplier. Previous studies demonstrate that commitment is a
result of communication efforts (Joshi 2009; Morgan and Hunt 1994; Palmatier et al.
2006a) as is trust (Anderson and Narus 1990; Sheng et al. 2005). According to Morgan
and Hunt (1994), trust and commitment are two key mediating variables in relationship
marketing and failing to include them in a model may result in a flawed understanding of
the impact of our antecedents. Therefore, we include trust as well as loyalty, calculative
commitment, and affective commitment in our model of interactive communication.
Beginning with communication and the dimensions of interactivity figure 3 is a
schematic showing their association with relational outcomes: satisfaction; loyalty;
calculative commitment; affective commitment; and trust and the association to the
behavioral outcome, advocacy.
Figure 3: B2B Interactive Communication
Earlier research has demonstrated that communication frequency can decrease
decision making time (Dholakia et al. 1993), improve satisfaction with communication
(Mohr and Sohi 1995) and increase collaboration (Mohr and Spekman 1994). Within an
organization communication improves team cohesiveness, profitability, and coordination
39
(Peters and Fletcher 2004). The dimensions of interactivity are part of the
communication process and communication is positively associated with satisfaction, so
based on our conceptualization, the dimensions of interactivity will lead to increased
satisfaction.
H4: (a) Social interaction, (b) rationality, (c) reciprocal feedback, (d) buyer
contacts and (e) supplier contacts will have a positive effect on satisfaction.
Reciprocal feedback has been investigated as a key variable in B2B
communication studies as well as CMC research (Joshi 2009). Reciprocal Feedback has
been referred to as a measure of bidirectional or interactive communication. These
interactions have the potential to increase customer satisfaction (Ramani and Kumar
2008).
H5: The positive impact of reciprocal feedback on satisfaction will be greater
than the impact of (a) social interaction, (b) rationality, (c) buyer contacts, and
(d) supplier contacts on satisfaction
Calculative Commitment is similar to Hirschman’s loyalty (1970). According to
Hirschman, buyers can exit a relationship which is the economic reaction, they may use
voice to complain which is the political reaction, or customers may remain loyal due to
structural constraints such as lack of alternatives or high switching costs. Hirschman
viewed these as alternative mechanisms which interacted with one another. When
customers are loyal they require a high level of dissatisfaction before exiting and as
40
dissatisfaction increases customers are more likely to use voice rather than exit.
Calculative commitment is an alternative to complaining or exiting and can explain why
customers remain in a relationship when satisfaction is low. Although satisfaction is still
necessary for the relationship to continue calculative commitment develops without
positive feelings. This type of commitment can have a negative impact on a buyers’
advocacy and customer retention (Jones and Sasser 1995). If a buyer feels trapped,
relationship marketing efforts often fail (Fournier 1998). In addition to calculative
commitment, positive feelings and trust are required in order for advocacy to develop.
Saying good things about a partner requires a social investment, and when a relationship
feels forced, due to a lack of alternatives or high switching costs, buyers are not
motivated to invest in the relationship (Fullerton 2003). Affective commitment provides
the motivation necessary for this social investment.
Affective commitment is the result of feelings of unity and congruent goals (Kim
and Frazier 1997; Stern 1986), and generally marketing channels research refers to
affective commitment as opposed to calculative (Geyskens et al. 1996). Since the
exchange process is embedded within social relations (Macneil 1981), repeated
exchanges may lead to an increasingly emotional attachment while the actors move
towards a focus on relationship or group benefits (Tallman 1991). Those companies that
continue to meet or exceed expectations are likely to be seen as delivering superior value
and buyers prefer to maintain long term relationships with companies they perceive as
delivering superior value relative to competitors (Morgan and Hunt 1994). Affective
commitment only arises after satisfaction. Under these conditions buyers are likely to
41
become advocates. Then when expectations are met and positive emotional bonds
develop advocacy will follow. Satisfaction is necessary to develop loyalty and calculative
commitment and it is also necessary for the positive feelings required for the
development of trust and affective commitment. Calculative commitment is necessary
for affective commitment to develop but on its own it will not lead to advocacy.
Satisfaction and affective commitment are the antecedents of advocacy.
H6: Satisfaction will have a positive effect on (a) loyalty, (b) calculative
commitment, (c) affective commitment, and (d) trust.
According to our model the level of satisfaction interacts with loyalty, calculative
commitment, affective commitment, and trust to determine the likelihood of advocacy.
Loyalty and calculative commitment do not lead to advocacy. Anderson (1998) shows
that customers with high satisfaction were more likely to take part in advocacy than those
with lower satisfaction and several studies indicate that advocacy is one of the
consequences of satisfaction (Bitner 1995; Mangold et al. 1999; Zeithaml et al. 1996).
Only with affective commitment or trust will advocacy occur:
H7: (a) Loyalty and (b) calculative commitment will have a negative effect on advocacy.
H8: (a) Affective commitment and (b) trust will have a positive effect on advocacy.
The study proposes that the mode of communication determines how interactive
communications will be with personal being the most interactive followed by digital and
impersonal being the least interactive. The more interactive the mode of communication
42
is the greater its association with the dimensions of interactive communication will be.
The dimensions of interactive communication have a positive impact on satisfaction and
increase the likelihood of trust and affective commitment which are positively associated
with advocacy. A schematic of the proposed model with the associated hypotheses is
presented in figure 4.
43
Figure 4: Interactive Communication in B2B Relationships
44
CHAPTER 4: METHODOLOGY
This chapter focuses on the methods for empirically investigating our
B2Bcommunication model. A description of the process of designing and testing the
survey instrument is provided. The steps in the data collection procedure and the
intended method of data analysis are discussed. The purpose of the design is to test the
hypothesized relationships as well as the overall model described in the previous chapter.
Survey Instrument
Items were developed based on existing scales where possible and modified as
appropriate. Scales were developed and tested for fourteen constructs. Previous research
utilized formal and informal to distinguish between modes of communication (Mohr et al.
1996; Mohr and Sohi 1995). Our method of distinguishing between modes of
communication based on interactivity is new and the scales for personal, digital, and
impersonal communication were modified to reflect this change. Previous scales did not
differentiate between modes of communication but summed the frequency of
communication across modes (Mohr et al. 1996). Our scales for buyer and supplier
contacts are also new and based upon previous scales measuring contact density, which
was a measure of the total number of contacts (Palmatier et al. 2006b). A summary of the
constructs is presented in Table 2.
In order to check face validity, PhD students and marketing research practitioners
were asked to supply feedback on the items. Each was asked to provide comments; on
45
the clarity of the items, the items ability to measure the construct, and any
additional comments. Each item was then reviewed for clarity and content validity by a
Professor of Marketing and this author. An extensive editing procedure was followed
until agreement was reached on each individual item. These revisions were incorporated
and subsequently transformed into a pilot study with a total of 68 items measuring 14
constructs.
Table 2: Constructs
Construct
Personal Communication
Digital Communication
Impersonal
Communication
Buyer Contacts
Supplier Contacts
Reciprocal Feedback
(Joshi 2009)
Social Interaction
(Palmatier et al 2006,
Sheng 2005)
Rationality
(Joshi 2009)
Satisfaction
(Homburg 2003)
Loyalty
(Homburg 2003)
Calculative Commitment
(Kumar 1995)
Affective Commitment
(Kumar 1995)
Trust
(Morgan and Hunt 1994)
The frequency of F2F communication.
The frequency of communication conducted via the
internet.
The frequency of non-digital communications including
memos, letters, and nondigital advertising.
The number of contacts at the buying firm who
communicate with the supplier.
The number of contacts at the supplier’s firm who
communicate with the buyer.
The frequency of responses to previous messages.
Interaction that is informal and not directly related to
business activities.
Information that can be used to justify a particular
course of action.
A measure of the overall evaluation of an exchange
relationship based upon past performance.
The intention to continue the relationship even in the
presence of problems.
The decision to continue and maintain a stable
relationship for economic reasons.
The desire to continue the relationship because of liking
or enjoyment
Confidence in and exchange partner’s reliability and
integrity.
46
Advocacy
(Harrison and Walker
2001, Zeithaml 1996)
Positive word of mouth.
The final check of face validity involved distributing the survey to 30 businesses
in Michigan. The goal was to ensure that the survey was readable and clear to a group of
respondents similar to the target population. No particular industry was selected for this
pilot and qualified respondents were managers or owners familiar with their firm’s
relationship to a supplier.
This researcher and a group of marketing research students who had been given
detailed instructions approached qualified business contacts and asked for their
participation. Attempts were made to snowball the collection procedure by asking
respondents for contact information and introductions to their contacts. This collection
method resulted in 48 surveys collected and additional feedback from businesses.
The instrument wording was modified based upon respondent feedback. The only
modifications involved changing “online communication” to “interactive
communication” in three items. The constructs and the final wording of the items are
listed in Table 4.
Table 3: Final Measurement Scale Items
Construct
Personal
Communication
Items
f1: We have frequent F2F interactions with this supplier.
f2: We frequently share information with this supplier in F2F
meetings.
f3: We often have F2F contact with this supplier.
f4: We often collaborate with this supplier in F2F meetings.
f5: We rarely have F2F meetings with this supplier. (R)
47 Digital
Communication
Impersonal
Communication
Buyer Contacts
Supplier
Contacts
Reciprocal
Feedback
(Joshi 2009)
d1: We have frequent online interactions with this supplier.
d2: We frequently share information with this supplier using
interactive communication.
d3: We often have online contact with this supplier.
d4: We often collaborate with this supplier using interactive
communication.
d5: We rarely have interactive communications with this
supplier.(R)
ic1: We frequently communicate with this supplier using
traditional nondigital media.
ic2: We frequently share information using traditional non-digital
media.
ic3: We are often in contact with this supplier using traditional
non-digital media.
ic4: We often collaborate with this supplier using traditional
nondigital media.
ic5: We rarely use traditional nondigital media to communicate
with this supplier. (R)
b1: This supplier is in contact with several individuals at our
company.
b2: This supplier communicates with members of several
departments at our company.
b3: This supplier primarily communicates with one contact at our
company. (R)
b4: This supplier communicates with members of several levels
of management at our company.
sc1: We are in contact with several individuals at this supplier.
sc2: We communicate with members of several departments at
this supplier.
sc3: We primarily communicate with one contact at this
supplier.(R)
sc4: We communicate with members of several levels of
management at this supplier.
rf1: This supplier solicits our views on an ongoing basis.
rf2: This supplier responds promptly to communications from us.
rf3: This supplier provides us with a lot of feedback on our
performance.
rf4: This supplier has frequent two-way communication with us.
rf5: This supplier has regular dialogues with us.
48 Social
Interaction
(Palmatier et al
2006, Sheng
2005)
Rationality
(Joshi 2009)
Satisfaction
(Homburg et al.
2003)
Loyalty
(Homburg et al.
2003)
Calculative
Commitment
(Kumar et al.
1995)
si1: We have close personal relationships with members of this
supplier.
si2: This supplier interacts with members of our company
socially.
si3: This supplier treats some members of our company like
friends.
si4: Some of our communication with this supplier is personal.
si5: All of our interactions with this supplier are solely business
related. (R)
si6: We have informal conversations with members of this
supplier.
rat1: This supplier provides us with information that helps guide
our decisions.
rat2: This supplier provides us with reasons for choosing a
particular action.
rat3: This supplier shares the results of their experience with us.
rat4: This supplier provides us with information we can use when
deciding between alternative courses of action.
s1: We are very satisfied with this supplier.
s2: We would choose this supplier if we had to do it over again.
s3: We are pleased with the relationship we have with this
supplier.
s4: We are unhappy with this supplier. ®
s5: We have not had a good experience with this supplier.(R)
l1: We sometimes ignore problems with this supplier because we
want to stay with them.
l2: We will continue our relationship with this supplier despite
any problems that occur.
l3: We will remain loyal to this supplier even when we have
problems.
l4: We tend to disregard problems with this supplier because they
always fix themselves.
cal1: We will stay with this supplier because changing suppliers
is too difficult.
cal2: We will stay with this supplier because of our investments
in the relationship with them.
cal3: We will continue our relationship with this supplier because
of a lack of good alternatives.
cal4: We will continue our relationship with this supplier because
we have a contract.
cal5: We will continue our relationship with this supplier because
switching suppliers is costly.
49 Affective
Commitment
(Kumar et al.
1995)
Trust
(Morgan and
Hunt 1994)
Advocacy
(HarrisonWalker 2001;
Zeithaml et al.
1996)
af1: We find it pleasant to work with this supplier and want to
stay with them.
af2: We genuinely enjoy our relationship with this supplier and
want to remain with them.
af3: We like the values of this supplier and plan to continue our
relationship with them.
af4: We like working with this supplier and want to remain with
them.
af5: We are delighted with this supplier and expect to stay with
them.
t1: This supplier cannot always be trusted. (R)
t2: This supplier is very honest.
t3: This supplier can be counted on to do what is right.
t4: This supplier is reliable.
t5: This supplier has high integrity.
ad1: If asked we will say positive things about this supplier.
ad2: We will recommend this supplier to others.
ad3: We will encourage others to do business with this supplier.
ad4: If asked we will tell others about our good experience with
this supplier.
ad5: We will discourage others from doing business with this
supplier. (R)
ad6: If asked we will speak favorably about this supplier to
others.
Pilot Study
Another survey was distributed using a similar method in Florida. The purpose of
this study was to investigate scale reliability as well as unidimensionality. This collection
method resulted in a total of 100 businesses contacted and 61 completed surveys.
Exploratory factor analysis (EFA) was conducted using principal components analysis.
This is most appropriate for extracting the minimum number of items needed to describe
the factor (Hair et al. 2010). EFA was done on all five scales at one time in order to
check for possible cross loadings between the new scales. Five new scales were
developed for this study: buyer contacts, supplier contacts, personal communication,
digital communication, and impersonal communication. Factor analysis of these scales
50 indicates a four factor solution is more appropriate. The five factor solution has one
variable loading on the final factor and the items for buyer (b1-b4) and supplier contacts
(sc1-sc4) load on a single factor as shown in the rotated component matrix presented in
table 4. The buyer and supplier contacts are a proxy for the sources and receivers of
communications and separating source from receiver becomes problematic with two-way
communication. The roles tend to alternate as listening is merged with speaking.
Table 4: EFA Of New Scales
Item
b2
sc1
sc4
b1
sc2
b3
b4
sc3
f3
f1
f4
f2
f5
d1
d3
d4
d5
d2
ic3
ic1
ic4
ic2
ic5
1
.864
.739
.720
.706
.666
.602
.598
.440
2
3
5
.885
.855
.849
.727
.698
.896
.868
.788
.643
.590
.831
.817
.774
.720
.804
51 4
The buyer and supplier communication variables were recoded into a single
variable by summing each buyer communication item with the corresponding item in the
supplier communication scale. For example, (Buyer Item 1 + Supplier Item 1) =
Recoded Item or b1+s1= bs1. Palmatier’s (2008) measure for contact density asked
respondents to estimate the number of relational ties between their organization and a
supplier organization. The total number of buyer and supplier contacts provides an
alternative method of measuring the number of relational ties or contact density.
Following the recode, factor analysis was conducted searching for a four factor
solution. The summated items performed well with good psychometric properties in the
second EFA presented in Table 5.
All items were examined for reliability and several items were identified for
deletion. A deletion of an item is justified when the item is conceptually related to
another item (Hair et al. 2010) or when the item-total correlation is low (Churchill 1979).
Due to cross loadings and low item-total correlations in the pre-test survey several
modifications were made. Generally item-total correlations of less than .4 should be
deleted and this is the pattern we followed with the exception of one case (Hair et al.
2010). Item five of impersonal communication did not have a loading greater than .4 on
any factor. Neither did it present any cross loading issues. Each item in a scale
measuring a single construct should have a similar amount of the common core of the
construct being measured. If this occurs then the items should be highly intercorrelated.
The corrected item-total correlation was .09, .49, and .55 for item five of impersonal
communication, digital communication, and personal communication respectively and
52 item five exhibited the lowest item-total correlations for each of the scales. By retaining
item 5 of the Impersonal Communication we are able to maintain symmetry in the scales.
Table 5: EFA With Modified Scales
Item
f3
f1
f4
f2
f5
d3
d1
d4
d5
d2
bs4
bs2
bs1
bs3
ic5
ic1
ic3
ic4
ic2
Personal
.896
.856
.845
.707
.670
Digital
Contacts
Impersonal
.859
.844
.746
.726
.587
.727
.725
.720
.653
.390
.843
.839
.765
.718
The scales for calculative commitment and loyalty were adapted from previous
studies (Homburg et al. 2003; Kumar et al. 1995). Both are related to the economic cost
of switching. As the two scales have not been used together in previous studies, we
conducted EFA with these to check for cross loadings and ensure unidimensionality.
The two factor solution accounted for 52 % of the variance but item 3 for
calculative commitment loaded onto the loyalty scale as shown in table 8. The item-total
correlation for item 3 of calculative commitment was .324. Based on these findings item
53 3 was removed. Table 7 is a summary of the final scale reliabilities and the items
removed due to low item-total correlations.
Table 6: Loyalty And Calculative Commitment EFA Item
l1
l4
l3
cal3
l2
cal5
cal1
cal2
cal4
Loyalty
.796
.724
.690
.646
.587
Calculative
.783
.766
.675
.506
Based on the results of the exploratory factor analysis our scales were purified.
Cronbach’s alpha greater than .7 indicates an acceptable degree of consistency and
repeatability of a scale (Nunnally and Bernstein 1994). Cronbach’s alpha for the final
scales range from .707 to .950.
Table 7: Scale Reliability And Modifications
Scale
Personal Communication
(F2F)
Digital Communication (D)
Impersonal Communication
(IC)
Buyer-Suppler Contacts (BS)
Social Interaction(SI)
Reciprocal Feedback (RF)
Rationality (RAT)
Satisfaction (S)
Loyalty (L)
Affective Commitment (AF)
Calculative Commitment
(Cal)
Number
of Items
retained
5
5
5
.869
.748
4
4
.845
.797
4
4
4
3
5
3
.806
.834
.803
.733
.950
.707
54 Cronbach’s Item-total
Alpha
correlation of the
REMOVED Items
.908
Item 5 .377
Item 6 .240
Item 2 .351
Item 5 .396
Item 4 .387
Item 3 .324
Item 4 .289
Trust (T)
Advocacy(Ad)
4
5
.884
.877
Item 1 .377
Structural Equation Modeling
We use Structural Equation Modeling (SEM) in order to test our model and our
hypotheses. SEM allows a researcher to test multiple interdependent relationships
between latent variables while accounting for measurement error in the estimation
process (Hair et al. 2010), and SEM allows us to work with both measured and latent
variables. Measured variables are those that are observed or directly measured. These are
also referred to as observed or manifest variables. Latent variables are those implied by
the covariance between two or more manifest variables. Latent variables are also referred
to as factor, unobserved variables, or constructs. Using a two-step SEM approach allows
the researcher to first test a measurement model linking the observed variables to the
latent factors which they reflect. Confirmatory Factor Analysis is used to test the
reliability and validity of the variables. Once a satisfactory measurement model has been
identified, the relationships between the latent variables can be tested using a structural
model (Anderson & Gerbing, 1998; Novak, Hoffman, & Yung, 2000).
Data Collection
As a research setting I use a national sample of businesses in the commercial
printing and graphic design industry. Limiting the study to this “somewhat homogeneous
population minimizes extraneous sources of variation” (Morgan and Hunt 1994 p.27).
Commercial art and graphic design is found on posters, business cards, letterhead, and
video. The business sells artwork for business purposes. The artwork is produced in a
variety of ways using specialized software, printers, scanners, and plotters. In the 1990’s,
many advertising agencies created their own graphics departments reducing the number
55 of freelance artists in the industry. Despite this vertical integration the low startup costs
(a desktop computer and a production system- together less than $10,000), have
contributed to a fragmented industry with over 35,000 firms in the U.S. The industry is
characterized by rapidly changing technology requiring members to constantly monitor
the environment for changes in preference as well as design and production technology.
According to the industry research firm IBIS World, advertising agencies account for
approximately 40% of the $90 billion industry’s revenue followed by packaging (35%)
and publishing (25%).
Sample
Data was collected from middle and upper level managers in the commercial
printing and graphic design industry who are familiar with the company’s buyer supplier
relationships. We use Campbell’s (1955) criteria of drawing upon a sample population of
key informants who are knowledgeable about the phenomena of interest and are willing
and able to respond to an online survey. An online survey was the most appropriate
method of data collection because our study is an investigation of online communication.
Online surveys have gained credibility as a valid and reliable method of collecting data
from business managers (Dillman 2011). Our method of collecting contact information
allowed us to screen a priori for job title and industry in order to minimize sampling
frame error.
The sample size for the final survey was determined from sample sizes used in
similar studies. Structural equation modeling assumes a large sample size in its
estimation procedure (Hair et al. 2010). Researchers agree that a sample size greater than
56 200 can be considered a large sample size (Hair et al. 2010; Kline 2010). Rules of
thumb such as “greater than 150” perform as well and often better than any ratio rules
utilized for SEM (Jackson 2003). The data are more likely to represent the true scores
with larger sample sizes so we set our sample size goal at 300.
A web scraper was used to gather emails from the selected industry. This
collection procedure resulted in one email address from each business website. The web
scraper program resulted in 24,900 contacts, of which12, 242 names were identified as
decision makers in their respective companies. These individuals had one of the
following words in their job title; buyer, purchasing, president, chief, or owner. 7,235 of
these emails bounced. The two primary reasons for bounced emails are email address
issues and server issues. Email address issues may be caused by an invalid email address
due to an error such as a typo or the address may no longer exist. Server issues can be due
to the receiving server being down, busy or possibly rejecting the message because of a
firewall. In addition to the bounced emails, 19 of the contacts were found to have
terminated their employment with the identified company. Our final list contained 4988
emails. The initial distribution resulted in 272 completed surveys. The second
distribution and reminder resulted in an additional 56 completed surveys for a total of
328. Besides the completed surveys, 321 respondents indicated they did not wish or were
not qualified to participate.
We use Malhotra’s (2010 p. 384) method to calculate our overall response rate.
While uncommon in the literature, this method does not allow eliminating a respondent
from the sampling frame for refusal to participate. We estimate our response rate by first
57 estimating the number of eligible respondents in the sample. Using the total number of
contacts (messages + completed surveys), this formula estimates the total number of
eligible respondents in the sample. We made contact with 649 (328+321) potential
respondents either through an email response or through an attempted survey giving us
4339 (4988-649) potential respondents whose eligibility has not been ascertained. Out of
649 contacts, 328 were eligible respondents and 321 were ineligible. This suggests that
50.5% (328/649) of our sample may have been eligible to participate in the survey.
Number of Contacts
649
Eligible Respondents
328
Ineligible Respondents
321
Not Ascertained
4339
An estimate for the number of eligible respondents in the sample can be calculated as
follows:
4339 * (328 / 649) = 2193
Therefore the total number of eligible units in the sample is 2193+328= 3257
And our response rate is 328/2521= 13%
Our study attempts to provide an initial test of a theoretical model. Since “we are
not attempting to generalize an established model to a new population or project a
descriptive statistic from a sample to some larger population, the possibility of
nonresponse bias is a nonissue in research such as ours” (Morgan and Hunt 1994 p 28).
Nonetheless, in keeping with common practice we’ve estimated our response rate which
58 is acceptable when compared to similar studies which have been in the range of 9 to 13%
(Morgan and Hunt 1994; Palmatier et al. 2013).
The degree of interest in the research may have prompted the email messages
regarding ineligibility, but our request for participation only asked for a response from
individuals who were qualified. There is no evidence that response bias occurred or that
the request was unsuccessful at screening for eligible respondents (Rodriguez-Pinto et al.
2008; Wilson 1999). Our web survey process allowed us to send an invitation that
screened for qualified respondents and simultaneously allowed them to opt in to
participate. Of the 328 who did opt in, 309 completed the survey in its entirety.
Prior to our pilot study our measures were assessed for face validity by
researchers, practitioners and respondents. This resulted in some modifications in
question wording. The pilot study provided initial evidence of construct reliability as
each of our measures demonstrated a Cronbach’s alpha greater than .7 as suggested by
Hair (2010). The final questionnaire was distributed to the target population. There were
three main parts to the questionnaire. When a respondent followed the link from their
email to the questionnaire the first page they see is a letter describing the research and
that participation was completely voluntary. This letter set forth all requirements for
research approved by IRB and is included in the appendix. The second part was the
instructions and measures for the constructs. The third part included questions
concerning company demographics.
59 All of the construct items were measured using multi-item five point Likert
scales. The scales were labeled as “Strongly Agree, Agree, Neither Agree nor Disagree,
Disagree, Strongly Disagree” The item questions were randomized and then purposely
reviewed to place the measures for the dependent constructs in the first half of the survey.
60 CHAPTER 5: RESULTS
First, we examine the data for non-response bias then we follow Anderson and
Gerbing’s (1998) two step procedure for SEM. The measurement model is then
investigated empirically for reliability, convergent and discriminant validity. Once the
measurement model is finalized the structural model is evaluated for fit. After ensuring
adequate fit we examine the coefficients of the model in order to test our hypotheses.
Assessing non response bias
Early and late respondents were compared to assess non-response bias
(Armstrong and Overton 1977). There were 253 complete responses from the first
request for participation and 56 responses followed the second request. Forty percent of
respondents worked at companies with 5-50 employees and over 50% of respondents
reported 2012 revenues greater than 5 million. There were no statistically significant
differences in the number of employees or revenues of early and late respondents. We
also examined whether there were any differences between our key variables of interest.
This was done by summing the scaled questions for the key constructs. The summed
scales were compared for differences between early and late respondents and none were
found to be statistically significant at the .05 level.
61
Table 8: Tests For Non-Response Bias
Variable
Number of
Employees
Revenue
Advocacy
Personal
Communication
Digital
Communication
Impersonal
Communication
Early Respondents
(n=253)
2.98
Late Respondents
(n=56)
2.98
2-tail significance
3.85
20.99
17.38
3.77
21.02
18.13
.709
.957
.326
17.34
17.89
.330
16.41
16.75
.622
.978
Measurement Model
Confirmatory Factor Analysis with the Maximum Likelihood (ML) method of
estimation was used to evaluate the measurement model. ML iteratively improves
parameter estimates to minimize a specified fit function (Hair et al. 2010; Lomax and
Schumacker 2012). ML is a common estimation procedure providing valid results and
appears to be robust to violations of multivariate normality (Hu et al. 1992).
We examine the standardized loadings to determine the reliability of the scale
items. We then proceed by providing additional evidence of unidimensionality with the
Chi square statistic as well as several fit indices. Next, we demonstrate convergent
validity by examining the t-values and the Average Variance Extracted. AVE also
provides evidence of reliability (Bagozzi and Yi, 1988), and this is followed with an
examination of construct reliability including composite reliability (Fornell and
Larckener, Hair). In order to show discriminant validity, we follow the method
recommended by Bagozzi et al (1982) and Anderson and Gerbing (1988). With the
62
measurement model displaying adequate fit, reliability, convergent validity and
discriminant validity we continue with the structural model and the testing of hypotheses.
Factor loadings greater than .7 (Hair et al. 2010) would be ideal to provide
evidence of item reliability and others accept .5 as a cutoff point (Chin 1998). The
squared loading is the amount of variance in the item that is explained by the latent
variable, so a loading of .5 would mean that the latent construct only explains 25 percent
(.52) of the variance in the item. The rest of the variance would be due to error thus
making the item not only unreliable but also interfering with construct validity as well as
an interpretation of the structural model. Three items displayed standardized factor
loadings below .5. We deleted these items (Hair et al. 2010). The standardized factor
loadings show the contribution of each observed variable to the latent variable of interest.
The measures removed included one item from each of the following; digital
communication, loyalty, and contact density. The loadings for these items were .39, .31,
and .43 respectively. Deleting these items led to the loyalty construct having only two
indicators. Three is the minimum number of indicators per latent variable sufficient for
SEM with five to seven being preferred (Hair et al. 2010). The loyalty construct needed
to be removed from the model.
The measurement model was run again with the loyalty construct and those items
below the .5 threshold removed. In our trimmed model, all of the observed variables have
loadings ranging from a minimum of .5 to a maximum of .94 as shown in Table 15.
These factor loadings provide evidence of item reliability. Additional evidence has been
provided by our preliminary exploratory and reliability analysis conducted in the pilot
63 studies. The elimination of items with cross loadings in the pilot study helped to ensure
unidimensionality, which is an assumption of SEM and reliability analysis (Hair et al.
2010; Kline 2010).
The fit of a model is tested by using the Chi square statistic. The Chi square
provides a measure of both internal and external consistency which is required for
unidimensionality. Small p-values indicate that the hypothesized model is not confirmed
by the data (Kline 2010). The significance level of Chi square is sensitive to sample sizes
and departures from multivariate normality so most researchers consider alternative
measures of model fit (Hair et al. 2010). One of the primary measures is the Chi square /
degrees of freedom ratio. Kline (2010) suggests values of less than two as evidence of a
good fit and in some cases as high as five for an adequate fit. Our Chi square / degrees of
freedom ratio is 1.9 providing evidence of good fit (Bollen 1989).
When relying on alternative fit indices, multiple fit indices must be evaluated as
many are sensitive to sample sizes and degrees of freedom (Kline 2010). The chi
square/degrees of freedom ratio as well as Non normed fit index (NNFI) are independent
of sample size and the Comparative Fit index (CFI) is only affected by sample size to a
small degree (Ding et al. 1995). NNFI and CFI values greater than .9 are recommended
as providing acceptable fit for these two indices. A value of .9 would indicate a 90%
improvement over the null model. Our Non normed fit index was .97 while our CFI was
.98, indicating a good fit.
64 Our model demonstrates a good fit. Hu and Bentler (1999) recommend a
combination rule for fit indices and provide evidence that a cutoff rule utilizing both Root
Mean Square Error of Approximation (RMSEA) and Standardized Root Mean Square
Residual (SRMR) resulted in the lowest sum of Type 1 and Type 2 error rates. The
cutoff rule they recommend states that RMSEA ≤ .06 and SRMR <.09. Our RMSEA =
.068 and our SRMR = .067. The SRMR meets the criteria for a good fit as well as the
minimizing error rates. The RMSEA is slightly higher than what is recommended by Hu
and Bentler for minimizing error rates, but still acceptable. According to Brown and
Cudeck (1993) RMSEA values between .05 and .08 suggest reasonable error of
approximation, and RMSEA ≥ .10 suggests poor fit. Overall the fit indices provide
evidence of a reasonable to good fit.
Convergent Validity
Convergent validity is the extent to which similar measures are correlated. The
larger factor loadings are when compared to their standard errors, the greater is the
evidence for convergent validity. This is evidence that the measured values represent the
underlying factor (Bollen 1989). This ratio of the factor loading to the error can be
expressed as a t-value. T-values greater than the absolute value of 2.56 are significant at
the .01 level. The t-values in our measurement model range from 8.75 to a high of 33.95.
Our items are representative of the underlying constructs. Additional evidence of
convergent validity is provided by an AVE greater than .5 (Fornell and Larckner 1981,
Hair 2010). The AVE for our latent variables are all greater than .5 except for reciprocal
feedback as can be seen in Table 13.
65 Construct Reliability
Construct reliability (Fornell and Larcker 1981) refers to the degree that
indicators share in their measurement of a construct. Reliable constructs have indicators
that are highly correlated. A composite reliability (CR) value of at least 0.7 is required
for a construct to be reliable (Hair 2010). Our CR scores range from a low of .84 to a
high of .96 indicating good construct reliability.
The t values, standardized factor loadings, AVE, and CR are displayed in Table 9.
The table shows that all of our t-values are greater than 2.56 which is the cutoff for a .01
level of significance. The standardized loadings are all .5 or greater indicating that the
constructs being measured explain at least 25% of the variance in each item. The
construct with the lowest AVE is reciprocal feedback which had an AVE of .44 but this
latent construct displayed adequate convergent validity with t-values between 8.95 and
11.43. The composite reliability of the latent variables ranged from .84 to .96.
66 Table 9: CFA Results
Construct
Item
T value
Personal Communication
P1
P2
P3
P4
P5
D1
D2
D3
D4
Ic1
Ic2
Ic3
Ic4
Ic5
Si1
Si2
Si3
Si4
Rat1
Rat2
Rat3
Rat4
Bs1
Bs2
Bs4
Rf1
Rf3
Rf4
Rf5
S1
S2
S3
S4
T2
T3
T4
T5
*
26.8
33.95
21.61
18.19
*
9.82
14.49
11.96
*
17.73
17.11
16.3
8.9
*
10.16
10.78
11.09
*
13.71
8.75
15.73
*
15.15
15.33
*
8.95
10.59
11.43
*
18.05
20.83
14.26
*
19.57
19.83
17.14
Digital Communication
Impersonal Communication
Social Interaction
Rationality
Contact Density
Reciprocal Feedback
Satisfaction
Trust
67 Standardized
Loading
0.94
0.89
0.95
0.82
0.75
0.85
0.56
0.81
0.67
0.77
0.92
0.89
0.86
0.51
0.7
0.67
0.73
0.75
0.81
0.74
0.5
0.82
0.83
0.84
0.82
0.66
0.56
0.68
0.74
0.87
0.79
0.86
0.68
0.87
0.84
0.85
0.78
AVE CR
.76
.96
.54
.88
.65
.94
.51
.88
.53
.85
.69
.92
.44
.84
.65
.93
.7
.94
Affective Commitment
Af1
18.01
0.86
Af2
19.56
0.9
Af3
16.76
0.82
Af4
18.17
0.86
Af5
*
0.8
Calculative Commitment
Cal1 *
0.69
Cal2 12.94
0.92
Cal5 13.13
0.82
Advocacy
Ad1
*
0.86
Ad2
18.23
0.83
Ad3
16.46
0.78
Ad4
12.68
0.65
Ad5
15.6
0.75
* indicates factor loading set to one for scaling estimation
.72
.96
.66
.91
.60
.93
Discriminant Validity
Discriminant Validity provides evidence that the measures of different constructs
are distinct from one another. Following the recommendations of Anderson and Gerbing
(1988), 112 CFA models were estimated to establish discriminant validity. For each pair
of latent variables a model was estimated with the latent variable correlation constrained
to 1 and a separate model with the correlation free to vary. A chi square difference test
was performed for each pair of models.
An example of the procedure is described below. The measurement models for
the latent variables digital and impersonal communication and their associated manifest
variables are run first with the correlation between Digital and Impersonal constrained to
unity. In this model the Chi square value was 168.52 with degrees of freedom 27.
68 Figure 5: Construct Constrained To Unity
The same model is run with the constraint removed and the correlation between
the constructs digital and impersonal is allowed to vary. The Chi square for this model
was 105.17 with degrees of freedom 26.
69 Figure 6: Construct Free To Vary
In order to perform the Chi Square difference test the chi square value for the
unconstrained model is subtracted from the statistic for the constrained model. We also
subtract the degrees of freedom for the unconstrained from the degrees of freedom for the
constrained. Our Chi square difference value is 168.52 – 105.17 = 63.35; this value can
be found in Table 12 in the Digital column and Impersonal row. Chi square with 1
degree of freedom is significant at the .05 level with a value greater than 3.841 and at the
.01 level with a value greater than 6.635. Following this process, a significant chi square
difference provides evidence of discriminant validity (Anderson and Gerbing 1988). All
70 of our Chi square differences are significant at the .01 level and in each case the model
with the better fit (lower Chi square) was the unconstrained model.
Table 10: Chi Square Difference Between The Constrained And Unconstrained
22.07
41.77
59.45
65.18
79.14
70.79
74.18
0
19.74
52.13
7.41
50.91
58.28
63.68
0
42.01
83.29
84.01
70.08
60.82
0
31
0
71.86 82.85
40.32 43.21
41.77 43.71
Trust
46.38
43.41
48.55
49.48
79.76
57.4
64.8
Calculative
29.22
40.35
64.89
67.21
89.47
69.26
77.03
30.63
252.1
63.26
65.09
73.44
69.94
67.87
Affective
14.08
15.69
47.35
50.37
80.01
59.04
66.16
Satisfaction
0
Reciprocal
0
60.69
Contacts
0
64.54
51.67
Social
Interaction
Rationality
Personal
Digital
Impersonal
Rationality
Social
interaction
Contacts
Reciprocal
Satisfaction
Affective
Calculative
Trust
Advocacy
Digital
0
43.63
0
21.28 63.35
43.73 62.43
30.34 61.23
Personal
Impersonal
Pairwise Models
0
83.54
86.56
0
52.18
The measurement model demonstrates adequate item reliability, a good fit
utilizing several indices and combinations of indices, convergent validity, composite
reliability, and discriminant validity. The testing of the measurement model was the first
step in the two step procedure recommended by Anderson and Gerbing (1988).
71 The Structural Model
The second portion of the two step procedure is to estimate the structural model.
Based on the results of the first step, the hypotheses have been restated to reflect the
changes in measuring the constructs as well as the absence of the loyalty construct. Two
modifications were made to the hypotheses. First, all hypotheses with the supplier
contacts and buyer contacts were changed to reflect their combination into the new
construct “contact density.” The second change was the elimination of the loyalty
construct from the hypotheses. A schematic representation of the modified model is
presented in Figure 7.
Figure 7: Theoretical Model After CFA
Twelve latent constructs and 50 observed variables were used to test the model.
Multiple fit indices are used to evaluate the model (Hair 2010, Kline 2005, Schumacker
72 and Lomax 2010). The χ2 is 2269.56, p< .001, which is significant but sensitive to
sample sizes and therefore not a reliable test of model fit in models with larger than 100
observations (Hair 2010). Kline recommends the χ2/df, root mean square error of
approximation (RMSEA), and at least one incremental such as the non normed fit index
(NNFI) or comparative index such as CFI. Table 11 is a summary of the fit. Other than
the significant χ2 which is common for models with large sample sizes, all of the indices
indicate an acceptable structural model fit.
Table 11: Structural Model Fit Statistics
Fit
Value
Acceptable Value
χ2
2269.56 (P<.001),
p>.05
df=1150
χ2/df
1.97
<3
RMSEA
.058
<.08
CFI
.97
>.9
NNFI
.97
>.9
Hypotheses Testing
In order to test hypotheses in a structural equation model the path coefficients are
examined with the significance of the path coefficient providing support for the
hypothesized relationships (Bentler 1990; Kline 2010). Twenty two paths were examined
and the results are presented here.
73 Personal, Digital, and Impersonal Communication
Personal, digital, and impersonal communications were expected to have a
positive impact on each of the dimensions of interactivity. This positive relationship is
confirmed in each case except for the relationship between impersonal communication
and contact density where the relationship was not significant. Personal communication
is hypothesized to have the largest positive impact on the dimensions of interactivity
followed by digital communication and finally impersonal communication with the
smallest impact. The first three sets of hypotheses require pairwise comparison of the
standardized path coefficients. Table 12 facilitates the comparison.
Table 12: Pairwise Comparison Of Paths
Personal
Digital
Impersonal
Social Interaction
.39**
.15*
.14*
Rationality
.31**
.31**
.19**
Reciprocal Feedback
.55**
.42**
.21**
Contact Density
.31**
.23**
.11
*p<.05, **p<.01
Figure 8 is a representation of the portion of the structural equation model
detailing the impact of personal communications on the dimensions of interactivity.
74 Figure 8: Personal Communication Paths
**p<.01
The path coefficients between personal communication and the dimensions of
interactivity are each significant; social interaction (.39), rationality (.31), reciprocal
feedback (.55), and contact density (.31). Personal, F2F communication is positively
associated with each of these dimensions of interactivity and the paths from personal
communication to social interaction, reciprocal feedback, and contact density were each
greater than the paths from digital communication to these dimensions. The path from
personal to rationality though was not greater than the path from digital to rationality, so
H1b was not supported.
75 Figure 9 is a representation of the portion of the structural equation model
detailing the impact of digital communications on the dimensions of interactivity.
Figure 9: Digital Communication Paths
Digital communication is positively associated with social interaction (.15),
rationality (.31), reciprocal feedback (.42), and contact density (.23). The second portion
of H2 involved comparing the coefficients from digital communication to the dimensions
of interactivity with the coefficients from impersonal communication to the dimensions
of interactivity. Each of the coefficients from digital to the dimensions of interactivity
was greater than the coefficients from impersonal to the dimensions of interactivity. H2 is
supported.
76 Figure 10 is a representation of the portion of the structural equation model
detailing the impact of personal communications on the dimensions of interactivity.
Figure 10: Impersonal Communications Paths
The path coefficients from impersonal communication to social interaction (.14),
rationality (.19), and reciprocal feedback (.21) were all significant and positive. The path
from impersonal to contact density (.11) was not significant. At the lowest level of
interactivity, communication is shown to have a positive impact on social interaction,
rationality, and reciprocal feedback. Impersonal communication did not have a
significant association with contact density, so H3d was not supported.
Figure 11 is a representation of the remainder of the model detailing the impact of
the dimensions of interactivity on satisfaction and satisfaction’s impact on relational
outcomes; calculative commitment, affective commitment, trust, and advocacy.
77 Figure 11: Dimensions Of Interactivity Paths
**p<.01
The path coefficient between social interaction and satisfaction (.02) was not
significant and H4a was not supported. The path coefficient between rationality and
satisfaction (.76) was significant and H4b was supported. Rationality had the greatest
positive impact on satisfaction.
The path coefficient between reciprocal feedback and satisfaction (.11) was not
significant and H4c was not supported. We failed to find support for a significant
relationship between reciprocal feedback and satisfaction. (H5)
The path coefficient between contact density and satisfaction was significant and
negative, so H4d is not supported. We failed to confirm our hypothesis and our findings
suggest an inverse relationship between contact density and satisfaction.
78
The path from satisfaction to calculative commitment (-.12) is not significant and
we failed to confirm H6a. The sign of the path was negative but the coefficient was not
significantly different from 0. The path from satisfaction to affective commitment (.99)
is significant and the largest coefficient in the model. The path from satisfaction to Trust
(.92) is significant. H6b and H6c are supported. The path from Calculative commitment
to advocacy (-.06) is not significant. H7 is not supported. The path from affective
commitment to advocacy (.81) is significant giving support for H8a. The path between
trust and advocacy (.09) is not significant so H8b is not supported. Only affective
commitment and not trust was related to advocacy.
Figure 12 summarizes the direct and indirect effects of personal and digital
communication on satisfaction. Only the paths through rationality and contact density are
shown as the other paths to satisfaction were not significantly different from 0.
Figure 12: Personal And Digital Total Effect Paths
79 Rationality in our study referred to instrumental messages that offered support for
certain courses of action. In interviews conducted during the course of collecting data
several respondents suggested that if you want something done and it’s important you
need to meet F2F. This is what is suggested by media richness theory, but the internet,
with its ability to mimic live meetings, should begin to approach the same capabilities as
F2F. Rationality is the only dimension of interactivity having a significant and positive
association with satisfaction. The other significant path was contact density which had a
negative coefficient. The strength of a compound path is the product of the coefficients
along the path. The total indirect effects of personal and digital communication are the
sum of the compound paths beginning with each variable. Table 16 shows the four paths
from personal and digital to satisfaction and the total indirect effects. Comparing the
total indirect effects of digital and personal on satisfaction we see that digital has a
greater positive impact on satisfaction when contact density is accounted for.
Table 13: Total And Indirect Effects
Paths
Indirect effects
Personal→Contact Density→Satisfaction
(.31)(-.15)= -.047
Personal→Rationality →Satisfaction
(.31)(.76)= .24
Digital→Contact Density→Satisfaction
(.23)(-.15)= -.035
Digital→Rationality→Satisfaction
(.31)(.76)= .24
80 Total effects on
Satisfaction
.193
.205
Table 14 provides a summary of the results. Personal communication has the
greatest impact on the dimensions of interactivity with all of its associated path
coefficients except one being greater than digital communication. Digital communication
displayed a greater association with the dimensions of interactivity than impersonal and
less of an association than personal with one exception. Personal and Digital had the
same association with rationality. Only rationality was positively associated with
satisfaction and only affective commitment was positively associated with advocacy.
Table 14: Summary Of Results
Hypotheses
Path
β
t-value
Results
H1a
Personal → Social Interaction
.39
5.30**
Supported
H1b
Personal → Rationality
.31
4.67**
Not Supported
H1c
Personal →Reciprocal
.55
8.83**
Supported
Feedback
H1d
Personal → Contact Density
.31
4.38**
Supported
H2a
Digital→ Social Interaction
.15
2.28*
Supported
H2b
Digital→ Rationality
.31
5.07**
Supported
H2c
Digital →Reciprocal Feedback
.42
7.31**
Supported
H2d
Digital→ Contact Density
.23
3.64**
Supported
H3a
Impersonal→ Social Interaction
.14
2.07*
Supported
H3b
Impersonal→ Rationality
.19
2.90**
Supported
H3c
Impersonal →Reciprocal
.21
3.82**
Supported
Feedback
H3d
Impersonal→ Contact Density
.11
1.67
Not supported
H4a
Social Interaction→Satisfaction
.02
.41
Not supported
H4b
Rationality→Satisfaction
.76
11.47**
81 Supported
H4c
Reciprocal
.11
1.81
Not supported
-.15
3.02**
Not supported
Feedback→Satisfaction
H4d
Contact Density→Satisfaction
H5a
Reciprocal Feedback >
Not supported
H5b
Reciprocal Feedback >
Not supported
H5c
Reciprocal Feedback >
Not supported
H6a
Satisfaction→Calculative
H6b
-0.12
1.95
Satisfaction→Affective
.99
19.93**
Supported
H6c
Satisfaction→Trust
.92
15.62**
Supported
H7
Calculative →Advocacy
-.06
1.68
H8a
Affective →Advocacy
.81
7.20**
H8b
Trust →Advocacy
.09
.88
*p<.05, **p<.01
82 Not supported
Not supported
Supported
Not supported
CHAPTER 6 DISCUSSION AND IMPLICATIONS
Drawing on the marketing channels and communications literature, this
study shows how communications differ in their degree of interactivity and can be
differentiated by the characteristics of the media being used. The study made a distinction
between personal, digital, and impersonal communication. Relationship marketing was
used to identify the dimensions of interactivity: rationality, social interaction, contact
density, and reciprocal feedback. Communication has been a key variable in
understanding B2B relationships and this research demonstrates that the content and
mode of communication have a significant impact on relationship satisfaction and that
affective commitment is the key driver of advocacy in a B2B relationship.
Summary of Study
Using the framework developed, we empirically examined the influence of
personal, digital, and impersonal communications on the dimensions of interactivity and
their impact on satisfaction, commitment, and advocacy. Using a two-step structural
equation modeling procedure with the data, the measurement model provided evidence of
adequate fit, reliability, convergent validity and discriminant validity. The fit indices of
the structural model then provided evidence of a good fit. The hypotheses were tested by
examining path coefficients. Fourteen of 23 hypotheses were fully supported.
The results show that personal, F2F communication has the greatest impact on
social interaction, reciprocal feedback, and number of contacts. Digital communication
83
has a weaker effect on these dimensions and impersonal communication has the
weakest effect. The exception to this continuum of interactivity where the more
interactive mode of communication has a greater impact on the dimensions of
interactivity was rationality. Personal and digital modes of communication had equal
impacts on rationality which is the only dimension of interactivity positively associated
with relationship satisfaction. The results also show that affective commitment leads to
advocacy in a B2B channel while trust and calculative commitment do not.
Discussion
Our results show that personal communication is the most interactive followed by
digital and then impersonal. Personal and digital communications have the same positive
association with rationality and rationality is the only dimension of interactivity that was
positively associated with relationship satisfaction. Contact density is negatively
associated with satisfaction. Relationship satisfaction is positively associated with both
trust and affective commitment and affective commitment was the only relational
outcome leading to advocacy.
Buyers are most likely to be satisfied with a relationship when the content of
communications from the supplier provides information that is relevant to work
decisions. Personal and digital communications are equally effective methods of sharing
this information. It appears that F2F communications increase participation, or contact
density, more than digital communications. However, contact density is negatively
associated with relationship satisfaction. F2F communications containing rational
content have the same impact on relationship satisfaction as digital communications, but
84
as the number of participants increases digital becomes the more effective method or
improving relationship satisfaction.
F2F communication is less effective than digital when a larger number of
participants are involved. While the study does not demonstrate the reasons for this
attenuating effect there are several possible avenues for future research. F2F meetings
may involve too much social interaction and too much feedback. The ability to provide
rehearsed information in digital communication may reduce the amount of feedback
needed and at the same time allow participants to ignore or screen out information
deemed irrelevant.
Personal communications are the most expensive mode of communication, and in
addition to aiding in understanding one another, the specific investment required for
personal communication can act as a signal of commitment to the relationship. The study
shows that personal communication has a greater impact on social interaction, reciprocal
feedback, and contact density than digital or impersonal. Social interactions may help to
build relational capital and signal the level of commitment and mutuality between firms.
While these interactions may aid in developing a mutual understanding of one another
they do not improve relationship satisfaction. Reciprocal feedback also increases
understanding and satisfaction with the communications (Mohr and Sohi 1995), but in
this study reciprocal feedback did not have a significant impact on relationship
satisfaction.
85 Digital communication can be as effective as personal when it comes to sharing
information related to work or indirect persuasion. Often F2F sales calls will not make a
direct appeal to make a purchase but rather share additional information that will aid the
decision maker in making a case for a particular purchase or decision. This type of sales
call may be just as effective online as it is F2F. However, digital offers the advantage of
sharing messages outside of the F2F exchange and including others at any time. These
messages can be viewed and responded to right away or after contemplating a response.
Digital also has the benefit of allowing many to many communications. Digital media
has the ability to mimic real communication while adding the ability to control timing,
rehearse responses, and maintain the context of a conversation over time; therefore,
everything that is expected in a F2F conversation should be expected in a digital
conversation and possibly more. It is an effective method of sharing and communicating
in the B2B channel and it is likely to be a more efficient method when the cost of
F2Fcommunication is taken into consideration.
The dimensions of interactivity are not all positively associated with relationship
satisfaction. Reciprocal feedback and social interaction have no significant impact on
satisfaction. Feedback is usually immediate with personal, F2Fcommunications, even if
the feedback is not verbal. Digital communication users may control the timing of
communications but feedback is still expected to occur. The widespread use of digital
communication and the ability of customers to quickly voice any dissatisfaction together
with the lack of a significant relationship between reciprocal feedback and satisfaction
may be interpreted as evidence that feedback is ubiquitous. Previous studies have shown
86 that social interaction can improve mutual understanding as well as customer specific
returns (Palmatier 2006). Our data suggest that these interactions do not improve
satisfaction in a business to business relationship from the buyer’s perspective.
Social interaction and reciprocal feedback may be important for reaching a mutual
understanding but they do not impact satisfaction. When the content of messages helped
justify decisions or provided information that was useful for the work being done, buyers
were more satisfied with the relationship. Rationality was the only dimension of
interactivity that had a positive impact on satisfaction. This positive influence on
satisfaction is attenuated by contact density.
Contact density or the number of relational ties had a negative impact on
satisfaction. As the number of contacts involved in the communications increases,
satisfaction decreases. Personal communication has a positive impact on relationship
satisfaction but as the number of contacts increases, the positive impact of
F2Fcommunication is diminished. When the total impact of personal and digital
communication is examined and we account for both contact density and rationality,
digital communication has the greater impact on satisfaction.
Satisfaction is not associated with calculative commitment. Calculative
commitment is the decision to continue and maintain a stable relationship for economic
reasons and affective commitment is the desire to continue a relationship because of
liking or enjoyment. Satisfaction does have a large positive impact on affective
commitment. The study focused on existing relationships with a major supplier and these
relationships may begin with calculative commitment but once affective commitment
87 develops it may overshadow other considerations. The focus may shift from the terms of
a specific transaction to the development of mutually beneficial transactions.
Satisfaction also has a positive impact on the development of trust in the
relationship. Satisfied buyers are more likely to like and trust their suppliers. Trust was
not shown to have a significant relationship with advocacy, which was only influenced by
affective commitment. The study suggests that buyers like suppliers who use F2F or
digital communication and that as the number of contacts increases the use of digital will
be more likely to lead to relationship satisfaction.
Saying good things about a partner requires a social investment, when a
relationship feels forced, buyers are not motivated to invest in the relationship (Fullerton
2003). Affective commitment provides the motivation necessary for this social
investment. Affective commitment is the way to foster Advocacy in a B2B channel. In
the B2B setting, communication that is focused on work related topics is the primary way
to increase relationship satisfaction, which increases affective commitment and affective
commitment is the primary driver of advocacy.
Limitations
As this was an initial test of a theoretical model we were not as concerned about
generalizability as we would be if testing an existing model. The results of this research
cannot be readily generalized to the larger population. A specific industry was chosen in
order to limit the influence of extraneous sources of variation which could confound our
results, but this and the fact that the entire sample is U.S. based limits the ability to
generalize the results.
88 The loyalty construct was dropped from our model. Several of the items used to
measure loyalty, though previously used in other research, loaded on the calculative
commitment construct. The remaining items were too few to be used for SEM.
We were unable to distinguish between buyer and supplier contacts. Our
measures for the number of buyer and supplier contacts were highly correlated making it
impossible to distinguish between the two measures. Rather than examining the separate
impacts of buyer and supplier contacts the measures had to be combined into a single
manifest variable measuring contact density.
Implications
We showed that personal, F2Fcommunication is the most interactive mode of
communication followed by digital and then impersonal. When cues that cannot be
easily communicated online such as tone of voice, posture, breathing and speaking
patterns, and facial expressions are necessary, F2F may offer benefits over digital.
However, these benefits dissipate as the number of contacts increases.
The study showed that digital communication has the same impact on rationality
as F2Fcommunication. Rationality involves providing information to justify a decision,
e.g., when the content of communication is focused on getting work done. Rationality
was the only dimension of interactivity positively associated with satisfaction. Social
interaction and reciprocal feedback do not have a significant impact on relationship
satisfaction. Often in a F2F meeting, avoiding social interaction is not possible or at least
not polite. For instance, consider workplace meetings where social interaction does take
place. This meeting may take longer and possibly involve more people than are
89 necessary for an outcome that could have been reached through digital communications
in less time.
Increasing the number of contacts will have a negative effect on satisfaction for
both F2Fand digital communication, but as the number of contacts increases, this
negative impact is greater for F2Fcommunications than for digital. Further research is
required to determine the optimum or maximum number of participants that can
effectively communicate in a group.
Too many contacts involved in personal communications will reduce the positive
impact of these interactions. Investigating situational variables such as the complexity of
the task environment, the relationship stage, or stage in the buying decision may lead to a
clearer understanding of the best combination of personal, digital, and impersonal
communication in different situations. As the number of contacts increases, digital
communication becomes increasingly important. Digital communication is the preferred
method for many to many communications. Future research might consider the desired
outcomes and investigate how objective measures such as profits and new accounts can
be obtained through advocacy.
The study has several important implications for managers. Digital
communications have several benefits including ease, speed, recording capabilities,
control over synchronicity, rehearsability and the ability to interact with a large number
of contacts. F2F also provides several benefits including immediate feedback, social
interaction and cues that cannot be communicated via the internet. These benefits can be
compared with the benefits of digital communication. Personal, F2F communication is
90 the best method for obtaining reciprocal feedback and for increasing social interaction,
which may sometimes be a goal of communication. Digital communication, on the other
hand, may allow managers to ignore unnecessary or irrelevant feedback and social
interaction. When the goal of communication does not require social interaction and
immediate feedback, digital communication may be at least as effective as F2F. If the
communication goal is task related and multiple contacts are involved, then digital
becomes the more effective communication method. Rationality, which is instrumental
communication, is the only dimension of interactivity having a positive impact on
relationship satisfaction and can be accomplished equally well through digital or
F2Fcommunication.
F2Fmeetings can be a drain on resources and time and the success of these
interactions depends on the number of contacts involved. Having multiple relational ties
reduces the positive impact of communication and this attenuating effect is greater with
F2Fcommunication than with digital. For communicating within larger groups digital is
the best alternative. When only a few contacts are involved, managers should first decide
if it will be necessary to “read” the other parties’ tone, expressions, and body language or
if it’s necessary to be “read” in order to effectively communicate. Next, the manager
should consider how the number of participants will impact these communications.
To encourage advocacy, fostering relationships where your company is well liked
will be important. In order to convert buyers to advocates they must enjoy doing
business with you. Satisfaction leads to affective commitment, which is enjoyment or
liking. Both personal and digital communications promote satisfaction in B2B
91 relationships. Messages with the greatest positive impact on satisfaction are those
messages that aid managers in making and justifying decisions. As the number of people
involved in these communications increase the method of communication should move
from personal to digital. Digital can provide many of the interactive benefits of F2Fas
well as added benefits such as rehearsability, control over timing, and many-to- many
capabilities. As the number of relational ties increases, digital communication becomes
more beneficial to the relationship than F2F. In order to improve satisfaction with a
relationship, communication content should focus on information for justifying decisions.
These messages have positive impact on satisfaction and lead to greater enjoyment of the
relationship, which increases the likelihood of a buyer becoming an advocate.
92 APPENDIX 1
Survey Instrument Thank you for participating in this study. Please choose one of your major suppliers whose relationship with your company is familiar to you, and indicate your level of agreement with the following statements on a range from Strongly Agree to Strongly Disagree. These statements are about your relationship with this supplier and the use of F2F communication, interactive communication (email, instant messaging, texting, telephone, social media, video, blogs, etc), and traditional nondigital media (advertising, sales literature, letters, memos, etc.). Strongly Agree Agree Neither Agree nor Disagree Disagree Strongly Disagree We are delighted with this supplier and want to stay with them.      We will stay with this supplier because  switching suppliers is costly.     This supplier has high integrity.      We will discourage others from doing business with this supplier.      We tend to disregard problems with this supplier.      We are unhappy with this supplier.      We have informal conversations with members of this supplier.      93
We primarily communicate with one individual at this supplier.      If asked we will speak favorably about this supplier to others.      This supplier has frequent two‐way communication with us.      We rarely have F2F meetings with this supplier.      This supplier communicates with members of several levels of management at our company.      We frequently share information with this supplier using interactive communication.      We often collaborate with this supplier in F2F meetings.      We frequently communicate with this supplier using traditional nondigital media.      We will recommend this supplier to others.      We will stay with this supplier because  we have a contract.          We will stay with this supplier because  changing suppliers is too difficult.     This supplier is not always reliable.      We like working with this supplier and want to remain with them.      We find this supplier pleasant to work with and want to stay with them. 94
We sometimes ignore problems with this supplier because we want to stay with them.      We are very satisfied with this supplier.      We will stay with this supplier because  of our investments in the relationship with them.     We have not had a good experience with this supplier.      We have close personal relationships with members of this supplier.      If asked we will tell others about our good experience with this supplier.      We like the values of this supplier and want to stay with them.      This supplier is very honest.      This supplier provides us with information that helps guide our decisions.      This supplier solicits our views on an ongoing basis.      We are pleased with the relationship we have with this supplier.      We will stay with this supplier despite any problems that occur.      This supplier can be counted on to do what is right.      We would choose this supplier if we had to do it over again.      95 All of our interactions with this supplier are solely business related.      We communicate with several individuals at this supplier.      This supplier communicates with several individuals at our company.      We will remain loyal to this supplier even when we have problems.      This supplier can be trusted completely.      We genuinely enjoy our relationship with this supplier and want to remain with them.      We will stay with this supplier because  of a lack of good alternatives.     We have frequent online interactions with this supplier.      We frequently share information with this supplier in F2F meetings.      We are often in contact with this supplier using traditional nondigital media.      This supplier interacts with members of our company socially.      We frequently share information with this supplier using traditional nondigital media.      This supplier provides us with a lot of feedback on our performance.      96 We communicate with members of several levels of management at this supplier.      This supplier has regular dialogues with us.      This supplier primarily communicates with one individual at our company.      This supplier shares the results of their  experience with us.     We often collaborate with this supplier using interactive communication.      We rarely use traditional nondigital media to communicate with this supplier.      We often have online contact with this  supplier.     Some of our communication with this supplier is personal.      We communicate with members of several departments at this supplier.      We rarely have interactive communication with this supplier.      We will encourage others to do business with this supplier.      This supplier provides us with reasons for choosing a particular action.      We often have F2F contact with this supplier.      97 We often collaborate with this supplier using traditional nondigital media.      This supplier communicates with members of several departments at our company.      This supplier responds promptly to communications from us.      We have frequent F2F interactions with this supplier.      This supplier treats some members of our company like friends.      This supplier provides us with  information we can use when deciding between alternative courses of action.     Our relationship with this seller is improving      Our firm's relationship with this seller is getting worse over time.      Our relationship with this seller is on a positive trajectory.      98 In our communication with this supplier we use (select all that apply):  Twitter  Facebook  YouTube  LinkedIn  Blogs  Online Communities  Online Meetings  None How many people are employed by your company?  Less than 5  5‐50  51‐100  101‐500  501‐1000  More than 1000 What was your company’s revenue last year (2012)?  Less than 500,000  Greater than $500,000 to $1 million  Greater than $1 million to $2 million  Greater than $2 million to $5 million  Greater than $5 million 99 Which of the following best describes your organization?  One Location of an organization with multiple locations  franchise  Local independent  Headquarters of organization with multiple locations  None of these apply Which of the following best describes your business?  Government Organization  Non‐profit organization  For profit organization In which state do you currently reside? 100 REFERENCES
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