Business Analytics Strategy: Social, Mobile, and Cloud Computing
The Impact of Business Analytics Strategy on
Social, Mobile, and Cloud Computing
Adoption
Research-in-Progress
Abhijeet Ghoshal
College of Business, University of
Illinois-Urbana Champaign
1206 S. Sixth St., Champaign, IL
[email protected]
Eric C. Larson
College of Business, University of
Illinois Urbana-Champaign
1206 S. Sixth St., Champaign, IL
[email protected]
Ramanath Subramanyam
College of Business, University of
Illinois-Urbana Champaign
1206 S. Sixth St., Champaign, IL
[email protected]
Michael J. Shaw
College of Business, University of
Illinois Urbana-Champaign
1206 S. Sixth St., Champaign, IL
[email protected]
Abstract
Information Technology is widely accepted as a catalyst for firms to develop new
capabilities. Recently, technologies that can harness the potential of big data have
gained traction among firms. Social, mobile, and cloud computing (SMC) technologies
have been widely recognized as technologies to collect and use big data. We focus on
adoption of these technologies in this research. We construct a model in which four
proposed antecedents: internal demands, market demands, technological skills
shortage and information security concerns, influence the adoption of SMC
technologies. We develop a framework to classify firms based on their strategic
objectives vis-à-vis use of business analytics which we call business analytics strategy
(BAS). Different BAS profiles may influence differently the antecedents to adoption of
SMC technologies.
Keywords: Big Data, Business Analytics, Technology Adoption, Organizational Innovation
Introduction
The last two decades have borne witness to drastic decreases in hardware costs, significant increases in
storage capacities, an exponential growth in processing power, and increased adoption of miniature
computing devices like wearable smart devices, smartphones and tablets. Together, this technology influx
and ubiquity of mobile devices have contributed to the high velocity generation of massive amounts, and
varied kinds of data. Not surprisingly, companies are increasingly adopting technologies that generate
and process “big data” to boost profits and increase their efficiency (e.g., Kiron, Prentice and Ferguson
2014; Laney and Buytendijk 2013).
Through various “big data” efforts, i.e., harnessing and exploring the massive data repositories for
business insights, firms create niche positions for themselves in the market. For instance, Davenport and
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IS Strategy, Structure and Organizational Impacts
Kim (2013) highlight the use of big data in searching for new market opportunities, developing new
products and marketing those products to potential customers. In fact, for some companies, big data is at
the heart of their business models: page rank at Google, “people whom you may know” at LinkedIn, or
new games at Zynga. For these firms, data analytics is critical for their survival and adaptation to dynamic
market conditions. Even traditional firms now recognize the immense value of using big data for
understanding their customers and better personalizing the customer experience, monitoring internal
organization performance and improving supply chain efficiency. Together, these initiatives help firms
remain competitive in increasingly dynamic marketplaces.
The adoption of Big Data is primarily driven by three capabilities that firms seek to develop – analysis of
external environment, analysis of internal process, and decision making. In order to understand how Big
Data help firms in developing these capabilities we use the theoretical framework of Technological
Adoption provided by Tornatzky and Fleicher (1990). This framework suggests that there are three
elements that influence the process of technological innovation – organizational context, technological
context, and environmental context (OET). It is recognized in the literature that although OET framework
provides a general understanding about technological innovation, studies in different contexts are
necessary in order to understand how a specific technology related factors influence adoption of the
technology in an organization. Specifically, big data is used by a firm to support its larger business
strategy through use of business analytics tools and techniques for improving processes related to the
three contexts. Since big data is now crucial to the success of firms, a thorough scientific study is required
in order to properly analyze the factors affecting big data adoption (Mcafee and Brynjolfsson 2012). In
this research, we use the adoption of social, mobile and cloud computing (SMC) as an outcome measure
since SMC technologies are recognized as the most important technologies that contribute to the growth
and analysis of big data (Howard and Plummer 2013, Srikanth 2013).
The decision to adopt such technologies is not straightforward. For instance, multitudes of technologies
are available that complement other organizational or technological capabilities. Further, certain
technologies or organizational capabilities are interdependent, i.e., to extract the full benefit of one
technology, other related technologies are necessary to be in place (Leonardi 2013). The use of these
technologies is primarily driven by the firm level business strategies. Based on the strategic goals of a
firm, the firm develops its own business analytics strategy (BAS) – the intention or position the firm takes
regarding the analysis of data and use of the insights from that data to advance specific organizational
objectives. However, the relationship between business analytics strategy and adoption of SMC
technologies is influenced through various firm level antecedents of adoption of SMC technologies. On
one hand, firms may be motivated to adapt SMC to understand external customers and improve internal
efficiencies. On the other hand, problems regarding integrating these technologies, such as technical skill
shortages and uncertainties with the security aspects of the new technologies inhibit firms from adopting
them swiftly. Based on a firm’s business strategy, the firm may selectively focus on the technical enablers
or the organizational barriers which would determine their degree of adoption of the technologies.
Building on prior theory and employing data collected through a global corporate survey, we propose a
model that enables us to answer the question - how much influence endogenous strategic position and
exogenous drivers for technology adoption have on the degree of adoption of SMC technologies in
organizations.
Information Systems has a strong tradition of technology adoption research at the individual and group
level (e.g, Karahanna et al. 1999, Venkatesh and Davis, 2000, Sarker and Valacich 2010, Sun 2013).
Several of these past research on technology adoption within firms are closely related to this study. Meyer
and Goes (1988) examined the assimilation of innovations into organizations, a process unfolding in a
series of decisions to evaluate, adopt, and implement new technologies impacted by both the context and
the technology. Top management participation in the assimilation of enterprise systems in the postimplementation stage (Liang et. al. 2007) and the interaction of top management tasks with the
technology (Cooper and Zmud 1990) are critical for successful adoption. Bala and Venkatesh (2007)
characterized assimilation of new standard inter-organizational business processes as dependent on the
relationship with business partners, pressure to mimic competitors and institutional inertia. Swanson
(1994) proposed an extension of OET framework (Tornatzky and Fleicher 1990) to emphasize the
communication link between the IS organization and the business to drive adoption. However, none of
these studies explain the adoption of technology specifically relevant to the use of big data and the
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Business Analytics Strategy: Social, Mobile, and Cloud Computing
relationship between the antecedents of their adoption and the firm’s strategy related to the use of
Business Analytics.
In essence, our study aims to provide insights that will help managers at all levels of a firm, including Clevel executives and IT managers focused on big data initiatives. Leveraging big data for competitive
advantage requires significant investments in manpower and technology (Gartner 2013). Also, these
technological investments have a maturation period and therefore often require significant time for
benefit realization (Gooch 2013). Our well-timed study will provide important evidence regarding the
connections between a firm’s BAS, antecedents of adoption, and adoption of emerging technologies. Our
first contribution is to characterize the firm according to its BAS – a profile of the dominant strategic
objective for the firm’s use of big data. The second contribution is an understanding of the influence of
various antecedents of adoption of technology on the adoption of SMC in the context of the firm’s BAS.
Our analysis is divided into two parts. In the first stage, we analyze the data across more than 1,200
organizations to explore the business analytics strategies adopted by firms. This data exploration is
required to develop a concrete understanding of the strategies employed by organizations that uniquely
define the business analytics objectives of the firm. In the second stage, we develop hypotheses regarding
the relationship between various business analytics strategies and adoption of SMC mediated by various
firm level antecedents of SMC adoption. We acknowledge that there may be mediation, moderation or a
direct effect, although our preliminary analysis does not show any support for the moderation effect of the
four antecedents of adoption. Further analysis will examine this empirical question.
Characterizing Business Analytics Strategy
The OET framework suggests that technological innovation within firms is influenced through three
factors – organizational, environment, and technological. Firms perform business analytics on big data
for improving efficiencies along these three dimensions. Business Analytics is used by firms for managing
risks in decision making by using data and verifying the effects of various decisions on overall business. In
the context of environment, use of business analytics help firms in identifying new markets, understand
customer demands, and accelerate product development. Finally, in the context of technology, business
analytics help firms in reducing internal cost of production, improve employee productivity, and improve
internal supply chain performance.
A firm’s BAS is shaped from its higher-level business strategy. While business strategy deals with the
question – “how do we compete effectively in each of our chosen product-market segments?,”
(Venkatraman 1989), we conceptualize BAS as focusing on the analysis of the context of the firm through
the use of analytics tools and big data to support the larger organizational business strategy. A firm
performs three essential functions in order to run its business: external communications with customers
and society, governance for coordinating decision making, and internal operations. Corresponding to the
priorities across these three dimensions, a firm adopts a BAS aligned with its larger business strategy.
Miles and Snow (1978) proposed a typology for firms - Prospectors, Defenders, and Analyzers– based on
the business strategies adopted by firms, i.e. growth and innovation-oriented, cost efficiency driven, or a
hybrid approach. Researchers have extended (Doty et al. 1993, Delery and Doty 1996) and adapted this
characterization to consider IT strategy (Sabherwal and Chan 2001). Business Analytics is used by all
these types of firms, albeit in different ways. The firms put emphasis on using big data for understanding
the customer market, improving organizational governance, and/or driving efficiency of internal
operations to different degrees based on their higher level business strategies. These degrees of strategic
emphasis create various configurations of use of business analytics that form the basis of distinct profiles
based on which firms are segregated. Firms are expected to fall on a spectrum based on the use of
business analytics across the three domains. At one end of the spectrum should be a firm with a profile
“Analytics Leader” that is expected to use business analytics across all the three domains. On the other
end of the spectrum is a firm with a profile “Analytics Laggard” that is expected to lag in the use of
business analytics, lacking any concrete strategic objective for business analytics. As the needs for data
and their strategic value of such data is expected to vary dramatically based on the unique objectives of
each firm, the adoptions of SMC technologies is expected to be dependent on the BAS profile of each firm.
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Research Setting and Data
The data for this study comes from the 2012 IBM Business Analytics survey (IBM 2012). A questionnaire
was designed and executed by IBM to survey technology and business executive decision makers in 1,200
organizations across various industries. The survey contains questions designed to generate formative
constructs to measure each firm’s emphasis with respect to business analytics: (i) market sensing, (ii)
internal operations, and (iii) governance. We employ k-Means clustering to create the profiles of firms
based on their BAS and establish groups of companies pursuing similar business analytics strategies. If
each of the three dimensions is used to sort firms high or low on those dimensions, there are theoretically
eight (2x2x2) potential clusters defining BAS. As such, we explore values of k from two to eight, and find
support for the presence of five clusters that are intuitive and possess high face validity. Future work will
employ cluster analysis other than k-means in order to test the validity of the clusters.
In Table 1, we show the final cluster centers with average scores on each of the dimensions corresponding
to the three business analytics strategies. We have designated names for each of the clusters: Analytics
Leader, Market Responder, Organization Optimizer, Efficiency Seeker, and Analytics Laggard.
Table 1. Cluster Analysis: Final Cluster Centers
Business Analytics Strategy
Business Analytics Domain
1
2
3
4
5
Analytics
Market
Organization
Efficiency
Analytics
Leader
Responder
Optimizer
Seeker
Laggard
Market Sensing
2.57
2.22
.35
.46
.47
Governance
2.67
.83
2.27
.91
.53
Internal Operations
2.55
.91
.75
2.23
.51
As summarized in Table 1, aggressive analyzers called Analytics Leaders focus on exploiting business
analytics across all the three business domains (Cluster 1), Market Responders are dominant analyzers
who focus on using business analytics for market intelligence and for developing their relationships with
their customers (Cluster 2), Organization Optimizers, who focus on using business analytics for decision
making for governing the organization (Cluster 3), Efficiency Seekers who are internal operations
dominant analyzers seeking to use business analytics primarily for improving internal operational
efficiencies (Cluster 4), and Analytics Laggards who minimally use business analytics in any of the
business domains (Cluster 5). The smallest cluster has 96 firms (Analytics Leader) and the largest cluster
has 390 firms (Analytics Laggard), indicating the nascent opportunity in this context. Next, we develop
the hypotheses to relate these five business strategy profiles with adoption of SMC technologies.
Theory and Hypotheses
Our model suggests that firms will face different levels of the antecedents to adoption depending on their
BAS profile. For example, an Analytics Laggard may face higher or lower market demands than an
Analytics Leader. In Figure 1, we propose the model we test showing arrows from the five profiles to the
antecedents of adoption to indicate the relationships. The four antecedents of adoption are theorized to
influence SMC adoption.
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Business Analytics Strategy: Social, Mobile, and Cloud Computing
Change in IT
Investment
Internal
Demands
Market
Demands
Business Analytics Strategy
• Analytics Leader
• Market Responder
• Efficiency Seeker
• Organization Optimizer
• Analytics Laggard
H1b
+
H1a
+
Adoption of
Social, Mobile
and Cloud
Computing
H2a
-
Technological
Skills
Shortage
H2b
-
Information
Security
Concerns
Figure 1: Business Analytics Strategy, Antecedents of SMC Adoption and SMC Adoption
Antecedents of SMC Adoption
Digital technologies are seen as central to improving the operational efficiencies of organizations by
optimizing processes, increasing transaction processing speed and cutting cost (e.g., Mithas et al. 2013).
In the recent past, enterprise systems (such as ERP) were proposed as solutions to enhance process
efficiencies due to their ability to improve data quality for decision-making, provide efficiency gains across
business processes, and enable better coordination between different units of a firm (Gattiker and
Goodhue 2005). Malhotra et. al. (2005) posit that supply chain partners use information technology
infrastructure to process information and create new knowledge. SMC technologies are precisely meant
for collecting and processing data to find patterns that are insightful and drive innovation in business
processes to alleviate inefficiencies (Trkman et al. 2010). Numerous companies including HP, Dell, and
IBM offer such services and solutions to firms (Niccolai 2011). Hence, we submit that internal demands,
the felt need to improve internal efficiency, is an important catalyst for companies adopting SMC
technologies.
Recently, digital technologies have also gained a center stage in developing customer centric
organizations. Organizations have taken great strides in improving customer satisfaction and service.
Wachovia Bank (now part of Wells Fargo) established a policy of contacting an unhappy customer the
same day (Berry et al. 1994). Continental Airlines has adopted a data warehousing platform to gain access
to real-time customer and flight information that helps them better understand and meet their
passengers’ needs and wants (Watson et al. 2006, Setia et al. 2013). Similarly, Barclay’s Bank, and Best
Buy focus on improving their customer services by collecting and analyzing data about their customers
(Cisco 2007, Kovac et al. 2009). SMC technologies that are used for storing and analyzing large volumes
of data are vital for these firms to recognize customer discontent and improve customer satisfaction. We
propose that market demands, the extent to which the organization must monitor the customer market
and the requirement of customers for personalization in products and services, are a factor that propels
adoption of SMC technologies as part of the broader business analytics strategy. Market demands are
even considered part of an organizational culture that drives customer value through monitoring needs
and responding accordingly (Setia et al. 2013). We hypothesize that internal demands and market
demands are positively correlated with the adoption of SMC technologies (as shown in Figure 1). Market
and internal demands are the precursors, not the realization of the satisfaction of those demands,
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IS Strategy, Structure and Organizational Impacts
meaning that market and internal demands are clearly distinguished from the outcome variable of
adoption and ensuring the falsifiability of our hypotheses.
Aoki (1990) emphasizes the importance of efficient and effective integration and coordination among
different divisions within organizations. Emerging technologies like cloud computing are known to be
associated with integration challenges pertaining to old legacy systems (Linthicum 2009). As firms age, it
becomes complex for the organizations to get rid of older systems due to unique cultures and routines
developed over time. Radical organizational re-engineering is often required to support the new
capabilities provided by new technologies (Abernathy and Clark 1985, Teece 1997). As highlighted in a
recent research report from IBM and University of Oxford (McKenna 2012), anecdotal evidence points to
an industry-wide shortage of people who can use structured and unstructured data generated by the
emergent tools for crafting actionable strategies. As a result, organizations may face a dearth of employees
who can steer through the challenges faced during the implementation of SMC technologies. Companies
possess heterogeneous technological skills, the human capital resources to design, develop, build and
maintain information systems (Ravichandran and Lertwongsatien 2005). Firms facing technological
skills shortages are less likely to produce usable and reliable systems and experience higher SMC
adoption.
Public policies, regulatory bodies, intellectual property regimes, tort laws, etc. can pose several external
challenges for the firms in the process of implementing new technologies. These laws vary across states,
countries and industries. Security of customer data is a priority in many countries (Flood 2013). The
problem of security may get compounded due to the large volume of data generated by SMC technologies.
The increasing number of security-related incidents in the field (e.g., TJ Maxx, Target (Sidel et al. 2013))
and their accompanying legal ramifications have increasingly driven firms to decelerate their pace of
emerging technology investments and to rethink the security implications of every technology that the
firms adopt. We term this construct as the information security concerns of the firm. The security
environment and the extent to which security concerns force the firm to incur higher costs or delay in
implementation act as a potential barrier to firms adopting SMC technologies. We hypothesize that
information security concerns are negatively correlated with the adoption of SMC technologies (as shown
in Figure 1).
Relation of Business Analytics Strategy with Antecedents of Adoption
A Market Responder firm uses business analytics capabilities to develop better understanding of the
customer and acquire new customers. Therefore, we posit that a market responder firm should have
positive correlation with market demands. An Organization Optimizer firm uses the business analytics
capabilities to make decisions at executive levels of the organization. Therefore, such a BAS profile should
have positive correlation with internal demands and market demands. Such a firm is also expected to be
very concerned about the security of its assets, intellectual property rights etc. Therefore, we posit that
organization optimizer would demonstrate a negative correlation with technological skills shortage and
information security concerns. An Efficiency Seeker firm is expected to use business analytics to improve
the efficiencies of its internal business processes. Therefore, we expect to observe a positive correlation
between this BAS profile and the construct internal demands. Finally, an Analytics Laggard is expected to
wait until firms of other profiles develop expertise in using business analytics for different functions and
help mature the technologies. Moreover, such firms are expected to be wary of using the technologies if
the challenges in using them are substantial. Therefore, we expect an Analytics Laggard to show a
negative correlation with technological skills shortage and information security concerns.
Influence of IT Investment
Forman (2005) provides strong evidence that IT investments impact technology adoption in general. Any
important IT initiative requires extensive organizational restructuring and training on new hardware and
software (Anderson et al. 2006). Moreover, new project execution requires substantial investments in
coordination, preparation, monitoring and launching for controlling costs which tend to be high during
new project implementation (e.g., Kim and Mukhopadhyay 2011). We expect that IT investment is
therefore positively correlated with the adoption of SMC. Thus, we control for the increase in information
technology investments as a predictor of adoption of SMC.
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Business Analytics Strategy: Social, Mobile, and Cloud Computing
Methodology
We have already discussed the source of the data in a previous section. The data also contains questions
that are relevant to study (i) the degree to which firms adopt social, mobile, and cloud computing
technologies, (ii) the presence of antecedents of adoption – internal demands, market demands,
technological skills shortage, and information security concerns, and (iii) the increase in IT investment
anticipated by the firm in the next two years.
Measurement
The dependent variable, Adoption of Social, Mobile, and Cloud Computing, is a formative construct that
measures the current adoption status of the three emerging technologies by the firm. Adoption status is
measured on a scale of 1 to 5 denoting no plans, planned within 24 months, pilot stage, limited set of
capabilities deployed and significant capabilities deployed, respectively. SMC, via their generation and
use of big data, help firms differently in developing the capabilities to compete in dynamic market places.
For instance, while SMC helps increase the firm’s communications with customers, these three
technologies help in the communications in different ways. Mobile devices, such as smartphones, are used
to track individual customer data. Nordstrom tracks data on – how many customers came through the
doors, how many were repeat visitors, etc. (Clifford and Hardy 2013). Social computing is used to gain
and use insights derived from interactions of people in a network of individuals. Tibco and Yammer
develop internal social platforms that can help companies organize their functions requiring interactions
of employees (Hardy 2012). Levy (2014) discusses tools that companies may use to analyze data over
social media platforms such as Facebook, Twitter etc. in order to spread the word about products and
track how the product popularity changes. Both mobile and social computing are means to interact with
the customers (Davies et al. 2013).
Internal Demands are measured as the extent to which the organization is motivated to adopt big data
technology to facilitate communication and collaboration, responding to internal employee demand and
providing business scalability. Market demands are the extent to which the organization is motivated to
adopt big data technologies to understand customer sentiment, respond to partner/customer demand and
enable faster time to market.
On the other hand, the antecedents, technological skills shortage and information security concerns, are
measured as the human capital with specific knowledge in a broad set of IT and business domains and
presence of IT security policies and practices insufficient to meet the firm’s needs, respectively. For
example, if we consider the construct technological skills shortage, cloud computing implementations
may require knowledge about cloud architecture and implementation procedures of on-site solutions
(Florentine 2013). Technological skills shortage regarding mobile computing include development of user
interfaces and applications that can communicate over the mobile networks and Wi-Fi (Sacco 2008).
Finally, social computing requires knowledge and skills to handle and use large amount of unstructured
data to develop insights using advanced technologies such as Hadoop. Technological Skills Shortage is
measured as a formative construct that captures inadequacy of IT skills related to the emerging
technologies of social, mobile and cloud computing respectively. The construct, Information Security
Concerns, is measured formatively as the existence of challenges in securely provisioning access control
and protecting confidential data across social, mobile and cloud computing.
Preliminary Results and Analysis
As we are in the process of analysis, we present our preliminary findings in this section. First, factor
analysis on the data shows clear and expectation-consistent loadings of the questions on the constructs
shown in Figure 1. We avoid presenting the results of factor analysis for brevity.
We perform linear multiple and ordinal regressions (including generalized ordered regressions) with
degree of adoption as the dependent variable. Given the structural form as in Figure 1, the antecedents of
adoption are endogenous constructs. In Table 1 we provide the results of the linear regression model for
degree of adoption.
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IS Strategy, Structure and Organizational Impacts
After controlling for the change in IT investment expected to drive adoption, the constructs – internal
demands and market demands, are positive and significant predictors of adoption. The coefficients of the
constructs – technological skills shortage and information security concerns - are negative and significant.
Therefore, our hypotheses for the relationship of the degree of SMC adoption with the antecedents of
adoption are supported. In the future, we will include additional control variables in order to derive
robust conclusions regarding the relationship of SMC adoption with the antecedents of adoption to
adoption of SMC. Our analysis up to this point does not leverage the BAS profiles in the structural model,
however, we expect that the profiles will provide additional insights. Preliminarily path regression
analysis indicates that the antecedents to adoption are experienced differentially depending on the BAS.
We will also perform structural equation modeling and mediation/moderation tests to analyze the full
model.
Table 1. Linear Regression Results (Degree of Adoption)
Coefficient
(Constant)
7.511***
Internal Demands
0.161*
Market Demands
0.287***
Technological skills shortage
-0.389***
Information Security Concerns
-0.242***
Change in IT Investment
0.266***
***: p-value < 0.01; **: p-value < 0.05; *: p-value < 0.1
Std. error
0.522
0.093
0.093
0.099
0.087
0.044
Discussion and Conclusion
In this research, we study various business analytics strategies of firms and their relationship with
adoption of SMC. These technologies serve as foundations for capabilities in business analytics. Such
capabilities enable firms to create their niche positions in dynamic market environments. Whereas
adoption of SMC technologies are influenced by the firms’ business analytics strategies, the specific
relationship with the adoption of SMC is shaped by firm level antecedents of technology adoption of SMC.
Based on our analysis, we first find that firms may be divided into five clusters based on their business
analytics strategies, the relative position they take in using big data across three business domains –
market sensing, governance and internal operations. While an Analytics Leader uses business analytics
for improving its performance across all three business domains, an Analytics Laggard is a minimal user
of business analytics for any of the three domains. The firms in the other three profiles focus their use of
business analytics primarily on one of the three business domains.
Next, we find that internal demands and market demands, play a role in driving SMC adoption. However,
because these technologies are new, they pose significant challenges to firms adopting them. Primary
among them are the technological skills and the broad information security concerns specific to SMC.
Both these constructs are negatively related to the adoption of SMC technologies.
The implications for top managers in the firm are that understanding the specific business analytics
strategy of the firm and anticipating the antecedents of technology adoption that drive SMC adoption are
critical for success. While the manager may have leverage to drive adoption through other mechanisms,
the environment of the firm matters in terms of achieving desired SMC outcomes.
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