Value Logics and Management Control Systems
July 29, 2016
Mark Klassen, PhD
Department of Accounting
Edwards School of Business
University of Saskatchewan
Saskatoon Canada
David Otley, PhD
University Management School
University of Lancaster
Lancaster, England
Abstract
Purpose: This paper explores the relationship between strategy and management control
systems through the lens of the value logic typology (Stabell and Fjeldstad, 1998).
Strategic typologies and management control systems has been an area of interest to
researchers. However, scholars have expressed concern over the conceptualizations.
The value logic typology builds on the Value Chain (Porter 1985) model to present an
alternative strategic typology, one that incorporates traditional firms (Value Chains) as
well as knowledge based firms (Value Shops) and network firms (Value Networks).
Design/methodology/approach: The relationship of value logics and management
control systems is explored by conducting interviews across Value Chain, Value Shop
and Value Network firms. We further extend our research by administering a
questionnaire to test our predictions of differences in the management control systems.
Findings from the qualitative field work and survey are both reported in this research.
Findings: Our findings suggest that differences do exist across the value logic categories,
particularly in how the management control systems are emphasized and the
characteristics of the management control systems.
Research limitations/implications: We argue that the value logic, is an important area
of interest to researchers and practitioners as economies migrate from traditional, Value
Chain, firms towards more knowledge-based, Value Shop, and network-based, Value
Network firms.
Originality/value: This study offers a new perspective of investigating the relationship
between strategy and management control systems by utilizing a typology, the value
logics, rather than traditional strategic typologies researched in years past.
Keywords:
Management control systems, strategy, value logics, contingency theory.
1. Introduction
Strategy has been recognized as an important variable in the study of management
control systems (MCS), particularly using contingency theory as a theoretical orientation
(Chenhall, 2003; Langfield-Smith, 2005, 1997). One area of the strategy - MCS
relationship that has been heavily researched uses strategic typologies to investigate the
relationship between a firm’s strategy and its MCS (Abdel-Kader and Luther, 2008;
Chenhall and Morris 1995; Dent, 1990; Simons, 1987). Examples of strategic typologies
include Porter’s cost leadership-differentiation (Porter, 1980), prospectors-analyzersdefenders-reactors (Miles and Snow, 1978), build-harvest-hold (Gupta and Govindarajan,
1984) and entrepreneurial-conservative (Miller and Friesen, 1982). Scholars have
recognized the strategy - MCS contingent research has made an important contribution to
management accounting research (Chenhall, 2003; Langfield-Smith, 1997, 2005;
Chapman, 2005; Tucker et al., 2009). The premise is that MCS should be tailored to
support the strategy of the business to lead to competitive advantage and superior
performance (Tucker et al., 2009; Dent, 1990; Samson et al., 1991). Empirical work has
often followed a contingency approach and involves investigation for systematic
relationships between specific elements of the MCS and the strategy of the organization
(Chenhall and Langfield-Smith, 1998; Moores and Yuen, 2001; Simons, 1987).
Contributions have evolved through critical mass and allowed scholars to surmise general
propositions related to strategy and MCS. For example, Chenhall (2003, p. 151)
concludes that, “Strategies characterized by conservatism, defender orientations and cost
leadership are more associated with formal, traditional MCS focused on cost control,
specific operating goals, budgets and rigid budget controls, than entrepreneurial, build
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and product differentiation strategies”. In addition to utilizing typologies, work on the
strategy – MCS relationship followed a number of research paths such as strategic change
(Abernethy and Brownell, 1999), operational strategies (Ittner and Larcker, 1997;
Davilla, 2000; Abernethy et al., 2001), strategic innovation strategies (Bisbe and Otley,
2004; Ittner et al., 2003) and alternative strategic models such as the resource based view
of strategy (Henri, 2006). Despite the abundance of research in the strategy – MCS
domain, scholars have expressed concerns about the disparate conceptualizations used
(Chapman, 1997; Abernethy and Brownell, 1999; Nilsson and Rapp, 1999; Chenhall,
2003; Luft and Shields, 2003; Gerdin and Greve, 2004; Merchant and Otley, 2007). One
critique of the typologies is that they do not integrate with each other in a convincing
way. Strategic typologies used in MCS research are argued to overlap (Chenhall, 2003;
Kober et al., 2007; Langfield-Smith, 1997) and are criticized as being dated (Chenhall
2003; Tucker et al., 2009). The central argument and contribution of this research is to
introduce a new contingent variable, the value logic, previously not investigated in the
strategy - MCS domain. Value logic is related to strategy, but differs from it in that the
value logic represents a business model within which a variety of strategies can be
developed. Value logic can therefore be seen as a more fundamental variable that
precedes business strategy.
The transformation towards service-based economies and the growth of the internet and
network type organizations adds relevance to researching the strategic question from a
value creation perspective. In the last twenty years firms such as Google, Facebook, ebay
and Twitter have grown into large successful organizations utilizing business models that
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are substantially different from those used by more traditional firms characterized by
Porter’s Value Chain (1985) model. In addition, this study uses a broader context of
MCS as earlier definitions of management controls systems (Anthony 1965) have been
argued to be too narrow (Chenhall,2003; Langfield-Smith, 1997; Merchant and Otley,
2007; Ferreira and Otley, 2005; Otley, 2007) and more recently oriented towards control
packages (Chenhall et al., 2011; Sandelin, 2008; Malmi and Brown, 2008; Abrahamsson
et al., 2011). MCS in this study is investigated by understanding the extent MCS is
emphasized across the value logics as well as the specific characteristics of the MCS.
Our research approach is qualitative through the use of field interviews performed across
an array of value logic firms. The research is then extended quantitatively through a
questionnaire administered to value logic organizations. In addition to the findings, this
paper contributes to the strategy – MCS domain by utilizing a mixed research method,
which has the advantage of convergence of findings from both research approaches
(Grafton et al., 2011). The findings of both the field interviews and questionnaire are
reported within this paper. The notion of fit in contingency theory research is important
and has been extensively discussed (Burkert et al., 2014; Gerdin and Greve, 2003, 2005;
Hartmann and Moers, 1999, 2003). The notion of fit used here within the contingency
theory framework is that of congruence (Gerdin and Greve, 2005) and is argued to be
justified by the early-stage nature of the study. As Chenhall states, “it may be useful for
contingency-based studies to first establish adoption and use of MCS, then to examine
how they are used to enhance decision quality and finally investigate links with
organizational performance” Chenhall, 2003, p. 135).
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2. Theory Development
2.1 Interpretation of MCS and Value Logics
Theory offers several ways of defining and classifying MCS (Anthony, 1988; Simons,
1994; Merchant and Van der Stede, 2003; Otley, 1999; Ferreira and Otley, 2009). For
the purpose of this study the MCS framework initially proposed by Thompson (1967) and
Ouchi (1979) and significantly refined by Merchant (1982) and Merchant and Van der
Stede (2003) is used. This framework provides several advantages for this study. The
framework is inclusive of a broad set of controls, which is important when exploring a
new contingent variable where value logic – MCS research is scant. Utilizing a broader
based MCS addresses criticisms that research has tended to approach the investigation of
MCS from a narrow perspective (Langfield-Smith, 1997; Otley, 1980; Fisher, 1995;
Chapman, 1998).
A firm’s value logic orientation is conceptualized by Stabell and Fjeldstad (1998), and
answers the business model question, how does a firm create value (Stabell and Fjeldstad,
1998)? The value logic conceptualization is mapped to a typology that builds upon the
seminal work of J.D. Thompson (1967) and Michael Porter’s value chain (Porter, 1985).
The value logic follows the strategic position of Nelson (1991) who argues that
understanding how firms differ is a central challenge for both the theory and practice of
strategic management. Stabell and Fjeldstad (1998) argue that the generic Value Chain
orientation used to strategically analyze a firm’s ability to create value is not applicable
across all industries. Bain and Company (a consulting firm) create value differently than
Toyota. Likewise Facebook (an online networking firm) creates value differently than
Bain and Company. Rather than ask the question, “Is Facebook a defender or
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prospector?” (Miles and Snow, 1978), this research begins by asking the question, “How
does Facebook create value?”
The value logic premise is that firms create value differently and they can be categorized
into three groups (Value Chain, Value Shop and Value Network) based on the firm’s
value creation technology. Value Chain firms create value by transforming inputs into
standardized products and services based on a long-linked technology (Thompson, 1967).
Examples of these firms are industrial manufacturers, fast food restaurants, mining
companies, and one-hour photos shops (Stabell and Fjeldstad, 1998; Thompson, 1967;
Sheehan et al., 2005; Fjeldstad and Haanaes, 2001). The product is the medium for
transferring value as raw materials are transported to the production facility and then
transformed into a product and finally transferred to the customer. The strategic
challenge is to produce products with the right quality at the lowest possible cost
(Fjeldstad and Haaneaes, 2001). Value Chains use economies of scale and capacity
utilization to reduce costs (Sheehan et al., 2005). The focus on cost leads to Value
Chains being “process” biased (Fjeldstad and Haaneaes, 2001) as they look to improve
the efficiency of the transformation process.
Whereas the Value Chain’s goal was to produce large numbers of standardized products,
the Value Shop’s goal is to solve customers’ problems. The problem that needs to be
solved will dictate the intensity of the activities. Value Shops are based on Thompson’s
intensive technology (Thompson, 1967, p. 17), including industries such as hospitals,
legal firms, consultants, garages and petroleum exploration (Thompson, 1967; Stabell
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and Fjeldstad, 1998; Sheehan et al., 2005; Fjeldstad and Haanaes, 2001). An information
gap exists between the customer and firm which employs problem solving experts and
can leverage additional specialists if required. Knowledge and reputation become key
value drivers for Value Shops (Sheehan et al., 2005). A strong reputation will attract
expert employees as well as customers through referrals. The problem solving
configuration is cyclical as it may involve several iterations of communication to
diagnose and solve the problem. The problem solving process may spiral in the sense
that new problems are uncovered needing to be solved by other experts.
The Value Network firm uses mediating technologies (Thompson, 1967) and links clients
or customers by facilitating the interdependent relationship. Value Networks belong to a
system of layered and interconnected networks. The interactivity within the network and
between networks is simultaneous and parallel (Stabell and Fjeldstad, 1998) because
many interactions by network members are occurring at the same time. For example
ebay members have similar parallel transactions occurring simultaneously every minute.
The value for the customer is being able to connect to the network and the characteristics
of the network that they are connecting to. The Value Network establishes, monitors and
terminates relationships amongst members and charges for accessing or performing
activities on the network. The increased number of connections to the network benefits
the customers and the firm (Kats and Shapiro, 1994). Value is created by the firm by
either increasing the number of customers on the network or by obtaining more profit
from existing customers utilizing the network. Examples include banks,
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telecommunications, airlines and many internet based firms (Stabell and Fjeldstad, 1998;
Sheehan et al., 2005; Fjeldstad and Haanaes, 2001).
Insert Table 1 Here
2.3 Contingency Theory Literature
To begin exploring the potential differences in MCS across value logics, a review of
MCS contingency literature was performed.
From a contingency perspective,
technology as a contingent variable is informative to the Value Chain and Value Shop
categories.
Technology has been defined as how the organization of work processes are
organized and research can be grouped into the themes of task uncertainty and
interdependence (Chenhall, 2003). Daft and Macintosh (1981) find that technologies
with high task analyzability (low task uncertainty) are related to high reliance on standard
operating procedures, programs and plans (Daft and Macintosh, 1981). Hirst (1983)
researched task uncertainty and its relation to accounting performance measures. Hirst
(1983) concluded that tasks high in difficulty would not rely on accounting performance
measures. Using Ouchi’s (1979) MCS framework, Rockness and Shields (1984)
concluded that input controls such as social controls and expenditure budgets, appear to
be most important when there is little knowledge of the transformation process
(technology uncertainty). Behaviour controls such as rules, procedures, program
evaluation review technique (PERT), are most important when there is a high level of
knowledge of the transformation process.
Abernethy and Brownell (1997) found that
when task uncertainty is highest, reliance on personnel controls and not accounting or
behavioural controls positively relates to performance.
In the Abernethy and Brownell
(1997) study behavioural and personnel controls were explained as being similar to action
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and personnel controls as defined by Merchant (1985). Abernethy and Brownell (1997)
believe that an individual’s level of professionalism can be represented by the length of
professional training and socialization processes to which they are exposed. This would
suggest that involvement in professional associations is an important factor in Value
Shop’s management control similar to Mintzberg’s (1979) description of Professional
Bureaucracy. Mia and Chenhall (1994) contrasted marketing departments with
production departments and found higher task uncertainty and broader MCS in
marketing. Brownell and Dunk (1991) researched manufacturing firms and concluded a
relationship between low task difficulty, participative budgeting and a high budget
emphasis. High task difficulty also supported participative budgeting, but budget
emphasis did not seem to matter. What is interesting about this study (Brownell and
Dunk, 1991) is that as the degree of task difficulty increases, budget emphasis seems to
lose relevance. Lau, Low and Eggleton (1995) found similar results replicating Brownell
and Dunk (1991) in a different cultural setting – Singapore.
Interdependence is defined as the extent which departments need to rely on other
departments for resources (Gerdin, 2005; Thompson, 1967). High levels of
interdependence are associated with informal controls such as statistical reports for
planning and informal communication (Macintosh and Daft, 1987), frequent interaction
with managers and superiors (Williams et al., 1990), broad scope MCS (Chenhall and
Morris, 1986) and greater usefulness of aggregated and integrated MCS (Bouwens and
Abernethy, 2000). Value Shops are conceptualized to have complex interdependence
that acts in a reciprocal manner as the problem solving nature moves back and forth
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sometimes involving additional experts to solve the problem. Value Chains’
interdependence is characterized as sequential as one department passes on information
or products to the next (Stabell and Fjeldstad, 1998).
Insight into Value Shop’s MCS is also provided through Widener (2004) as she
researched the relationship between strategic human capital and MCS. Widener (2004)
argued that strategic human capital, which includes the knowledge and skills of
employees (Barney, 1991), enhances the knowledge of the firm and is a primary strategic
resource of many firms needed to sustain competitive advantage (Quinn et al., 1996).
Her study found that the use of strategic human capital was positively associated with the
use of personnel and non-traditional controls. Widener (2004) argues that firms will
invest more in personnel controls if employees are strategically important to the firm
(Snell and Dean, 1992). For Value Shops, employees and the knowledge they possess are
the key drivers to solve problems in Value Shops (Stabell and Fjeldstad, 1998).
Knowledge of employees and organizational knowledge has also been researched in the
context of MCS through the broad label of knowledge management (Mouritsen and
Larsen, 2005). The tacit knowledge that helps a firm compete is embedded in the
employees and thus makes it tough to manage (Coff, 1997; Kogut and Zander, 1995).
One reason it is difficult to manage is because the tacit knowledge is tough to transfer and
replicate (Tsai, 2006). Face-to-face communication is often used to overcome this
difficulty (Teece, 1977). Coff, et al. (2006) recognize the importance of leveraging tacit
knowledge in firms that use human capital as a strategic competency and has been linked
9
to firm performance (Gates and Langevin, 2010). They conclude that information
technology can be an effective tool to leverage and communicate the rare knowledge, but
that it also has implications on the degree to which the information is coded, or in a
sense, standardized (Coff et al., 2006). The characteristics of a Value Shop present a
configuration where knowledge was an important aspect to how Value Shops create
value. Knowledge and reputation drive value because customers will be attracted to the
Value Shop believing the Value Shops employees and knowledge resources will enable
the problem solving ability of the Value Shop.
Whereas the contingency literature illustrates a polarization of MCS for Value Chains
and Value Shops, the Value Networks are more complex. Stabell and Fjeldstad (1998)
argue the Value Network would adopt an Administrative Adhocracy design based on
Mintzberg (1979) design alternative framework. The structure of the Value Network
contains an operating core which is the network or infrastructure that connects to the
network as well as an administration function that supports the business with activities
such as promoting the network, managing contracts and service provisioning (Stabell and
Fjeldstad, 1998). Mintzberg (1979) believes the operating core can become similar to the
Machine Bureaucracy and is characterized by: “highly specialized, routine operating
tasks, very formalized procedures in the operating core, a proliferation of rules,
regulations and formalized communication throughout the organization, large-sized units
at the operating level, reliance on the functional basis for grouping tasks, relatively
centralized power for decision making” (Mintzberg, 1979, p. 315). The administration
function are characterized by problem solving activities. This conceptualization suggests
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Value Networks are similar to Value Chains for the Operating Core (the network) and
Value Shops for the administration function.
Table 2 is presented to integrate the findings from the contingency literature. Consistent
with the choice of MCS framework utilized in this research, the MCS findings have been
categorized according to Merchant and Van der Stede’s (2003) framework. The
consistent themes in the literature suggest that Value Chains through their standardization
of work processes, low task difficulty, higher task certainty and routine operating tasks
are associated with results controls and action controls. Alternatively the problem
solving nature of the Value Shops leads to higher task difficulty, lower task certainty,
more complex interdependence and a strong reliance on the human capital and
knowledge as a strategic asset. Value Networks are conceptualized to follow a similar
Value Chain pattern of MCS for the operating core and a Value Shop pattern for the
administrative component and therefore placed in the middle. The “High, Low,
Medium” categories are the conceptual association of the MCS described in the literature
and the Value Logics.
Insert Table 2 Here
3. Case Studies
To explore MCS across value logic firms, we first conducted field interviews at twenty
organizations as an initial pilot study. Exploratory fieldwork is important when the area
of investigation is “new” and there is an absence of an extant body of theory and data
(Glass and Strauss, 1967, 1970; Eisenhardt, 1989). Given the lack of theoretical and
empirical investigation into the value logic and MCS area, beginning with semi-
11
structured field interviews was deemed appropriate to build knowledge about the
phenomenon (Yin, 2003). Standard practices for qualitative data analysis were used
following the guidelines of Nachmias and Nachmias (1992) and Yin (2003). Procedures
were adopted to enhance external, context and construct validity as well internal
reliability. For example planning, communications and pre-interview discussion were
consistent amongst the interviewees to mitigate observer bias (Lillis, 2006) and were pretested with academics and a select group of subjects (Smith, 2003). Interviewees’
comments were probed and clarified in the interview to reduce interpretation gaps
(McKinnon, 1988) and supporting documentation was collected to promote triangulation
(Ryans et al., 2002; Silverman, 2001). Interviews were transcribed and inductive codes
were established to identify common themes by each of the MCS categories in
Merchant’s framework. To promote external validity, firms were selected according to
their value logic type following an approach used by Snow and Hambrick (1980).
Results: Value Chains
Budgets were discussed by interviewees as being the most important control system to
ensure the strategies of the organization were being adhered to and monitored.
Interviewee responses reflected budgets being used in a “tight” manner. Examples of
tightness included multiple review processes for budget approvals, frequency of
reviewing the budget and the level of detail within the budget. Examples were obtained
that illustrated how high-level budgetary numbers could be “drilled down” to detailed and
hierarchal representations of the budgetary number. As one interviewee noted, “nothing
of any significance gets accomplished unless it goes through our budgetary process”.
Performance measures were also used extensively, but more frequently in the context of
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process and efficiency measures. There was a consensus from Value Chain firms that
when performance measures were used, it was difficult to cascade the performance
measure to the individual employee level. Employees were “connected to equipment and
process” making it difficult to assess performance measures at the individual level.
Action controls were pervasive in Value Chains, particularly with respect to standard
operating procedures, supervision and security or limitation of access. Interviewees
described how their organizations spent considerable effort to ensure procedures were
detailed, understood and followed by employees. An interviewee from a manufacturer of
turbines described how supervisors carry a note pad in their pocket at all times to note
any occurrences or deviations from the established operating process.
Discussions about personnel controls at value chains were relatively consistent. Selecting
the best operational employees (employee selection) was not viewed as critical compared
to Value Shop interviewees. Value Chain interviewees felt that through training,
employees could be taught the established process and procedure. In this sense it was
less important to hire the right employee as it was to ensure appropriate training was in
place so that employees understood the process required to produce the goods. Although
an extreme example, in the interview process, a senior executive of an agriculture
manufacturer expressed, “if you have half a brain you are generally qualified… we will
teach the employee what to do”. The mission and vision statements were not seen as
critical control systems compared to the other control systems discussed.
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Results: Value Shops
The interviews conducted at Value Shop firms resulted in a consensus that personnel
controls were critical in order for the firm to succeed. Whereas Value Chain interviewees
emphasized their ability to train employees to follow the appropriate procedures, Value
Shop interviewees explained that hiring the “right” person for the job was critical. The
methods used in employee selection were more extensive including examples of multiple
interviews from a cross section of managers, aptitude tests, networking and extensive
background analysis. Examples of words used by interviewees to describe the
importance of hiring the right person were; “huge”, “critical”, “lots of emphasis” and
“needs to be in the top quartile”. Whereas training was often described as being
performed “in-house” for Value Chains, Value Shop interviewees expressed the
importance of the professional associations to provide technical training to their
employees. Training was also discussed in the context of mentoring and coaching to
ensure judgment was being passed on between senior and junior resources. Similar
results were obtained from the supervision discussions. Supervision was as much about
mentoring as it was about ensuring employees were following a standard operating
procedure. Standard operating procedures were often reframed by Value Shop
interviewees into a discussion of providing their employees with methodologies and best
practices. For Value Shops, the key control was not to ensure an employee followed a
scripted process or standard operating procedure, but to equip the employee with
knowledge to allow them to perform the work. Interviewees also expressed the
importance of establishing a control to ensure employees had access to expertise
throughout the firm. Examples were obtained that showed elaborate knowledge
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databases and search inquiry capabilities to find expert resources and/or specialized
information such as past client deliverables. A notable exception of a detailed standard
operating procedure raised by Value Shop interviewees related to the security of the
information and in some cases privacy of the customer. Examples were provided by
interviewees of tight processes related to securing the information of the firm. Mission
and vision statements were generally emphasized more in Value Shops. There were more
examples of visibility and communication of the mission and vision in the Value Shop
firms. In one Value Shop, the new employee orientation training required all employees
to be able to cite the mission and vision of the firm. In another organization, each
employee’s business card had the vision of the firm on the back of the card.
Budgets were used in all organizations interviewed with the exception of a law firm.
They were expressed as being important but discussed in a different context. For Value
Chain firms the budget was the key control to execute strategy. For Value Shop firms,
budget targets would be achieved if the firm had the right employees performing the
work. Value Shop interviewees described the budgets as being important to establish the
future “pipeline” of work. In this respect the budget was not as important to monitor
variances of present work as it was to forecast future demand of revenue. The future
revenue or “pipeline” of work was critical to ensure the appropriate number and skill
level of employees was being managed. From a performance measurement perspective,
Value Shop interviewees gave examples of how the goals and objectives of their firm
“cascaded” to the individual employee level. Examples were obtained showing how an
individual’s goals and objectives aligned to the firm goals and objectives primarily
15
through their pay for performance incentive programs. The opinion of most Value Shop
interviewees was that employees understood how their individual objectives and
measures aligned to the firm’s objectives and measures.
Results Value Networks
Perhaps the most telling word used by Value Network interviewees was the word
“depends”. For many of the MCS discussed, interviewees answered questions by
framing their response to a particular area within their organization. Interviewees
explained their organizations as having a basic infrastructure that operated differently
than the rest of the organization. For example the banking firms highlighted the context
of the banking infrastructure whereas the telecommunications firm discussed the context
of the network infrastructure (switches, and telecommunication lines). Although the
names of the environments differed, interviewees described their organization as the
network infrastructure (the operating core) and the “strategic side” (the administrative
function) of the business, which was everything else in the organization.
Descriptions of MCS from Value Network interviewees became similar to the Value
Chain interviews for the operational or network aspects of their business and similar to
Value Shops for the strategic side of their business. Operating the networks had tight
budgetary control and detailed performance measures that were oriented towards process
measures. Managers discussed performance measures as being very precise, detailed and
constantly monitored. Network availability was cited by all interviewees as the most
important measure. As one interviewee explained “we are chasing the third decimal in
network availability… network downtime can be critical to our business”. Managers
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explained that it was difficult to cascade organizational goals and objectives directly to
individual employees whose primary role was associated with the network. There were
many examples of detailed standard operating procedures and tight supervision as well as
extensive training to ensure procedures of running the network were being followed.
Security and limitation of access was generally explained to be critical in regards to
protecting the network. Physical limitation of access and in many cases examples of
multiple authority levels and procedures were in place to prevent unwarranted access to
the network. Employee selection was seen as important to ensure resources had the
technical skills to operate the network effectively. The perception of the Value Network
managers was that the organization had invested heavily in controls to ensure the network
was running “smoothly”. As a result, for many employees who were not directly
involved with running the network, the focus was on the “strategic side” of the business.
Discussions about MCS for the strategic side of the Value Network organizations were
more similar to the Value Shops. Managers explained that budgeting was oriented
towards forecasting revenue and either attracting new customers or maximizing profits
from existing customers. Similarly, performance measures and pay for performance
were often associated with growth of the number of customers or profitability of the
customers. For the strategic side of the Value Network business, supervision and
standard operating procedures were discussed in terms of broader programs operated by
the organization. Examples of broader programs included large scale marketing
campaigns and customer engagement processes. The programs created broader
procedures for employees to follow as they performed their work. For example the
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marketing campaign at a bank involved cross selling additional products and created
specific procedures and targets for employees that were supervised and monitored. As
the priorities of the Value Network shifted, new programs introduced new procedures.
Capturing and managing knowledge was expressed to be important for Value Networks
similar to Value Shops, but in a different context. For Value Shop firms, the perspective
of knowledge management was centered on the employee. The goal was to equip the
employee with access to additional experts or information to allow them to perform their
job better. In Value Networks, an emphasis was placed on capturing knowledge about
the network or the customers who used the network. This information allowed the
organization to adopt new programs to attract new customers or maximize profit with the
existing customers. Examples were documented of elaborate databases that mined
customer and network data to segment customers into different behavioural groupings
and how marketing and operational programs were developed to target the groupings.
Value Network managers discussed how employee selection and employee training were
becoming more important for their organizations to ensure the appropriate programs were
being launched. The discussions related to mission and vision statements were mixed.
While some Value Network organizations appeared to emphasize mission and vision as a
way to bring employees together with a common purpose, other managers expressed
doubt as to their organization’s commitment to mission and vision.
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4. Development of the Research Predictions
4.1 Value Chain Controls
The characteristics of Value Chains suggest they create value by transforming inputs into
standardized products (Stabell and Fjeldstad, 1998), where tasks are routine with a
proliferation of rules, regulations and formalized procedures. Findings from the
technology contingency literature suggest that in an environment where task uncertainty
is low and there is a high level of knowledge of the transformation process, then budgets,
and action control such as standard operating procedures, rules, procedures and program
evaluation techniques will be utilized (Daft and Macintosh, 1981; Hirst, 1983; Rockness
and Shields, 1984; Abernethy and Brownell, 1997; Mia and Chenhall, 1994; Brownell
and Dunk, 1991). The field interviews supported these findings for Value Chains firms.
Interviewees described “tight” budgetary and performance measurement systems that
were process oriented, detailed, monitored extensively and involved multiple levels of
approvals. Security and limitation of access was described to be emphasized by all Value
Logics in the interviews, but for different reasons. For Value Chains, securing the
production facilities and equipment was important to ensure procedures were followed
and employees were safe. Value Network interviewees highlighted the important of
securing access to the network or operating environment to ensure the network was
operating effectively at all times. Value Shops interviewees discussed the importance of
limiting access to confidential information. We therefore believe security and limitation
of access will not be emphasized more in any Value Logic.
Prediction 1: Value Chains will emphasize budgets more than Value Shops and
Value Networks.
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Prediction 2: Value Chains will emphasize performance measures more than
Value Shops and Value Networks.
Prediction 3: Value Chains will emphasize standard operating procedures more
than Value Shops and Value Networks.
Prediction 4: Value Chains will emphasize supervision more than Value Shops
and Value Networks.
Prediction 5: Security and Limitation of Access will not be emphasized to the
same degree in all Value Logics.
4.2 Value Shop Controls
The Value Shop creates value by solving problems with the nature of the problem being
solved dictating the intensity of the activities. The conceptual literature (Stabell and
Fjeldstad, 1998; Thompson, 1967; Sheehan et al., 2005; Fjeldstad and Haanaes, 2001)
suggested that employees are more likely to control themselves through self-control
(Orlikowsky, 1991; Abernethy and Stoelwinder, 1995) as organizations equip the
employees with tools to allow them to apply their expertise. The notion of self-control
was noted in the field interviews. Value Shops had a higher degree of pay for
performance when compared to Value Chains and Value Networks. In Value Shops the
degree of task uncertainty is believed to be higher and the interdependence more complex
compared to Value Chains and therefore a reliance personnel and informal controls is
warranted (Abernethy and Brownell, 1997; Widener, 2004; Macintosh and Daft, 1987).
Firms will invest more in personnel controls if employees are strategically important to
the firm (Widener, 2004). Case study evidence suggested that Value Shops place
20
emphasis on a range of personnel controls (employee selection, employee training and
mission and vision statements). Interviewees discussed these controls with emphatic
language (huge, critical, needs to be top quartile) and expressed that the employees were
closely associated to the reputation of their organization. Interviewees discussed the
importance of the skills and knowledge of the employees individually as well as the
aggregate knowledge of their organization. Consistent with the knowledge management
literature (Mouritsen and Larsen, 2005; Coff et al., 2006) interviewees highlighted the
importance of building processes and knowledge databases to enable employees to
perform better.
Prediction 6: Value Shops will emphasize pay for performance more than Value
Chains and Value Networks.
Prediction 7: Value Shops will emphasize employee selection more than Value
Chains and Value Networks.
Prediction 8: Value Shops will emphasize employee training more than Value
Chains and Value Networks.
Prediction 9: Value Shops will emphasize vision and mission statements more
than Value Chains and Value Networks.
Prediction 10: Value Shops will emphasize knowledge management more than
Value Chains and Value Networks.
4.3 Value Networks
Value Networks were argued by Stabell and Fjeldstad (1998) to align to an
Administrative Adhocracy design (Mintzberg, 1979) and their components the operating
21
core and administrative function. As the operating core becomes similar to the machine
bureaucracy (Mintzberg, 1979) we expect the MCS in the operating core to be similar to
the Value Chain controls. Field interviews supported this premise. Value Network
interviewees’ descriptions of the MCS as they pertained to the network, were similar to
Value Chain descriptions. Tight usage of budgets, operating performance measures and
action controls were emphasized as being important to operating the network. The
administrative adhocracy has an administrative component where employees act as
problem solvers (Mintzberg, 1979). Field interviews supported this premise as well.
Discussion with Value Network interviewees described MCS in the administrative
function to be similar to Value Shop interviewees. Value Network interviewees were
very specific to describe the context of the MCS as either pertaining to the network
(operating core) or the “strategic side” (administrative function) of the business.
Therefore, we do not expect any of the MCS in Value Networks to be emphasized more
than Value Chains or Value Shops.
5. Research and Questionnaire Design
The field interviews and theoretical discussion provided a foundation to formulate
predictions as to the potential differences in MCS across the value logics as outlined in
section 4. In this sense, triangulation helped validate the research program (Campbell
and Fiske, 1959) building on the findings of the theoretical discussion and case study
findings (Nachmias and Nachmias, 1987 p. 207; Silverman, 2003, p. 233). The
questionnaire was administered to firms within a representative value logic industry. The
choice of industry was chosen following a process of Snow and Hambrick (1980)
22
including a review of common example industries in the value logic literature
(Thompson, 1967; Stabell and Fjeldstad, 1998; Sheehan et al., 2005; Fjeldstad and
Haanaes, 2001). For example banks were recognized by all scholars in the literature as
an example of a value network firm. Publicly available information, reviews by other
academics and inclusion of a validation question in the questionnaire were used to
enhance external validity. The validation question used Stabell and Fjeldstad (1998)
definitions to ask the respondent to classify their organization’s value logic. Associations
within the industries were utilized to facilitate contact with the individual firms.
Meetings were held with senior executives to explain the questionnaire and invite
participation within their organization. Care was taken to ensure that the invite to
participate in the survey would only go to senior management within their organization.
Response rates were high within the organizations, with the lowest participation rate
being above 80%.
Similar to the case studies the unit of analysis under investigation is the firm. The firm
level is argued to be appropriate choice since the basic argument of the value logic is that
firms could be configured differently than the logic expressed by Porter’s (1985) Value
Chain. For the questionnaire, multiple respondents completed the questionnaire within
each organization and the respondents’ answers within each organization were averaged
to obtain a mean score for the organization. By averaging the respondents’ answers
within the firm, common source bias was also reduced (Podsakoff et al., 2003). Since the
value logic typology had not been empirically validated, it was not feasible to administer
a large scale survey to randomized firms. The final data set consisted of 27 respondents
23
from 3 mining companies (Value Chain Category), 35 respondents from 3 International
Accounting Firms (Value Shop Category) and 30 respondents from 6 Financial
Institutions (Value Network Category), giving a total of 12 firms to form the sample.
The questions used in the questionnaire were developed from the theoretical discussion
and the field interviews. There is a recognized trade-off between reliability and construct
validity (Smith, 2003; Silverman, 2001) that applies to the issue of choosing between
established questions or creating new questions. Although many of the MCS have been
researched before, they have not been researched in the context of the value logics.
Further, there were ten MCS components identified in the theoretical discussion and case
studies that may differ in the value logics. Although researchers have recognized the
need for a broader perspective of MCS (Otley, 1980; Chapman, 1997; Langfield-Smith,
1997; Chenhall, 2003), there is not a validated instrument that incorporates the breadth of
MCS in the context of the value logics, so a new instrument was designed. The essence
of the survey questions can be found in Tables 3 and 6.
5.1 Value Logics – The Predictor Variable
In this research, firms were categorized into three value logic categories (the predictor
variable) based on the firm selection process as outlined in Section 3. Therefore the
common rater effect (Podsakoff et al., 2003) was somewhat mitigated in that respondents
were pre-assigned to a value logic category based on which firm they worked in. In
addition, respondents were presented a brief paragraph and asked to select which
business model best described their firm. The paragraphs were a summary of the Value
24
Logic definitions described by Stabell and Fjeldstad (1998). The results confirmed that
the categories assigned were appropriate.
5.2 Management Control Systems
Management Control Systems – Design and Use
Researchers have investigated MCS under different categorizations (Tucker and Parker,
2013) such as the categories of design (Ittner and Larcker, 1998; Moores and Yuen, 2001;
Auzair and Langfield-Smith, 2005) and use (Baines and Langfield-Smith, 2003; Henri,
2006; Widener, 2007). Design of MCS is generally associated with the existence of MCS
(Tucker et al., 2009; Langfield-Smith, 1997, 2005). For example, did an organization
have a budget or not. Use of MCS is generally associated with how the controls are used
(Tucker et al., 2009; Langfield-Smith, 1997; 2005). For example, did an organization
emphasize an MCS by utilizing it in a “tight” manner or if the MCS existed but was used
differently. The design and use categories were apparent in the interviews conducted.
From a design perspective the MCS investigated according to the Merchant framework in
the interview guideline “existed” in all of the firms interviewed with the exception of a
law firm that did not have a budget. How the MCS was used differed across the value
logic firms primarily in two different dimensions. Interviewees described the use of
MCS in terms of emphasis. However, interviewees also explained use in terms of the
MCS characteristics. For example in Value Chains, standard operating procedures were
used to ensure employees followed a scripted task to produce an output. Alternatively,
Value Shops use standard operating procedures to provide general methodologies and
best practices to support employees’ problem solving capabilities. In this sense the MCS
25
is used differently and more appropriately explained as having a different MCS
characteristic. Therefore MCS is categorized on two dimensions: emphasis and
characteristics. The following discussion relates to the analysis to develop the emphasis
constructs. For the MCS that included multiple questions related to emphasis, factor
analysis was performed for three related purposes: to investigate how many latent
variables underlie a set of items, to explain variation among a set of variables and to
define the substantive content or meaning of the subsets (DeVellis, 2003). Table 3,
presents the factor analysis of the MCS emphasis constructs that included multiple items
(questions).
Insert Table 3 Here
The factor extraction technique used in the Table 3 was principal component analysis
(PCA), with the first principal component, shown in Table 3, extracting the most variance
(Tabachnick and Fidell, 2007). Factor analysis was performed and rotated using the
oblique method, which allows for factors to be correlated (DeVellis, 2003, p. 125). For
each variable, the explained variance for the first factor was greater than 50% (range of
55% in SupervScale to 86% in MisVisScale) with eigenvalues greater than 1 (range of
1.65 for SupervScale to 4.31 for MisVisScale). All other factors had eigenvalues less
than 1, which provides support for context validity of the MCS emphasis variables.
Convergent validity is supported in that multiple items loaded onto the factor in access of
.5 (Bagozzi and Yi, 1988). A Cronbach Alpha (Cronbach, 1951) was used as the
coefficient of reliability for testing internal consistency of the variable. The level
considered “acceptable” is generally between .50-.60 for instruments being used for the
first time. In addition to the MCS emphasis variables described in Table 3, there were
26
four other MCS emphasis variables. Two of them, pay for performance and employee
section were measured by two questions each. The remaining two MCS emphasis
variables, security and limitation of access and employee training were measured through
a single question. Although the single question may be argued to be having less validity
and reliability, they are kept for exploratory investigation. As a result, ten MCS
emphasis variables are used in the analysis. For the variables with more than one item, a
composite score was achieved through averaging the scores of each item (question) in the
factor. The emphasis variable was renamed as indicated in Table 4, which reports the
intercorrelations among the constructs. Despite the non-orthogonal rotation, as this table
shows, the dimensions were largely independent (Bagozzi and Yi, 1991).
Insert Table 4 Here
6. Results
6.1 MCS Emphasis Constructs
Table 5 reports the descriptive statistics, ANOVA and planned comparison results for
MCS emphasis constructs by value logic type (Value Shop, Value Chain and Value
Network). As described previously the respondents within each firm were averaged so
that the unit of analysis is the firm, resulting in N = 12. The mean and standard deviation
are reported with the theoretical ranges for all constructs being 1 (low) and 7 (high).
Within each MCS emphasis construct, the value logic with the highest mean is bolded.
One of the notable differences in the descriptive statistics is the polarization between
Value Chains and Value Shops. The descriptive statistics in Table 5 show that for nine of
the ten MCS emphasis constructs, Value Chains or Value Shops had the highest mean.
Further, in six emphasis constructs, the pattern was that Value Shops (Value Chains) had
27
the highest mean whereas the Value Chains (Value Shops) had the lowest, suggesting
polarization between Value Shops and Value Chains for the emphasis constructs. This is
consistent with the arguments presented in the theoretical discussion and case studies.
The descriptive statistics illustrated that the means were highest as predicted in seven out
of nine prediction statements. The only MCS emphasis constructs that were not
supported, descriptively were standard operating procedures and mission and vision.
Therefore Predictions 3 and 9 are not supported.
Insert Table 5 Here
Table 5 also reports the results of the ANOVA which was performed to test if the MCS
emphasis constructs means were statistically different across the value logics. The results
show that four of the MCS emphasis construct means were statistically different to
varying degrees. The strongest significant difference in means across the value logics
was with the KnowMgmtScale construct (F = 17.536, p<.001) and EmpSelScale
construct (F = 8.89, p=.007). Supervision (SupervScale) was statistically different (F =
3.27, p=0.085) as well as and budgets (F = 4.05, p=0.056).
To test the predictions of the MCS emphasis constructs, a planned comparisons analysis
is performed for the constructs that were statistically different (Tabachnick and Fidell,
2007). The predictions are used to formulate the weighted coefficients for comparisons.
For example in the BudgetScale construct, Value Chains are predicted to emphasize the
construct more than Value Shops and Value Networks. Thus Value Chains are assigned
a weighted coefficient of 1 and Value Shops and Value Networks are assigned a weight
of -0.5.
28
For the budget emphasis (BudgetScale) and supervision emphasis construct
(SupervScale), Value Chains were predicted to emphasize budgets more than Value
Shops and Value Networks. The planned comparison test reported that the mean for
Value Chains was significantly higher compared to Value Shops and Value Chains (t(9)=
6.040, p<.001) for BudgetScale and therefore Prediction 1 is supported. However, for
SuperScale the planned comparisons test reported that the difference was not significant
(t(9)= 1.190, p=.265) and therefore Prediction 4 is not supported. Value Shops were
predicted to emphasize employee selection (EmpSelScale) and knowledge management
(KnowMgmtScale) more than Value Chains and Value Networks (Predictions 7 and 10
respectively). These predictions were both supported in the planned comparison tests for
employee selection (t(9)= 4.165, p=.002) and knowledge management (t(9)= 4.993,
p<.001). The remainder of the prediction statements were not supported because there
was not a statistical difference between the means.
6.2 Management Control System Characteristics
In addition to the MCS emphasis constructs twenty-six questions were asked related to
the characteristics of MCS. A similar pattern to the MCS emphasis constructs was
observed. In twenty-one of the twenty-six questions asked, Value Chains or Value Shops
had the highest mean. Table 6 reports the descriptive and the ANOVA results for the
MCS characteristic questions that were statistically different. Although predictions were
not formally developed for MCS characteristics, for consistency a planned comparison
test was performed with the prediction equation developed from a review of the Value
Logic means.
29
Table 6 reports that statistical differences were observed across six MCS suggesting the
differences in the Value Logic MCS occur not only through emphasis but through the
manner in which the MCS is used. In all cases the characteristics findings support the
case study evidence or theoretical discussion. Value Shop interviewees gave examples of
budgeting at a higher level and more focused on forecasting future revenue or “pipeline”,
the term that several Value Shop interviewees used. This characteristic (emphasize
revenue budget/forecast versus cost budget/forecast) was statistically significant for
Value Shops compared to Value Chains and Value Networks (t(9)= 4.714, p=.002).
Standard operating procedures in Value Shops are characterized by methodologies, best
practices and experience (t(9)= 6.076, p=.002) whereas Value Chains and Value Network
may rely more on prescriptive operating procedures. Value Shop employees use general
procedural guidance and may rely more on their tacit knowledge (Mouritsen and Larsen,
2005) to solve problems as the Value Shops are configured to deal with unique cases
(Stabell and Fjeldstad, 1998). With respect to knowledge management, capturing data on
the customer and utilizing technology are more important to Value Shops and Value
Networks than Value Chains (t(9)= 8.442 p=.098). Additionally this research reported
statistical significance with the MCS characteristic, “professional designation important
aspect of employee selection” (t(9)= 4.604, p<.001). The Abernethy and Brownell (1997)
research operationalizes personnel controls through level of professionalism (Hage and
Aiken, 1967) suggesting that Value Shops may use professional designations as a key
characteristic of how employee selection is used in management control. Security and
limitation of access was found not to be emphasized differently across the value logics.
30
However, consistent with the case studies, the focus is different for each Value Logic.
For Value Chains the focus is on safety and securing assets (t(9)= 6.907, p=.002) and for
Value Shops and Value Networks the focus was more on data and technology (t(9)= 4.572
p=.005). In the case studies, Value Chain managers described a MCS environment where
it was difficult to cascade corporate goals and objectives to the individual employee and
therefore found it difficult to develop individual performance measures for many
operational employees. This characteristic was observed as the mean for “organizational
performance measures can easily translate into individual measures” was statistically
lower (t(9)= 2.326, p=.064) for Value Chains.
Insert Table 6 Here
7. Discussion
The central argument and contribution of this research is that the value logic represents a
new and fundamental contingent variable, previously not studied in the strategy and MCS
domain. Value logic is related to strategy, but differs from it in that value logic represents
a business model within which a variety of strategies can be developed. Our preliminary
evidence suggests that differences do exist and the value logic has potential to be an
important variable explaining differences in MCS between firms, and of use to both
academics and practitioners.
The value logic offers an alternative typology that focuses on the underlying value
creation or business model. Given the shift in the economy towards more knowledge
based and network or internet based firms, the typology offers a perspective of strategy
useful to understanding MCS. The value logic concept also provides an insight that may
31
be useful to researchers investigating strategic change. Whether the strategic change is
intended or emergent firms may undergo fundamental business model changes such as a
Value Chain becoming more Value Shop or Value Network oriented in order to compete.
Understanding the differences in MCS across the value logics is useful for practitioners
to understand the design and use of MCS in order to execute their strategy.
This study reinforces a broader definition of MCS as differences in MCS were noted
across results, action and personnel controls. Differences were noted in both the
emphasis constructs and individual characteristics. This finding highlights the
complexity and importance of how firms “use” MCS and suggests the lack of MCS
broadness critique (Chenhall, 2003; Langfield-Smith, 1997; Merchant and Otley, 2007;
Ferreira and Otley, 2005) may warrant a MCS control package perspective through the
lens of the value logic configuration. For Value Chain firms, budgets were emphasized
more than in Value Shops and Value Networks. This finding was evident in the case
studies as Value Chain managers described the importance of budgeting to their
organization and provided numerous example of the “tightness” (multiple review
processes for budget approvals, frequency of reviewing the budget and the level of detail
within the budget). The questionnaire results also found statistical differences in the
budget emphasis construct. In this case budget “use” is emphasized more in Value
Chains. However differences were also found at the characteristic level in the case
studies and questionnaire. In the case studies, Value Chain managers provided evidence
that it was difficult to cascade corporate goals and objectives into individual performance
measures, although several organizations had tried but failed. Value Shop managers
32
described a different experience noting that their employees clearly understood their
individual targets and that they could see the alignment to the firm objective and goals.
The differences in MCS for Value Shops were noted in the personnel controls
particularly through employee selection and knowledge management as evidenced in the
questionnaire statistical findings and case studies.
Value Shops may have some well-
established techniques for performing tasks (Stabell and Fjeldstad, 1998), such as
information acquisition procedures, but overall the problem solving nature of the Value
Shop configuration is cyclical, iterative and complex. Consistent with previous
contingency research accounting forms of control are not well suited when tasks are more
complex or uncertain (Hirst, 1983; Brownell and Hirst, 1986; Brownell and Dunk, 1991).
The questionnaire found that Value Shops emphasize employee selection to recruit
problem solvers who can apply their expertise, providing further evidence to the case
studies where Value Shop Managers’ assessment of employee selection was “huge”,
“critical” and “lots of emphasis”. These findings are also consistent with MCS research
that shows employee selection is emphasized in firms that emphasize human capital
(Widener, 2004). Knowledge management was also emphasized more in Value Shop
firms compared to Value Chains and Value Networks. In the case studies Value Shop
managers discussed the importance of equipping their employees with tools and
knowledge to allow them to utilize their professional skills. Rather than scripted standard
operating procedures described in Value Chains, the standard operating procedures were
characterized as methodologies, best practice and experience in Value Shops. The case
studies further described a control environment of equipping the experts to do their job
33
and then paying them based on their ability to perform the outcomes of their work. The
differences in MCS noted within the Value Shops, creates a context of multiple controls
(Value Shop controls) working together for the purpose, according to the Value Logic
typology, of equipping the employees to problem solve. This is consistent with the
notion of MCS working as compliments and substitutes (Gerdin, 2005; Fisher, 1995;
Otley, 1980) as well as control packages (Chenhall et al., 2011; Sandelin, 2008; Malmi
and Brown, 2008; Ittner and Larcker, 1997).
The Value Network MCS were not as distinct or polarized compared to Value Chains and
Value Shops. The evidence from the case studies and statistical results of the
questionnaire suggest the notion of Stabell and Fjeldstad’s (1998) concept that Value
Network’s follow conform to an Administrative Adhocracy (Mintzberg, 1979).
The key structure of the Administrative Adhocracy is that two components exist, the
operating core and the administrative function. The operating core is characterized by
high degrees of standardization and Mintzberg (1979) suggests that the operating core
becomes similar to the Machine Bureaucracy design. In the case studies, Value Network
managers described MCS similar to Value Chains when referring to their network
infrastructure (operating core). Similarly Value Network managers described MCS
similar to Value Shops when discussing “the strategic” side of the business (the
administrative function). For example Value Network managers described their use of
performance measures for their network as being very precise, detailed and constantly
monitored similar to the discussion with Value Chain managers. Value Network
managers also gave examples of emphasizing employee selection to the extent that it
34
enabled their organization to achieve a competitive advantage. The context of this
discussion was not for employees employed in the operational aspects of the network
infrastructure, but for employees employed on the “strategic side” of the business.
Having two operating contexts that were sufficiently different within one organization
would mean contingency researchers may need to assess the effect the contingent
variable has on each operating context. For example the general proposition that
uncertain environments lead to more open and externally focused MCS (Chenhall, 2003)
may apply differently to each operating context.
Insert Table 7 Here
The evidence from this study indicates that value logic is an important explanatory
variable for several aspects of the design and use of MCS. Rather than being yet another
typology of business unit strategy, it represents a logically prior variable characterizing
the business model adopted by an organization. Several alternative strategies are possible
within the chosen business model. Future work on the strategy-MCS linkage might well
benefit from examining these linkages within each value logic, rather than aggregating
data across value logics, which is the approach implicitly taken by previous studies. Such
an analysis would likely show stronger results than the rather confusing patterns currently
reported.
Limitations of the study are primarily caused by the exploratory nature of the research
topic. Case studies were utilized to uncover knowledge of a potentially new contingent
variable, the value logic and the potential differences in MCS across the Value Logics.
To extend our knowledge of the value logic phenomenon, a questionnaire was utilized
35
where the questions were formulated from the knowledge of the theoretical discussion
and case studies. Further, the value logic typology is to date conceptual and does not
have the empirical evidence that other well-known strategic typologies have. However,
the significant contribution of this research is to introduce a new variable with the
potential to inform future research that adopts alternative research approaches in the
strategy – MCS research domain.
This research can be extended in several directions. Validating the value logic typology
will be important to promote larger scale surveys. Similarly, this research identified
differences in MCS across the value logics. Research could be performed utilizing
empirically tested MCS scales to further validate the research findings. Research
utilizing a value logic framework would be informative to understand strategic change
from the perspective of what happens to the MCS as a firm changes its business model
(e.g. from a Value Chain to a Value Shop). Based on evidence form the Value Network
firms, this research suggested that two distinct operating environments may exist in the
Value Network, each with its own MCS. This research adopted a congruent approach of
fit to locate the research in the contingency literature given the early stages of the
research. Adding performance as a variable and developing more complex contingency
models is an area of future research. This paper is a first step to introducing a new
explanatory variable in the strategic typology – MCS domain. Additional empirical
evidence and theoretical concepts are required to more fully understand the value logics
and MCS relationship.
36
Table 1: Summary of Value Logics
Value Chains
Value Shops
Value Networks
Primary Technology
Long-linked
Intensive
Mediating
Value Created by
Transforming inputs
into standardized
products and services
Firm is part of an
industrial value chain
Solving customers
problems
Linking customers
Firm belongs to a
system of referred
shops
Firm belongs to a system
of layered and
interconnected networks
Sequential
 Major driver of cost
is scale and capacity
utilization
Cyclical, spiraling
 Information gap
exists between
customer and
firm/employee
Simultaneous, parallel
 Establishes, monitors
and terminates
relationships amongst
members
 Employees are
experts and can
leverage specialists
 Charge for accessing or
activity on the network
Industry Context
Interactivity
Key Characteristics
 Process bias by
focusing on
improving the flow of
goods/services
through the firm.
 Standardization is
important
 Knowledge of
employees and firm
lead to referrals and
reputation
 Strive to increase
number of customers or
profitability of each
customer on network
37
Table 2: Management Control Systems Conceptualized By Value Logic
Results Controls
 Budgets (Brownell and Dunk 1991:
Lau, Low and Eggleton 1995)
 Accounting Performance Measures
(Hirst 1983)










Action Controls
Standard Operating Procedures,
programs, plans (Daft and
Macintosh, 1981)
Rules, procedures, PERT (Rockness
and Shields 1984)
Personnel Controls
Social controls (Rockness and
Shields 1984)
Personnel controls (Abernethy and
Brownell 1977, Widener 2004)
External, non-financial and future
oriented information (Mia and
Chenhall 1994)
Informal Communication
(Macintosh and Daft 1987)
Frequent interaction with managers
and superiors (Williams et al. 1990)
Face to face communication (Teece
1977)
Employee Selection (Widener 2004)
Knowledge Management (Coff
1977,Kogut and Zander 1995,
Mouritsen and Larsen 2005)
Value Chain
Value Shop
Value Network
High
Low
Medium
High
Low
Medium
High
Low
Medium
High
Low
Medium
Low
High
Medium
Low
High
Medium
Low
High
Medium
Low
High
Medium
Low
High
Medium
Low
High
Medium
Low
Low
High
High
Medium
Medium
38
Table 3: Factor Analysis of Management Control System Emphasis Constructs
Emphasis
Variable
Name of
Variable
Summarized Item in Questionnaire (Q)
Survey
Budget
Budget
Scale
 the budget holds people
accountable
 performance evaluations tied to
meeting budget
 budget process involves many
approval levels
 budgets are detailed and pervasive
 achieving (or not) performance
target leads to reward or punishment
 for every metric we have there is an
associated target
 performance measures are objective,
precise and timely
 we provide training on policies and
procedures
 checklists used to ensure policies
and procedures are followed
 policies and procedures detailed,
precise, well-documented
 compliance checking to ensure
procedures are being followed
 new employee needs good
understanding of policies &
procedures
 supervisors important to ensure that
sop are followed
 daily work of our employees is
heavily supervised
 nature of work changes, supervision
varies substantially
 employees know and understand
mission and vision
 communicating mission and vision
is important
 we constantly reinforcing our
mission and vision
 employees value mission and vision
 see linkages between mission and
vision and how employees perform
Performance
Measurement
Standard
Operating
Procedures
Supervision
Mission and
Vision
Knowledge
Management
PerfMeas
Scale
SOPScale
Superv
Scale
MisVis
Scale
Know
Mgmt
Scale
 employee's performance
impaired without knowledge
management
 heavy investment in knowledge
management
 machinery or network upgrades
more important than knowledge
management
Loading
on First
Factor
Variance
Explained
Eigen
value
Cronbach
Alpha
0.81
0.55
2.22
0.72
0.62
1.87
0.63
0.66
3.32
0.86
0.55
1.65
0.54
0.86
4.31
0.96
0.77
3.08
0.90
0.77
0.71
0.68
0.89
0.65
0.79
0.87
0.85
0.81
0.80
0.75
0.88
0.76
0.54
0.94
0.94
0.92
0.93
0.92
0.93
0.94
0.81
39
Table 4: Intercorrelations Amongst Scales
Scale
BudgetScale (1)
Perf|MeasScale (2)
PayPerfScale (3)
SecurityScale (4)
SOPScale (5)
SupervScale (6)
EmpSelScale (7)
EmpTrainScale (8)
MisVisScale (9)
KnowMgmtScale (10)
1
2
3
4
5
—
.077
.080
.034
-.007
.124
.008
-.047
.132
.020
—
.038
-.086
-.076
.134
-.015
-.032
-.020
-.102
—
-.014
-.047
-.004
-.055
-.007
.024
-.049
—
.060
-.015
.036
-.033
.038
-.083
—
-.055
.011
-.058
-.017
.091
6
7
8
9
—
-.008 —
.005 -.092 —
-.009 .004 -.019 —
.083 .022 -.034 .023
10
—
40
Table 5: Descriptive Statistics, ANOVA and Planned Comparison Results
Value Shop
3
4.61
Std.
Dev.
0.18
Value Chain
3
5.52
0.10
Value Network
6
4.65
0.61
Total
Value Shop
Value Chain
Value Network
Total
Value Shop
Value Chain
Value Network
Total
Value Shop
12
3
3
6
12
3
3
6
12
3
4.86
4.75
5.43
4.92
5.01
6.14
5.60
5.37
5.62
5.20
0.58
0.22
0.33
0.82
0.64
0.40
0.05
1.30
0.96
0.71
Value Chain
Value Network
Total
Value Shop
Value Chain
Value Network
Total
Value Shop
Value Chain
Value Network
Total
Value Shop
Value Chain
Value Network
Total
Value Shop
Value Chain
Value Network
Total
Value Shop
Value Chain
Value Network
Total
Value Shop
3
6
12
3
3
6
12
3
3
6
12
3
3
6
12
3
3
6
12
3
3
6
12
3
5.24
4.53
4.88
5.53
4.86
5.44
5.32
5.15
5.19
4.49
4.83
4.35
4.00
4.18
4.18
6.03
4.79
5.84
5.63
4.49
3.45
4.73
4.35
4.53
0.13
0.49
0.60
0.29
0.31
0.44
0.45
0.66
0.08
0.46
0.55
0.32
0.37
0.21
0.28
0.32
0.83
1.09
0.97
0.27
1.87
0.81
1.12
0.31
Value Chain
3
3.50
0.38
Value Network
6
4.47
0.47
12
4.24
0.59
N
BudgetScale
(Prediction 1)
PerfMeasScale
(Prediction 2)
PayPerfScale
(Prediction 6)
SecurityScale
(Prediction 5)
SOPScale
(Prediction 3)
SupervScale
(Prediction 4)
EmpSelScale
(Prediction 7)
EmpTrainScale
(Prediction 8)
MisVisScale
(Prediction 9)
KnowMgmtScale
(Prediction 10)
Total
* p<.01; **p<.05; ***p<.10
Mean
F
4.046
Planned
Comparison Test
C < (S+N)
Sig.
.056**
t(9)= 6.040
p< .001*
0.958
.420
0.619
.560
1.704
.242
2.894
.107
C < (S+N)
3.274
.085***
8.891
.007*
1.699
.237
1.455
.284
t(9)= 1.190
p=.265
S > (C+N)
t(9)= 4.165
p=.002*
S > (C+N)
17.536
<.001*
t(9)= 4.993
p<.001*
41
Table 6: ANOVA and Planned Comparison Results for MCS Characteristic Questions
Budgeting: Emphasize
revenue budget/forecast
versus cost
budget/forecast
Performance Measures:
Organizational
performance measures
can easily translate into
individual measures
Security and Limitation
of Access: Focus on
safety and securing
assets
Security and Limitation
of Access: Focus on
securing data and
technology
SOP: Employees rely
on methodologies, best
practice and experience
SOP: Integrated SOP
into larger programs
such a quality and
safety
N
Mean
Std.
Deviation
Shop
Chain
Network
Total
Shop
Chain
Network
Total
3
3
6
12
3
3
6
5.24
3.26
2.64
3.45
4.65
3.66
4.82
0.40
1.05
1.48
1.57
0.05
0.06
0.83
12
4.49
0.76
Shop
Chain
Network
Total
Shop
Chain
Network
Shop
3
3
6
12
3
3
6
3.69
6.24
4.96
4.96
6.18
4.60
6.37
0.80
0.45
1.08
1.25
0.25
0.75
0.37
3
6.18
0.25
Shop
Chain
Network
Total
Shop
Chain
Network
Total
Shop
Chain
Network
Total
3
3
6
12
3
3
6
12
3
3
6
4.74
3.60
3.32
3.74
5.16
5.36
3.32
4.29
6.43
4.81
4.20
0.11
0.56
0.58
0.77
0.50
0.39
0.86
1.20
0.40
0.05
0.79
12
4.91
1.11
3
3
6
4.55
2.59
4.42
0.72
0.56
0.92
12
3.99
1.12
Employee Selection:
Professional
designation important
aspect of employee
selection
Knowledge Mgt:
Shop
Focus on capturing key Chain
information on the
Network
customer – utilizing
Total
technology
* p<.01; **p<.05; ***p<.10
F
Sig.
4.564
.043**
Planned
Comparison
Test
S > (C+N)
t(9)= 4.714
p= .002*
3.623
.070**
C < (S+N)
t(9)= 2.326
p= .064***
C > (S+N)
5.859
.023**
t(9)= 6.907
p = .002*
C < (S+N)
15.535
.002*
7.916
.011**
11.389
.003*
t(9)= 4.572
p=.005*
S > (C+N)
t(9)= 6.076
p= .002*
N < (S+C)
t(9)= 2.352
p= .043**
S > (C+N)
13.083
.002*
t(9)= 4.604
p< .001*
C < (S+N)
6.053
.022**
t(9)= 8.442
p=.098***
42
Table 7: Summary of Statistical MCS Differences
Value Logic



Value Chain






Value Shop




Value Network

Management Control System
MCS exist in Value Chains across a broad spectrum (results,
action and personnel controls)
Value Chains emphasize budgets by using them in a tight
manner
Organizational performance measures are used in Value
Chains but Value Chains can’t easily translate organizational
performance measures into individual employee measures
Security and limitation of access is used for the safety of
employees and to secure the assets of the Value Chain
Larger programs such as safety and quality are used to
integrate standard operating procedures
MCS exist in Value Shops across a broad spectrum (results,
action and personnel controls)
Employee selection is emphasized
Knowledge management is emphasized
Budgeting is used in Value Shops, but the orientation of
budgeting is towards revenue and not costs
Security and limitation of access used in Value Shops but the
focus is on security of data and technology
Professional designations are an important aspect of employee
selection
Standard operating procedures are expressed through
employees reliance on methodologies, best practices and
experience
MCS exist in Value Networks across a broad spectrum (results,
action and personnel controls)
The structure of the Value Networks is theorized to result in
MCS used in different perspectives: The operating core has
MCS use similar to the Value Chains whereas the
administrative activities has MCS use similar to Value Shops.
43
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