When Does Digital Business Strategy Matter to …...antecedents include concepts of three important domains encompassing strategy (i.e., digital business strategy), market approach

Digital Business Strategy and Market Performance

Thirty Seventh International Conference on Information Systems, Dublin 2016 1

When Does Digital Business Strategy Matter to Market Performance?

Completed Research Paper

Alexander Leischnig University of Bamberg Marketing Intelligence Feldkirchenstraße 21

96052 Bamberg, Germany [email protected]

Steffen Woelfl University of Bamberg Marketing Intelligence Feldkirchenstraße 21

96052 Bamberg, Germany [email protected]

Bjoern S. Ivens

University of Bamberg Marketing

Feldkirchenstraße 21 96052 Bamberg, Germany

[email protected]

Abstract

Business digitization has become an important topic in both management practice and academic research. The purpose of this research is to further illuminate and describe the conditions under which a digital business strategy can contribute to achieving strategic advantages. Drawing on configuration theory and using a configurational approach based on fuzzy-set Qualitative Comparative Analysis (fsQCA), we explore configurations of digital business strategy, factors related to firms' market approaches, and environmental factors to explain superior market performance. The findings of our study indicate four distinct configurations that differ in their particular composition, but that are consistently sufficient pathways for high market performance. Knowledge of these configurations deepens the understanding of the complex causal patterns among key elements of digital ecodynamics and provides strategic choices for management practice.

Keywords: Business strategy, digital business ecosystems, firm performance



Introduction

Organizations in a wide range of industries redesign processes and even entire business models to transform innovative information technology (IT) options and digitization opportunities into strategic advantages. For example, McKinsey & Company predicts a third wave of digitization for health care services, which involves the development of purely digital offerings and the data-driven analysis of customer preferences (Biesdorf and Niedermann 2014). In addition, eBay’s Devin Wenig (2014) emphasizes a seamlessly merged world of stationary and online retail. These and many other examples from further industries indicate that business digitization has received a top place on managers’ agendas.

Besides business practice, the topic of digitization has emerged as a research priority in many academic disciplines such as business research and information science (IS). Prior work shows that firms differ in their degree of business digitization (Slywotzky et al. 2000) and that digitization of businesses has implications for firm-internal strategies and practices such as human resource management (HRM) and work flow management (e.g., Bhansali and Brynjolfsson 2007), as well as for the ways in which firms operate in markets and develop, nurture, and maintain relationships with market actors such as customers and suppliers (e.g., Gunasekaran et al. 2002; Rust and Espinoza 2006). Furthermore, prior work emphasizes that a holistic understanding of digital ecodynamics, defined as the confluence of firm abilities and IT contingent upon environmental conditions, constitutes a new frontier in IS (El Sawy et al. 2010).

The focus of this research is on digital business strategy (Bharadwaj et al. 2013) within the broader scope of the firm and its environment to develop new insights for the generation of strategic advantages. Specifically, we aim at uncovering situations in which a digital business strategy contributes to market performance. Drawing on configuration theory (Ketchen et al. 1993; Meyer et al. 1993), we examine the interplay among digital business strategy, dimensions of firms’ market approaches, and environmental characteristics to unravel configurations of these factors sufficient for high market performance. Knowledge of such configurations provides vision for the interconnected structures and the causal patterning of key elements of digital ecodynamics and helps develop more fine-grained theories about how particular elements work together to produce strategic advantages. In addition, and from a managerial point of view, knowledge about alternative configurations sufficient for superior market performance offers design choices for managers that may serve as guidelines for assessing and revising market strategies and reconfiguring existing activities.

To achieve this goal, we follow recent calls for research that advocate a configurational approach as a means to deepen the understanding of fused, dynamic interactions of organizational and environmental factors (El Sawy et al. 2010; Park and El Sawy 2013). We conduct an exploratory study using fuzzy-set Qualitative Comparative Analysis (fsQCA; Ragin 2008), that is, a set-theoretic method based on Boolean algebra, to illuminate the complex causal patterns among digital business strategy and characteristics of the firm and the business environment to explain market performance. FsQCA has received increased attention in the business literature (e.g., Fiss 2011; Leischnig et al. 2016) and in the IS literature (e.g., Park and El Sawy 2013; Tan et al. 2016). This method takes into account that an outcome of interest (such as high market performance) rarely has a single antecedent, that multiple antecedents work together to bring about an outcome, and that a specific antecedent can have positive or negative effects on an outcome, depending on how it combines with other antecedents to form a configuration (Greckhamer et al. 2008). As such, fsQCA is particularly well-suited for the analysis of complex causality (Ragin 2008) inherent to digital ecodynamics by providing vision of complementarity, substitution, and suppression effects (El Sawy et al. 2010).

Conceptual Background

Theoretical Foundation

The primary theoretical perspective adopted here is that of configuration theory. Configuration theory builds on holistic synthesis as the dominant mode of inquiry (Doty and Glick 1994; Meyer et al. 1993) and assumes that “organizational phenomena can best be understood by identifying distinct, internally consistent sets of firm and their relationships to the environment and to performance outcomes” (Ketchen et al. 1997, p. 224).



According to configuration theory, firms are systems of interconnected elements that tend to form configurations because their interdependence makes them fall into patterns (Meyer et al. 1993; Miller 1996). Configurations can embrace multiple domains (Dess et al. 1993) and are constellations of elements that commonly occur together and that are orchestrated and connected within a unifying theme (Meyer et al. 1993; Miller 1996).

Configuration theory seeks to explain how order emerges from the interplay of elements and it considers reciprocal and nonlinear relationships among the elements as well as the occurrence of alternative causal routes to an outcome (Meyer et al. 1993). Configuration theory holds that for sets of elements, there exist a limited range of configurations that enable organizations to accomplish strategic goals and thus achieve superior performance (Ketchen et al. 1993). Hence, configuration theory incorporates the idea of equifinality (Doty and Glick 1994; Gresov and Drazin 1997), which means that “a system can reach the same final state from different initial conditions and by a variety of different paths” (Katz and Kahn 1978, p. 30).

In addition, configuration theory assumes that the elements within configurations can differ in their importance and can be classified as core and peripheral factors based on their causal essentiality for the outcome in question (Fiss 2011). Core factors are those elements for which evidence demonstrates a strong causal link with the outcome of interest, whereas peripheral factors are those elements for which evidence indicates a weaker causal relationship with the outcome in question (Fiss 2011). Peripheral factors in a configuration typically surround core conditions and underline their central features (Grandori and Furnari 2008; Fiss 2011).

The configuration theoretical perspective has been emphasized as a useful inquiring system in the IS literature, especially for studying digital ecodynamics with their “holistic messy nature” (El Sawy et al. 2010, p. 838). A configuration theoretical perspective helps advance the understanding of how IT systems and strategies interrelate with additional organizational elements and properties of the environment in a systemic way and form configurations for achieving competitive firm performance (El Sawy et al. 2010). For example, in a study that examines the interplay among organizational agility, IT capability, top management team energy, organizational size, and environmental turbulence to explain high firm performance, Park and El Sawy (2013) identify five alternative configurations that represent equifinal causal pathways to the focal outcome. The patterning of the antecedents indicates that in turbulent environments, the presence of a high IT capability has an important role in bringing about high firm performance, whereas in stable environments, the negation of a high IT capability contributes to high firm performance. The configurational approach thus provides vision for the opposing effects of IT capability as an enabler or inhibitor firm performance (Park and El Sawy 2013).

Research Framework

Figure 1 depicts our research framework and uses a 5-Venn diagram (Grünbaum 1975) to capture the configurational perspective adopted here. The left side of the framework shows five antecedents and the right side of the framework shows the outcome of interest (i.e., market performance). The five antecedents include concepts of three important domains encompassing strategy (i.e., digital business strategy), market approach (i.e., focus on business to business markets (BtB) and/or business to consumer markets (BtC), and focus on selling services and/or goods), and environment (i.e., customer heterogeneity and technological turbulence). The overlaps of the antecedents symbolize the multiple configurations that may arise as sufficient for the outcome.

The primary question of this research is: When does a digital business strategy lead to market performance, considering firms’ market approaches and diverse environmental demands? This question builds upon the premise that firms face the challenge to develop consistent patterns of internal elements that match the demands emerging from external conditions to outperform competitors (Ketchen et al. 1993). Thus, strategic elements and elements of the market approach need to be aligned such way that they enable firms to cope with environmental demands and achieve superior market performance.

Empirical research on digital business strategy supports this notion. For example, Mithas, Tafti, and Mitchell (2013) demonstrate that a firm’s digital strategic position relative to its industry influences digital strategic moves and that this effect differs contingent on environmental factors such as turbulence. In this research, we extend the causal chain and focus on market performance as critical facet of business



success. Market performance is defined as the degree to which a firm achieves market-based goals (Vorhies and Morgan 2003). According to configuration theory, multiple routes to market performance likely exist. These routes can encompass alternative combinations of factors related to strategy, market approach, and environment and they represent alternative “causal recipes” (Ragin 2008) for the same outcome.

Figure 1. Research Framework

The focal strategic factor in our study is that of digital business strategy. In line with Bharadwaj et al. (2013, p. 472), we define digital business strategy as an “organizational strategy formulated and executed by leveraging digital resources to create differential value.” Inherent to this definition is the notion that digitization is part of a firm’s business model, implying that top management shows commitment toward digitization as a strategic option for the creation of differential value. In addition, this definition recognizes digital resources as inputs for the creation of differential value as a performance goal, which transcends the predominant focus on systems and technologies (Bharadwai et al. 2013). For example, prior work reveals that IT alone does not create value; it needs to be a part of a business value creating process and should be aligned with additional IS and organizational factors in a synergistic fashion (Croteau and Raymond 2004; Kohli and Grover 2008).

Studies indicate that digital business strategy matters to business success (e.g., Drnevich and Croson 2013; Johnson and Bharadwaj 2005; Setia et al. 2013). Investments in IT and digital resources influence performance by enabling different profit mechanisms that relate to such issues as collusion/coordination, governance, competence, and flexibility (Drnevich and Croson 2013). For example, a digital business strategy may generate digital options through enhanced digitized process capital and digitized knowledge capital, thus enabling a business infrastructure that improves a firm’s capacity to take competitive actions (Sambamurthy et al. 2003). An empirical study with focus on customer-side digital business strategies in services supports this notion and shows that information quality, as a property of digital design, contributes to superior service performance by enabling strategic and operational coordination (Setia et al. 2013). However, prior work indicates also that in the long-term digitization may have detrimental effects. Specifically, implementing a digital business strategy may lead to higher system visibility and the revelation of a firm’s key value appropriation mechanisms, which provides incentives for market rivals to imitate (Grover and Kohli 2013), thus eroding a firm’s competitive advantage. The way in which a digital business strategy transforms into performance thus depends on additional firm-internal and external factors (e.g., El Sawy et al. 2010; Johnson and Bharadwaj 2005; Melville et al. 2004). Following this, we examine additional conditions to better understand the situations in which digital business strategy transforms into performance gains.



Specifically, we focus on firms’ market approaches and consider two important facets: customer focus and offering focus. Customer focus refers to the extent to which a firm operates in business markets (BtB) and/or consumer markets (BtC) (Homburg et al. 1999). Firms target different market segments and thus differ in terms of customer focus. While some firms focus exclusively on business markets and target business customers, others focus exclusively on consumer markets and direct market activities to individuals or private households. In addition, some firms operate in business and consumer markets and focus on both customer segments. Apparently, business markets and consumer markets differ in several aspects such as, for example, purchasing/selling processes, customer characteristics, offer complexity, industry standards, legal issues (e.g., Brown et al. 2007; Fern and Brown 1984). These differences foster firms to develop and implement different strategies to accomplish market-based goals and thus give rise to the question of when does digital business strategy contribute to superior market performance?

Besides differences in customer focus, firms differ with regard to the type of offering they sell. The dimension of offering focus refers to the extent to which firms sell services and/or goods (Verhoef and Leeflang 2009). Similar to customer focus, firms employ alternative offering-based approaches such as pure service providers, pure manufacturers, or solutions providers that offer bundles of goods and services (Tuli et al. 2007). Prior research has established that services differentiate from goods on key dimensions (i.e., intangibility, simultaneity of production and consumption, inseparability, and nonstandardization; Zeithaml et al. 1985), which provides several opportunities for service digitization (Rai and Sambamurthy 2006). However, how service digitization relates to performance is a matter of debate and has been controversially discussed in the literature (Kathuria et al. 2014).

The environmental characteristics considered in this research are customer heterogeneity and technological turbulence. Customer heterogeneity refers to the degree of (dis)similarity of a firm’s customer base (Achrol and Stern 1988). A heterogeneous customer base confronts firms with diverse customer needs and preferences that require a differentiated market approach to address them. A major challenge for firms that operate in heterogeneous markets is to obtain and understand information about customers and transform them into appropriate strategic programs and market activities (Dwyer and Welsh 1985). Customer heterogeneity has been associated with decision maker uncertainty (Achrol and Stern 1988) because it implies strategic variety. To date, research on how customer heterogeneity relates to digital business strategy and its performance implications is scarce. However, one may argue that a digital business strategy can improve firms’ abilities to systematically manage information about customers, thus providing a more accurate basis for segmentation and targeting and the creation of differential value. In addition, a digital business strategy that fits into firms’ core businesses may help reach and attract digital-prone segments that would not have been reached otherwise.

Besides customer heterogeneity, we also consider technological turbulence, that is, the rate of technological change (Jaworski and Kohli 1993). A technologically turbulent environment shows rapid technology changes and requires increased investments in technology and technology-building capabilities to generate tech-based competitive advantages (Slater and Narver 1994). Studies indicate that technological turbulence acts as an important moderator in variance-based models (e.g., Pavlou and El Sawy 2006). More recently, research adopting a configurational perspective reveals that technological turbulence is an important element in configurations sufficient for achieving high performance. For example, El Sawy et al. (2010) note that technological discontinuity in combination with demand uncertainty, improvisational and planned dynamic capabilities, and different forms of IT systems forms a configuration sufficient for high performance.

Research Approach

Data Collection and Sample Characteristics

We conducted a survey to examine and describe the causal patterns among digital business strategy, market approaches, environmental factors, and market performance. The sampling frame consisted of firms identified through a proprietary database and involved key informants with expert knowledge about firms’ business approaches and market strategies (i.e., CEOs, managing directors, marketing and sales managers). The firms covered various industries such as manufacturing, professional services, retail, or consumer goods to ensure sufficient variation.



Data for this study were collected through a self-administered paper and pencil survey. Key informants received the questionnaire together with a cover letter that invited them to participate in the survey. In addition, the cover letter informed the key informants that there are no correct or wrong answers and that all data are collected anonymously (Podsakoff et al. 2003). After the mail survey, we sent a reminder e-mail that included a link to the questionnaire in online format, thus allowing informants to answer the questions online.

In total, we received 121 valid responses. The average firm in the sample has a sales volume between 25 million and 50 million Euro and has 100 to 250 employees. Approximately 55 percent of the firms are family businesses. Of the respondents, 34 percent have a top management position (i.e., CEO or managing director), 42 percent have a senior-level management position (i.e., marketing or sales director), 18 percent have a mid-level management position (i.e., marketing or sales manager), and 6 percent have other functions in the firms (e.g., technical director). Respondents’ average organizational tenure is 14.7 years (SD = 15.77) and the mean age is 46.3 (SD = 10.24).

Construct Measures, Measurement Validation, and Tests for Potential Biases

We used a standardized questionnaire as the main data collection instrument and employed established scales for the construct measures whenever possible. The questionnaire contained multiple-item scales for the measurement of digital business strategy, customer heterogeneity, technological turbulence, and market performance, and single items to capture the dimensions of firms’ market approaches. For digital business strategy, we asked respondents to indicate the degree to which they agree or disagree with six items that relate to such issues as the extent to which digitization is a management priority within firms, firms offer digital solutions, and firms use digital resources for business activities. These items were shown on a 7-point Likert-type agreement scale. We captured customer focus as firms’ share of business in BtB and/or BtC markets based on Homburg et al. (1999) and firms’ offer focus as the share of business in selling services and/or goods based on Verhoef and Leeflang (2009). To measure customer heterogeneity, we used four items based on Achrol and Stern (1988) that were presented on a 7-point Likert-type similarity scale. For technological turbulence, we used four items based on Jaworski and Kohli (1993) that were shown on a 7-point Likert-type agreement scale. Finally, we used a scale developed by Vorhies and Morgan (2005) to capture firms’ market performance. This scale consisted of four items and asked respondents on a 7-point Likert-type rating scale to indicate their firm’s market performance relative to that of major competitors, thus accounting for industry effects (i.e., whether a firm operates worse or better than its competitors). Table 1 below details the measurement instruments for each of the constructs and provides information on reliability and validity criteria.

We established the measurement model using confirmatory factor analysis (CFA) and by assessing global fit indices and criteria for the internal structure of the model (e.g., Bagozzi and Yi 1988; Bagozzi et al. 1991). For evaluation of the overall model fit, we used multiple indices including the comparative fit index (CFI), the Tucker–Lewis index (TLI), and the root mean square error of approximation (RMSEA). The results showed that the measurement model has an acceptable overall model fit (χ2 = 171.96, df = 122, χ2/df = 1.41, CFI = 0.95, TLI = 0.92, RMSEA = 0.06). To assess the internal structure of the measurement model and to confirm reliability and validity of the construct measures, we calculated additional parameters. The results of these analyses showed that Cronbach’s alpha for the scales ranges between 0.80 and 0.87 and exceeds the commonly used threshold of 0.7 (Nunnally 1978). In addition, the results showed that composite reliability scores range between 0.80 and 0.87 and that average variances extracted range between 0.52 and 0.70, thus exceeding the standard thresholds of 0.6 and 0.5, respectively (Bagozzi and Yi 1988). Analysis of discriminant validity according to the procedure suggested by Fornell and Larcker (1981) revealed that the average variance extracted for each construct is higher than the squared inter-construct correlations. Table 2 shows descriptive statistics for the construct measures and discriminant validity. In summary, the results of the measurement model validation procedure indicated that the model fits the empirical data well.

We performed additional tests to control for potential biases. Following Armstrong and Overton (1977), we assessed nonresponse bias by comparing the responses of early and late respondents for the focal constructs. The results of a series of t-tests revealed no significant differences (i.e., all p > 0.05), thus indicating that nonresponse bias is no issue in our study. In addition to nonresponse bias, we controlled for common method bias (Podsakoff et al. 2003; Podsakoff and Organ 1986). First, we run Harman’s



single factor test based on exploratory factor analysis (EFA), which revealed that no single factor emerged from the unrotated factor solution and that no first factor explained the majority of the variance in the variables. Next, we performed a χ2-difference test based on CFA (Malhotra et al. 2006), which indicated that a single-factor model in which all items of the constructs loaded on a single factor fits the data significantly worse than the postulated multi-factor model in which items loaded on the respective constructs (∆χ2 = 500.03, ∆df = 4, p < 0.001). Based on these results, common method bias does not constitute an issue in our study.

Table 1. Information on Construct Measures

Digital business strategy (α = 0.86, CR = 0.87, AVE = 0.52)

To what extent do you agree or disagree with the following statements?

7-point Likert-type agreement scale ranging from 1 = “completely disagree” to 7 = “completely agree”

– Our managers consider digitization as a key factor for achieving competitive advantages.

– The core values of our company include digitization as a key to improvement.

– We offer several digital solutions to our customers.

– Our customers can interact with us in many ways via the Internet.

– Many of our processes, programs, or activities relate to the integration of new digital technologies.

– We use many digital resources for our business activities.

Share of business BtB/BtC (α = n.a., CR = n.a., AVE = n.a.)

Please indicate the percentage of your turnover that arises from BtB or BtC markets. (total = 100%)

– Share BtB in %

– Share BtC in %

Share of business services/goods (α = n.a., CR = n.a., AVE = n.a.)

Please indicate the percentage of your turnover that arises from selling goods or services. (total = 100%)

– Share goods in %

– Share services in %

Customer heterogeneity (α = 0.87, CR = 0.87, AVE = 0.69)

With regard to the following characteristics, the customers in our industry are …

7-point Likert-type similarity scale ranging from 1 = “very similar” to 7 = “very different”

– Individual attributes

– Preferred variety of brands/features

– Preferences in price/quality

– Credit needs*

Technological turbulence (α = 0.86, CR = 0.87, AVE = 0.70)

To what extent do you agree or disagree with the following statements?

7-point Likert-type agreement scale ranging from 1 = “completely disagree” to 7 = “completely agree”

– The technology in our industry is changing rapidly.

– Technological changes provide big opportunities in our industry. – A large number of new product ideas have been made possible through technological breakthroughs

in our industry. – Technological developments in our industry are rather minor. (rc)*

Market performance (α = 0.80, CR = 0.81, AVE = 0.53)

Please evaluate the performance of your firm relative to your major competitors.

7-point Likert-type rating scale ranging from –3 = “much worse” to +3 = “much better”

– Share growth relative to competitors

– Growth in sales revenue

– Acquiring new customers

– Increasing sales to existing customers

Notes: α = Cronbach’s alpha; CR = composite reliability; AVE = average variance extracted; rc = reverse coded item; * = item eliminated based on the scale refinement procedure; n.a. = not applicable.



Table 2. Descriptive Statistics and Discriminant Validity

M SD 1 2 3 4 5 6

Digital business strategy 4.0 1.37 0.52

Customer heterogeneity 3.9 1.59 0.00 0.69

Technological turbulence 4.3 1.48 0.22 0.05 0.70

Share of business BtB/BtC 75.2/24.8 35.06 0.03 0.00 0.05 –

Share of business services/goods 40.5/59.5 39.94 0.09 0.00 0.02 0.01 –

Market performance 4.6 0.85 0.01 0.00 0.00 0.00 0.02 0.53

Notes: AVE on the diagonal and squared correlations below the diagonal.

Fuzzy-set Qualitative Comparative Analysis (fsQCA)

In order to delineate configurations of digital business strategy and the organizational and environmental factors sufficient for high market performance, we run a fsQCA. FsQCA views cases as combinations of attributes (i.e., the antecedents and the outcome in question; Ragin 2008). It rests on the premise that relationships between concepts are best understood in terms of set relations (Fiss 2011). To assess such set relations, the antecedents and the outcome need be represented in fuzzy-set membership scores. Fuzzy-set membership scores range between 0 and 1 and express the degree to which a case is member of a set and, at the same time, the complement of the set (i.e., the negation; Ragin 2008). For example, a case with a fuzzy-set membership score of 0.75 in the set of firms with a high market performance has a fuzzy-set membership score of 0.25 (i.e., 1 – 0.75 = 0.25) in the complement set of firms with a not-high market performance. FsQCA describes how the membership of cases in sets of antecedents is linked to membership in the outcome set (Fiss 2011; Ragin 2008). FsQCA examines the explicit connections between antecedents and the outcome in terms of necessity and sufficiency. Necessity means that an antecedent condition has to be present for an outcome (Ragin 2008). From a set-theoretic perspective, necessity exists when the instances of the antecedent are a superset of the instances of the outcome (Ragin 2006). By contrast, sufficiency means that an antecedent (or a combination of multiple antecedents) can bring about an outcome (Ragin 2008). Sufficiency implies that instances of the antecedents (or combinations thereof) are a subset of the instances of the outcome (Ragin 2006).

The fsQCA in this study involved five antecedents as shown in Figure 1. The outcome of interest is high market performance. Following recommendations in the literature (Fiss 2011; Ragin 2008; Schneider and Wagemann 2010), we performed the fsQCA in three steps: First, we calibrated the fuzzy-sets and transformed the construct measures into fuzzy-set membership scores. Next, we examined whether any of the antecedents is a necessary condition for market performance (superset analysis). Finally, we examined (combinations of) antecedents for high market performance to delineate sufficient configurations (subset analysis). We used the fs/QCA software program (Ragin et al. 2006) for each of the steps.

Calibration

For calibration, we combined the multiple-item construct measures into composite scores. In line with the QCA literature, we then defined three qualitative anchors to structure the calibration (i.e., the threshold for full membership in a set, the threshold for full non-membership in a set, and the crossover point; Ragin 2000). For calibration, the construct measures are centered on the crossover point and transformed to odds ratios. Then, the logarithm of these odds ratios is calculated and the degree of membership in the fuzzy-set as expressed by the fuzzy-set membership score is calculated. The resulting fuzzy-set membership scores are tied to the respective membership thresholds and the crossover point.

For digital business strategy (measured on a 7-point Likert-type agreement scale), we set the threshold for full membership in the set of digital businesses at value 7 (i.e., the scale maximum) and the threshold for full non-membership in the set at value 1 (i.e., the scale minimum). Value 4 (i.e., the scale midpoint) served as the crossover point. Thus, cases that indicate complete agreement with all items for digital business strategy are fully in the set of digital businesses, whereas cases that indicate complete



disagreement with these items are fully out of the set. In addition, cases that have a value between 4 and 7 are more in than out of the set and cases that have a score between 1 and 4 are more out of than in the set. We proceeded in a similar fashion to define the fuzzy-sets for high customer heterogeneity, high technological turbulence, and high market performance. For each of these concepts, we set the threshold for full membership in the set at the scale maximum, the threshold for full non-membership in the set at the scale minimum, and the crossover point at the scale midpoint. Regarding the dimensions of firms’ market approach captured as the share of business in BtB/BtC markets and as the share of business in selling services/goods in percent, we performed a linear transformation to obtain fuzzy-set membership scores. A firm that has a share of business in BtB markets of, for example, 65 percent yields a fuzzy-set membership score of 0.65 in the set of firms that focus on BtB markets. This firm is more in than out of the set of firms with BtB focus (and more out of than in the set of firms with focus on BtC markets (i.e., the negation) based on the corresponding fuzzy-set membership score of 0.35) Likewise, a firm that has a share of business in selling services of, for example, 75 percent yields a fuzzy-set membership score of 0.75 in the set of firms that focus on selling services and, as such, is more in than out of the set of services providers and more out of than in the set of firms that focus on selling goods (corresponding fuzzy-set membership score: 0.25).

Noteworthy, the calibration of construct measures can yield fuzzy-set membership scores of exactly 0.5. These scores meet the crossover point and lead to problems when determining whether a case is in or out of a particular fuzzy set (Ragin 2008). To address this problem, we added a constant of 0.001 to the fuzzy-set membership scores for all conditions below full membership (Fiss 2011).

Analysis of set relations

The analysis of set relations involved two steps including an analysis of necessity followed by an analysis of sufficiency. Necessity means that for each empirical case, the fuzzy-set membership score of the outcome is smaller than the fuzzy-set membership score of the antecedent (Ragin 2006; Schneider and Wagemann 2012). This rule typically does not hold for all cases and the QCA literature thus suggests inspection of consistency scores. In an analysis of necessity, consistency captures the degree to which instances of the outcome agree in displaying the antecedent thought to be necessary (Ragin 2006). An antecedent is considered as necessary or “almost always necessary” if its consistency score exceeds the threshold of 0.9 (e.g., Schneider et al. 2010).

Next, we examined configurations of the five antecedents for high market performance in a sufficiency analysis. As outlined in the QCA literature (Ragin 2008; Schneider and Wagemann 2012), we created a so-called truth table, that is, a data matrix that captures all logically possible combinations of the five antecedent conditions. Each row of the truth table reflects a particular combination of antecedents and the number of rows of a truth table grows exponentially with the number of antecedents analyzed (i.e., n = 2k, where n denotes the number of rows and k denotes the number of antecedents considered). We then refined this truth table based on frequency and consistency (Fiss 2011; Ragin 2008, Schneider and Wagemann 2012). Frequency refers to the number of empirical cases that show a particular combination of antecedents. Some rows of the truth table can show many cases, while other rows show only a few cases, and some rows might even show no cases. The definition of a frequency cutoff ensures that only those combinations that achieve a minimum level of empirical representation are part of the analysis. Combinations with little or no empirical representation are treated as logical remainders in the analysis. For small and medium-sized samples, the QCA literature recommends a frequency cutoff of 1. For larger samples, the frequency cutoff should be set at a higher value (Ragin 2008). Following this, we set the frequency cutoff at value 3. This threshold ensured that 83 percent of all of the empirical cases were part of the analysis and that combinations with less than three cases were treated as logical remainders (e.g., Greckhamer et al. 2013 for further details). To distinguish combinations of antecedents that consistently lead to an outcome from those that do not, QCA literature recommends the definition of a consistency threshold of at least 0.8 (Fiss 2011; Ragin 2008). In an analysis of sufficiency, consistency expresses the degree to which the instances of an antecedent (or combination of multiple antecedents) agree in displaying the outcome (Ragin 2006). In our analysis, we set the minimum acceptable level of consistency at value 0.96. We obtained this value from an inspection of the ordered consistency scores and a dip in the scores at value 0.96 (Schneider and Wagemann 2010). Next, we assessed proportional reduction in inconsistency (PRI) scores for those configurations that passed the consistency threshold (Misangyi and



Acharya 2014). PRI consistency is sensitive to conditions being a subset of the presence and the negation of an outcome (Schneider and Wagemann 2012). We set the PRI threshold at 0.8 and analyzed the refined truth table using the truth table algorithm as implemented in the fs/QCA software program (Ragin et al. 2006).

Results

Table 3 shows the results of the analysis of necessity and indicates consistency and coverage scores for each of the antecedent sets and their negations. Coverage scores express the proportion of overlap of the outcome set and the antecedent set and assess the relevance (or trivialness) of a necessary condition (Ragin 2006). According to the results, consistency scores for the presence and the negation of each of the antecedents are lower than 0.9. Thus, none of the antecedents (and none of their negations) can be considered as necessary or indispensable for high market performance.

Table 3. Necessary Conditions for High Market Performance

Antecedents Consistency Coverage

Digital business strategy 0.69 0.86

Share of business BtB/BtCᵃ 0.82 0.67

Share of business services/goodsᵇ 0.48 0.73

Customer heterogeneity 0.66 0.83

Technological turbulence 0.74 0.82

~Digital business strategy 0.68 0.84

~Share of business BtB/BtC 0.32 0.79

~Share of business services/goods 0.69 0.72

~Customer heterogeneity 0.66 0.81

~Technological turbulence 0.60 0.84

Notes: ~ = logical not (i.e., negation); necessity consistency threshold = 0.9; ᵃ presence indicates focus on BtB markets; ᵇ presence indicates focus on selling services.

Table 4 illustrates the results of the analysis of sufficient configurations of antecedents for high market performance, which is the main focus of this research. To summarize the findings of this analysis, we use the notation developed by Ragin and Fiss (2008). Black circles indicate the presence of an antecedent and circles with a cross-out indicate the negation of an antecedent. In addition, large circles indicate core elements and small circles indicate peripheral elements in configurations. Blank spaces in Table 4 indicate antecedents that have a subordinate role in a configuration. We ordered the configurations by their core conditions.

The results in Table 4 reveal four configurations sufficient for high market performance. One of these configurations (i.e., configuration 1) has two neutral permutations (i.e., configurations 1a and 1b). From a technical perspective, the configurations obtained by the fsQCA are conjunctions, that is, Boolean-algebraic products of the conditions examined (Thiem et al. 2016), thus offering insights into multiple conjunctural causality (Ragin 2008). The occurrence of four alternative configurations for high market performance points to across-type equifinality and the occurrence of two neutral permutations within configuration 1 points to within-type equifinality (Fiss 2011). In addition to the configurations, Table 4 shows consistency and coverage scores for the overall solution and for each of the particular configurations. Consistency indicates the significance of a subset relation and coverage helps assess the empirical relevance of the solution and the configurations (Ragin 2006). The overall solution consistency is 0.91 and the consistency scores for the particular configurations range between 0.90 and 0.97, thus indicating consistently sufficient pathways to high market performance. In addition, the combined model has an overall solution coverage of 0.63, which indicates that the four configurations account for the



majority of membership in the outcome (i.e., the solution has a high “explanatory” power). For the specific configurations, Table 4 shows raw and unique coverage scores. While raw coverage scores indicate the proportion of overlap of the size of a configuration set and the outcome set relative to the size of the outcome set; unique coverage scores control for overlapping configuration sets by partitioning the raw coverage (Ragin, 2006). The raw coverage scores for the configurations range between 0.16 and 0.39 and the unique coverage scores range between 0.02 and 0.08.

Table 4. Configurations for High Market Performance

Configurations

Antecedents 1a 1b 2 3 4

Digital business strategy

Share of business BtB/BtCᵃ

Share of business services/goodsᵇ

Customer heterogeneity

Technological turbulence

Consistency 0.92 0.90 0.97 0.97 0.97

Raw coverage 0.39 0.39 0.24 0.27 0.16

Unique coverage 0.02 0.04 0.08 0.05 0.03

Overall solution consistency 0.91

Overall solution coverage 0.63

Notes: = presence of an antecedent; = negation of an antecedent; big circle = core element; small circle = peripheral element; blank space = subordinate antecedent; ᵃ presence indicates

focus on BtB markets; ᵇ presence indicates focus on selling services; Analysis thresholds: frequency = 3 (83% of the cases); raw consistency = 0.96; PRI consistency = 0.8; Intermediate and parsimonious solutions.

Configuration 1a includes the negation of a high share of business in selling services, the presence of high customer heterogeneity, and the presence of high technological turbulence. Digital business strategy and a share of business in BtB markets have a subordinate role in this configuration as indicated by the blank spaces. The presence of high customer heterogeneity and the negation of a high share of business in selling services are core conditions, high technological turbulence is a peripheral condition. Thus, configuration 1a involves firms that primarily sell goods and that face a heterogeneous customer base and a technologically turbulent environment. Configuration 1b encompasses the presence of a high share of business in BtB markets, the negation of a high share of business in selling services, and the presence of high customer heterogeneity. Digital business strategy and technological turbulence have a subordinate role. The focus on selling goods and the presence of high customer heterogeneity are core conditions in this configuration, whereas the focus on BtB markets is a peripheral factor. Thus, configuration 1b involves firms that primarily serve business customers, sell goods, and that face a heterogeneous customer base. Configuration 2 combines the negation of a well-established digital business strategy with the presence of a high share of business in BtB markets, and the presence of high technological turbulence. The focus on selling services and customer heterogeneity both have minor roles in this configuration. The negation of a well-established digital business strategy and the presence of high technological turbulence are core elements in this configuration and the focus on serving B2B markets is a peripheral factor. Configuration 2 thus involves firms that have no or an emerging digital business strategy, that primarily operate in BtB markets and that experience a high technological turbulence. Configuration 3 reveals that the presence of a well-established digital business strategy in combination with the presence of a high share of business in BtB markets, the negation of a high share of business in selling services, and the negation of high technological turbulence is a sufficient configuration for high



market performance. Customer heterogeneity has a minor role in this configuration. The presence of a well-established digital business strategy and the negation of high technological turbulence are core conditions in this configuration, whereas the focus on BtB markets and the selling of goods are peripheral conditions. This configuration covers digitalized businesses that primarily focus on BtB markets, sell goods and that operate in a technologically stable environment. Finally, configuration 4 shows that the presence of a well-established digital business strategy in combination with the negation of a high share of business in BtB markets, the presence of high customer heterogeneity, and the presence of technological turbulence contributes to high market performance. The focus on selling services has a subordinate role in this configuration. In configuration 4, all factors are core conditions. This configuration includes digitalized business that focus on B2C markets, that have a heterogeneous customer base, and that operate in a technologically turbulent environment with rapid technological changes.

Discussion

Business digitization has become an important topic in both management practice and academic research. The purpose of this research was to further illuminate and describe the conditions under which a digital business strategy can contribute to achieving strategic advantages in terms of superior market performance. The definition and implementation of a digital business strategy include “the design of products and services and their interoperability with other complementary platforms, and their deployment as products and services by taking advantage of digital resources” (Bharadwaj et al. 2013, p. 474). These activities typically involve dedicated investments, the development and alignment of organization capabilities and systems, and they oftentimes extend beyond firms’ boundaries, thus giving rise the question of when does digital business strategy matter to market performance?

Drawing on configuration theory (Ketchen et al. 1993; Meyer et al. 1993) and based on a configurational approach using fsQCA (Fiss 2011; Ragin 2008), we examined the interplay among digital business strategy, two dimensions of firms’ market approach (i.e., customer focus and offer focus), and environmental characteristics (i.e., heterogeneity of firms’ customer base and technological turbulence) in an attempt to analyze the complex causal patterns among these factors and to describe configurations sufficient for achieving high market performance. Our study thus responds to recent calls for research that advocate a configurational perspective to deepen the understanding of digital ecodynamics (El Sawy et al. 2010) and to calls for research on digital business strategy as a means for creating differential value (Bharadwaj et al. 2013). The findings of our analysis contribute to the literature by delineating four configurations sufficient for high market performance, with one configuration having two neutral permutations. These configurations are alternative pathways or “causal recipes” and represent equifinal routes to high market performance. Thus, and as proposed by configuration theory, we show equifinality (Doty and Glick 1994; Gresov and Drazin 1997) and the perseverance of multiple realities (Woodside 2014) for a key factor of business success. Knowledge of these configurations deepens the understanding of how factors covering three important domains (i.e., strategy, market approach, and environment) work together to achieve superior market performance. In regard to digital business strategy, the results indicate the opposing roles of this factor for achieving high market performance. Both the presence and the absence of a well-established digital business strategy within configurations contribute to high market performance, depending on how this antecedent combines with the additional organizational and environmental factors under investigation. In addition, in two configurations (i.e., 1a and 1b) a digital business strategy may be either present or absent.

For firms that primarily serve consumer markets, experience a heterogeneous customer base, and face frequent technological changes (e.g., online retailers), a digital business strategy contributes to outperforming competitors and achieving a superior market performance (configuration 4). This result corresponds to findings of prior configurational studies, which point to IT-enabled agility as a means for achieving high performance in turbulent environments (Park and El Sawy 2013). A digital business strategy, as reflected by top management commitment to digitization, the provision of digital solutions and interaction modes, and the use of digital processes and resources, may provide a means for coping with diverse customer preferences and environments in which technology changes rapidly by, for example, enabling customization of offerings and providing multiple customer touchpoints and ways to inform about and purchase products (e.g., via mobile devices). In addition, and as configuration 3 reveals, for firms that primarily serve business customers, offer goods, and experience a technologically stable



environment, a digital business strategy contributes to high market performance. In technologically stable business markets, radical tech-based innovations are unlikely to occur (Zhou et al. 2005), thus fostering firms to develop new approaches to create and capture differential value. A digital business strategy may then provide the basis for such approaches by enabling firms to develop and launch offering bundles that consist of existing products and digital solutions such as, for example, performance monitoring of machinery and predictive maintenance services. For example, John Deere, a manufacturer of farm machinery, offers several digital solutions for fleet management, machine health prognostics and diagnostics to optimize overall farm performance (Porter and Heppelmann 2014).

Besides these performance-enhancing effects of a digital business strategy, configuration 2 reveals a combination of factors including the negation of a well-established digital business strategy. This finding suggests that uniform predictions about the role of digital business strategy for achieving high market performance are likely misleading and that the impact of digital business strategy on market performance differs contingent on organizational and environmental factors. Prior studies with focus on firms’ agility and IT capability make similar observations and demonstrate that both the presence and the negation of these factors within configurations can produce high performance (Park and El Sawy 2013). Firms that primarily serve business customers and that operate in a technologically turbulent environment can achieve high market performance by having no or no well-established digital business strategy. In business markets characterized by rapidly changing technology, firms have little time to capitalize on and appropriate value from technological innovations and products. Because implementing a digital business strategy can increase system visibility and thus firms’ vulnerability to imitation, firms have to find a tradeoff between system visibility and value appropriation (Grover and Kohli 2013), which may explain the finding obtained by the fsQCA.

In summary, the results of our study contribute to the existing body of work on digital business strategy by embracing a configuration theoretical perspective and a configurational approach to capture and analyze complex causality as it is inherent to digital ecodynamics. The research framework outline here might serve as the basis for subsequent studies on digital business strategy. These studies should include additional antecedents and examine the consequences of configurations arising from these antecedents for market performance as well as for other facets of business success. For instance, it would be interesting to know digital business strategy and firms’ generic strategies (i.e., differentiation, cost-leadership, or niche strategy) work together and relate to market performance. In addition, more research is needed on when digital business strategy matters to firm profitability.

Besides these substantive contributions and the implications that derive for future studies, our research adds to the IS literature by pointing to fsQCA as a relatively novel method based on Boolean algebra. In contrast to commonly employed linear-algebraic methods (e.g., regression analysis or structural equation modeling) that offer insights into cross-case tendencies (Ragin 2008), fsQCA focuses on the explicit connections between antecedents and an outcome in terms of necessity and sufficiency. As such, it helps decompose correlations (Ragin 2008) and it can provide insights that may complement those obtained by linear-algebraic methods. Our research thus seeks to provide impetus for future research using fsQCA and for studies that employ Boolean-algebraic and linear-algebraic methods to deepen the understanding of phenomena of interest.

References

Achrol, R. S., and Stern, L. W. 1988. “Environmental determinants of decision-making uncertainty in marketing channels,” Journal of Marketing Research (25:1), pp. 36-50.

Armstrong, J. S., and Overton, T. S. 1977. “Estimating nonresponse bias in mail surveys,” Journal of Marketing Research (14:3), pp. 396-402.

Bagozzi, R. P., and Yi, Y. 1988. “On the evaluation of structural equation models,” Journal of the Academy of Marketing Science (16:1), pp. 74-94.

Bagozzi, R. P., Yi, Y., and Phillips, L. W. 1991. “Assessing construct validity in organizational research,” Administrative Science Quarterly (36:3), pp. 421-458.

Bhansali, S., and Brynjolfsson, E. 2007. “Digitizing work: Driving and measuring changes in information worker time use and performance via a longitudinal quasi-experiment,” in Proceedings of the 2007 International Conference on Information Systems, Montreal, Quebec, Canada.



Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., and Venkatraman, N. 2013. “Digital business strategy: Toward a next generation of insights,” MIS Quarterly (37:2), pp. 471-482.

Biesdorf, S., and Niedermann, F. 2014. “Healthcare’s digital future,” McKinsey Quarterly (July), 2014. Brown, B. P., Bellenger, D. N., and Johnston, W. J. 2007. “The implications of business-to-business and

consumer market differences for B2B branding strategy,” Journal of Business Market Management (1:3), pp. 209-230.

Croteau, A.-M., and Raymond, L. 2004. “Performance outcomes of strategic and IT competencies alignment,” Journal of Information Technology (19:3), pp. 178-190.

Dess, G. G., Newport, S., and Rasheed, A. M. 1993. “Configuration research in strategic management: Key issues and suggestions,” Journal of Management (19:4), pp. 775-795.

Doty, D. H., and Glick, W. H. 1994. “Typologies as a unique form of theory building: Toward improved understanding and modeling,” Academy of Management Review (19:2), pp. 230-251.

Drnevich, P. L., and Croson, D. C. 2013. “Information technology and business-level strategy: Toward an integrated theoretical perspective,” MIS Quarterly (37:2), pp. 483-509.

Dwyer, F. R., and Welsh, M. A. 1985. “Environmental Relationships of the Internal Political Economy of Marketing Channels,” Journal of Marketing Research (22:4), pp. 397-414.

El Sawy, O. A., Malhotra, A., Park, Y., and Pavlou, P. A. (2010). “Research commentary-Seeking the configurations of digital ecodynamics: It takes three to tango,” Information Systems Research (21:4), pp. 835-848.

Fern, E. F., and Brown, J. R. 1984. “The industrial/consumer marketing dichotomy: A case of insufficient justification,” Journal of Marketing (48:2), pp. 68-77.

Fiss, P. C. 2011. “Building better causal theories: A fuzzy set approach to typologies in organization research,” Academy of Management Journal (54:2), pp. 393-420.

Fornell, C., and Larcker, D. F. 1981. “Evaluating structural equation models with unobservable variables and measurement error,” Journal of Marketing Research (18:1), pp. 39-50.

Grandori, A., and Furnari, S. 2008. “A chemistry of organizations: Combinatory analysis and design,” Organization Studies (29:3), pp. 459-485.

Greckhamer, T., Misangyi, V. F., Elms, H., and Lacey, R. 2008. “Using QCA in strategic management research: An examination of combinations of industry, corporate, and business unit effects,” Organizational Research Methods (11:4), pp. 695-726.

Greckhamer, T., Misangyi, V. F., and Fiss, P. C. 2013. “Chapter 3 The two QCAs: From a small-N to a large-N set theoretic approach,” in Configurational theory and methods in organizational research, P. Fiss, B. Cambré, and A. Marx (eds.), Bingley: Emerald, pp. 49-75.

Gresov, C., and Drazin, R. 1997. “Equifinality: Functional equivalence in organization design,” Academy of Management Review (22:2), pp. 403-428.

Grover, V., and Kohli, R. 2013. “Revealing your hand: Caveats in implementing digital business strategy,” MIS Quarterly (37:2), pp. 655-662.

Grünbaum, B. 1975. “Venn diagrams and independent families of sets,” Mathematics Magazine (48:1), pp. 12-23.

Gunasekaran, A., Marri, H. B., McGaughey, R. E., and Nebhwani, M. D. 2002. “E-commerce and its impact on operations management,” International Journal of Production Economics (75:1-2), pp. 185-197.

Homburg, C., Workman Jr, J. P., and Krohmer, H. 1999. “Marketing’s influence within the firm,” Journal of Marketing (63:2), pp. 1-17.

Jaworski, B. J., and Kohli, A. K. 1993. “Market orientation: Antecedents and consequences,” Journal of Marketing (57:3), pp. 53-70.

Johnson, D. S., and Bharadwaj, S. 2005. “Digitization of selling activity and sales force performance: An empirical investigation,” Journal of the Academy of Marketing Science (33:1), pp. 3-18.

Kathuria, A., Andrade Rojas, M., Saldanha, T., and Khuntia, J. 2014. “Extent versus range of service digitization: Implications for firm performance,” in Proceedings of 20th Americas Conference on Information Systems, Savannah, 2014

Katz, D., and Kahn, R. L. 1978. The social psychology of organizations (2nd ed.). New York: Wiley. Ketchen, D. J., Thomas, J. B., and Snow, C. C. 1993. “Organizational configurations and performance: A

comparison of theoretical approaches,” Academy of Management Journal (36:6), pp. 1278-1313. Ketchen, D. J., Combs, J. G., Russell, C. J., Shook, C., Dean, M. A., Runge, J., ... and Beckstein, B. A. 1997.

“Organizational configurations and performance: A meta-analysis,” Academy of Management Journal (40:1), pp. 223-240.



Kohli, R., and Grover, V. 2008. “Business value of IT: An essay on expanding research directions to keep up with the times,” Journal of the Association for Information Systems (9:1), pp. 23-39.

Leischnig, A., Henneberg, S. C., and Thornton, S. C. 2016. “Net versus combinatory effects of firm and industry antecedents of sales growth,” Journal of Business Research forthcoming.

Malhotra, N. K., Kim, S. S., and Patil, A. 2006. “Common method variance in IS research: A comparison of alternative approaches and a reanalysis of past research,” Management Science (52:12), pp. 1865-1883.

Melville, N., Kraemer, K., and Gurbaxani, V. 2004. “Review: Information technology and organizational performance: An integrative model of IT business value,” MIS Quarterly (28:2), pp. 283-322.

Meyer, A. D., Tsui, A. S., and Hinings, C. R. 1993. Configurational approaches to organizational analysis,” Academy of Management Journal (36:6), pp. 1175-1195.

Miller, D. 1996. “Configurations revisited,” Strategic Management Journal (17:7), pp. 505-512. Misangyi, V. F., and Acharya, A. G. 2014. “Substitutes or complements? A configurational examination of

corporate governance mechanisms,” Academy of Management Journal (57:6), pp. 1681-1705. Mithas, S., Tafti, A., and Mitchell, W. 2013. “How a firm’s competitive environment and digital strategic

posture influence digital business strategy,” MIS Quarterly (37:2), pp. 511-536. Nunnally, J. 1978. Psychometric methods, New York: McGraw-Hill. Park, Y., and El Sawy, O. A. 2013. “The value of configurational approaches for studying digital business

strategy,” in Configurational theory and methods in organizational research, P. Fiss, B. Cambré, and A. Marx (eds.), Bingley: Emerald, pp. 205-224.

Pavlou, P. A., and El Sawy, O. A. 2006. “From IT leveraging competence to competitive advantage in turbulent environments: The case of new product development,” Information Systems Research (17:3), pp. 198-227.

Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., and Podsakoff, N. P. 2003. “Common method biases in behavioral research: A critical review of the literature and recommended remedies,” Journal of Applied Psychology (88:5), pp. 879-903.

Podsakoff, P. M., and Organ, D. W. 1986. “Self-reports in organizational research: Problems and prospects,” Journal of Management (12:4), pp. 531-544.

Porter, M. E., and Heppelmann, J. E. 2014. “How smart, connected products are transforming competition,” Harvard Business Review (92:11), pp. 64-88.

Ragin, C. C. 2006. “Set relations in social research: Evaluating their consistency and coverage,” Political Analysis (14:3), pp. 291-310.

Ragin, C. C. 2008. Redesigning social inquiry: Fuzzy sets and beyond. Chicago: University of Chicago Press.

Ragin, C. C., Drass, K. A., and Davey, S. 2006. Fuzzy-set/qualitative comparative analysis, Tucson, AZ: Department of Sociology, University of Arizona.

Ragin, C. C., and Fiss, P.C. 2008. “Net effects analysis versus configurational analysis: An empirical demonstration,” in Redesigning social inquiry: Fuzzy sets and beyond, C. C. Ragin (ed.), Chicago: University of Chicago Press, pp. 190-212.

Rust, R. T., and Espinoza, F. 2006. “How technology advances influence business research and marketing strategy,” Journal of Business Research (59:10/11), pp. 1072-1078.

Sambamurthy, V., Bharadwaj, A., and Grover, V. 2003. “Shaping agility through digital options: Reconceptualizing the role of information technology in contemporary firms,” MIS Quarterly (27:2), pp. 237-263.

Schneider, C. Q., and Wagemann, C. 2010. “Standards of good practice in qualitative comparative analysis (QCA) and fuzzy-sets,” Comparative Sociology (9:3), pp. 397-418.

Schneider, C. Q., and Wagemann, C. 2012. Set-theoretic methods for the social sciences: A guide to qualitative comparative analysis, Cambridge, UK: Cambridge University Press.

Schneider, M. R., Schulze-Bentrop, C., and Paunescu, M. 2010. “Mapping the institutional capital of high-tech firms: A fuzzy-set analysis of capitalist variety and export performance,” Journal of International Business Studies (41:2), pp. 246-266.

Setia, P., Venkatesh, V., and Joglekar, S. 2013. “Leveraging digital technologies: How information quality leads to localized capabilities and customer service performance,” MIS Quarterly (37:2), pp. 565-590.

Slater, S. F., and Narver, J. C. 1994. “Does competitive environment moderate the market orientation-performance relationship?,” Journal of Marketing (58:1), pp. 46-55.

Slywotzky, A. J., Morrison, D. J., and Weber, K. 2000. How digital is your business?, New York: Crown Business.



Tan, C. W., Benbasat, I., and Cenfetelli, R. T. 2016. “An exploratory study of the formation and impact of electronic service failures,” MIS Quarterly (40:1), pp. 1-29.

Thiem, A., Baumgartner, M., and Bol, D. 2016. “Still lost in translation! A correction of three misunderstandings between configurational comparativists and regressional analysts,” Comparative Political Studies (49:6), pp. 742-775.

Tuli, K. R., Kohli, A. K., and Bharadwaj, S. G. 2007. “Rethinking customer solutions: From product bundles to relational processes,” Journal of Marketing (71:3), pp. 1-17.

Verhoef, P. C., and Leeflang, P. S. 2009. “Understanding the marketing department’s influence within the firm,” Journal of Marketing (73:2), pp. 14-37.

Vorhies, D. W., and Morgan, N. A. 2003. “A configuration theory assessment of marketing organization fit with business strategy and its relationship with marketing performance,” Journal of Marketing (67:1), pp. 100-115.

Vorhies, D. W., and Morgan, N. A. 2005. “Benchmarking marketing capabilities for sustainable competitive advantage,” Journal of Marketing (69:1), pp. 80-94.

Wenig, D. 2014. “How digital is transforming retail: The view from eBay,” McKinsey Quarterly (July), 2014.

Woodside, A. G. 2014. “Embrace • perform • model: Complexity theory, contrarian case analysis, and multiple realities,” Journal of Business Research (67:12), pp. 2495-2503.

Zeithaml, V. A., Parasuraman, A., and Berry, L. L. 1985. “Problems and strategies in services marketing,” Journal of Marketing (49:2), pp. 33-46.

Zhou, K. Z., Yim, C. K., and Tse, D. K. 2005. “The effects of strategic orientations on technology- and market-based breakthrough innovations,” Journal of Marketing (69:2), pp. 42-60.

Documents

When Does Digital Business Strategy Matter to …...antecedents include concepts of three important domains encompassing strategy (i.e., digital business strategy), market approach