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How to improve firm performance using big data analytics capability and business strategy alignment?
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“The results also illuminate the significant moderating impact of analytics capability-business strategy alignment on the BDAC -FPER relationship.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > Abstract
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“The review identified three primary dimensions that reflect BDAC, that is, BDA management capability, BDA technology capability and BDA talent capability (see Table 1).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 3 RESEARCH MODEL AND HYPOTHESES In order to develop a research model to measure BDAC, this study began by investigating commonly cited dimensions that influence BDAC perception in big data environment (Alismaili et al., 2016).The review identified three primary dimensions that reflect BDAC, that is, BDA management capability, BDA technology capability and BDA talent capability (see Table 1).Throughout our review and theoretical exploration, BDAC was frequently identified as a higher-order and multidimensional construct, which indicated that several subdimensions would determine the initially identified primary dimensions.As such, we conducted two Delphi studies to explore the subdimensions of BDAC under each primary dimension identified in the review.Round one of the Delphi study was conducted in November 2014 (n=51) and round two in February 2015 (n=43) with respondents that represent balance of analytics practitioners, consultants and academics.Using these two studies, we found support for 11 subdimensions (i.e., BDA planning, investment, coordination, control, connectivity, compatibility, modularity, technical knowledge, technology management knowledge, business knowledge, and relational knowledge) under three primary dimensions (i.e., management capability, infrastructure capability and talent capability) proposed in the research model (see Fig. 1).Drawing on the RBT and entanglement view, the research model conceptualizes BDAC dimensions as having the attributes of complementarity and co-specialization, which work together in a synergistic fashion to achieve distinctive firm performance (Clemons and Row, 1991;Kim et al., 2012;Powell and Dent-Micallef, 1997;Tippins and Sohi, 2003).To evaluate how RBT unifies entanglement view of sociomaterialism to support our BDAC model, we review each theory's complementarities in Table 5. Table 5: RBT and Entanglement view of Sociomaterialism: Similarities & Complementarities to support the BDAC model Theory Key ideas Similarities with the BDAC model Complements to the BDAC model
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In the paper
“The findings show BDAC as a hierarchical model, which consists of three primary dimensions (i.e., management, technology, and talent capability) and 11 subdimensions (i.e., planning, investment, coordination, control, connectivity, compatibility, modularity, technology management knowledge, technical knowledge, business knowledge and relational knowledge).”
- Also stated in: § 3 RESEARCH MODEL AND HYPOTHESES In order to develop a research model to measure BDAC, this study began by investigating commonly cited dimensions that influence BDAC perception in big data environment (Alismaili et al., 2016).The review identified three primary dimensions that reflect BDAC, that is, BDA management capability, BDA technology capability and BDA talent capability (see Table 1).Throughout our review and theoretical exploration, BDAC was frequently identified as a higher-order and multidimensional construct, which indicated that several subdimensions would determine the initially identified primary dimensions.As such, we conducted two Delphi studies to explore the subdimensions of BDAC under each primary dimension identified in the review.Round one of the Delphi study was conducted in November 2014 (n=51) and round two in February 2015 (n=43) with respondents that represent balance of analytics practitioners, consultants and academics.Using these two studies, we found support for 11 subdimensions (i.e., BDA planning, investment, coordination, control, connectivity, compatibility, modularity, technical knowledge, technology management knowledge, business knowledge, and relational knowledge) under three primary dimensions (i.e., management capability, infrastructure capability and talent capability) proposed in the research model (see Fig. 1).Drawing on the RBT and entanglement view, the research model conceptualizes BDAC dimensions as having the attributes of complementarity and co-specialization, which work together in a synergistic fashion to achieve distinctive firm performance (Clemons and Row, 1991;Kim et al., 2012;Powell and Dent-Micallef, 1997;Tippins and Sohi, 2003).To evaluate how RBT unifies entanglement view of sociomaterialism to support our BDAC model, we review each theory's complementarities in Table 5. Table 5: RBT and Entanglement view of Sociomaterialism: Similarities & Complementarities to support the BDAC model Theory Key ideas Similarities with the BDAC model Complements to the BDAC model · plain
“Using these two studies, we found support for 11 subdimensions (i.e., BDA planning, investment, coordination, control, connectivity, compatibility, modularity, technical knowledge, technology management knowledge, business knowledge, and relational knowledge) under three primary dimensions (i.e., management capability, infrastructure capability and talent capability) proposed in the research model”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > Abstract
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- Also stated in: § 3 RESEARCH MODEL AND HYPOTHESES In order to develop a research model to measure BDAC, this study began by investigating commonly cited dimensions that influence BDAC perception in big data environment (Alismaili et al., 2016).The review identified three primary dimensions that reflect BDAC, that is, BDA management capability, BDA technology capability and BDA talent capability (see Table 1).Throughout our review and theoretical exploration, BDAC was frequently identified as a higher-order and multidimensional construct, which indicated that several subdimensions would determine the initially identified primary dimensions.As such, we conducted two Delphi studies to explore the subdimensions of BDAC under each primary dimension identified in the review.Round one of the Delphi study was conducted in November 2014 (n=51) and round two in February 2015 (n=43) with respondents that represent balance of analytics practitioners, consultants and academics.Using these two studies, we found support for 11 subdimensions (i.e., BDA planning, investment, coordination, control, connectivity, compatibility, modularity, technical knowledge, technology management knowledge, business knowledge, and relational knowledge) under three primary dimensions (i.e., management capability, infrastructure capability and talent capability) proposed in the research model (see Fig. 1).Drawing on the RBT and entanglement view, the research model conceptualizes BDAC dimensions as having the attributes of complementarity and co-specialization, which work together in a synergistic fashion to achieve distinctive firm performance (Clemons and Row, 1991;Kim et al., 2012;Powell and Dent-Micallef, 1997;Tippins and Sohi, 2003).To evaluate how RBT unifies entanglement view of sociomaterialism to support our BDAC model, we review each theory's complementarities in Table 5. Table 5: RBT and Entanglement view of Sociomaterialism: Similarities & Complementarities to support the BDAC model Theory Key ideas Similarities with the BDAC model Complements to the BDAC model · plain
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In the paper
“For the interaction model, the results provide us a standardized beta of 0.261 for BDAC-FPER (p < 0.01), 0.542 for ACBSA-FPER (p < 0.01) and 0.153 (p < 0.05) for BDAC*ACBSA-FPER respectively.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 5.3 Structural Model
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“In addition, BDA management capability (BDAMAC) was identified as a significant dimension indicating that achieving sustainable competitive advantage with analytics relies heavily on decision makers.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 6 Discussion
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“Similarly, BDATEC was explained by BDACN (75%), BDACM (85%), and BDAMD (81%).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 5.2 Higher-Order Measurement Model
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“Finally, BDATLC was explained by BDATM (86%), BDATK (89%), BDABK (87%), and BDARK (88%).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 5.2 Higher-Order Measurement Model
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In the paper
“For the interaction model, the results provide us a standardized beta of 0.261 for BDAC-FPER (p < 0.01), 0.542 for ACBSA-FPER (p < 0.01) and 0.153 (p < 0.05) for BDAC*ACBSA-FPER respectively.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 5.3 Structural Model
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“The findings confirm a superior prediction power of the interaction model, which is reflected in firm performance (Δ R 2 = 0.093, f 2 = 0.23, p<0.01).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 5.3 Structural Model
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“The interaction model explained around 60% of the variance.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 6 Discussion
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“The findings confirm the value of the entanglement conceptualization of the higher-order BDAC model and its impact on FPER.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > Abstract
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“Three core themes underpin perceptions of BDATEC: connectivity, compatibility and modularity.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 3.2 BDA technology capability (BDATEC) BDATEC refers to the flexibility of the BDA platform (e.g., connectivity of cross-functional data, compatibility of multiple platforms, modularity in model building, etc.) in relation to enabling data scientists to quickly develop, deploy, and support a firm's resources.Three core themes underpin perceptions of BDATEC: connectivity, compatibility and modularity.It is important to tackle volatile business conditions (e.g., changes in competition, market dynamics, or consumer behaviour) and align resources with long-term and short-term business strategies (e.g., new product development, diversification, etc.).With a flexible BDATEC, firms can source and connect various data points from remote, branch, and mobile offices; create compatible data-sharing channels across various functions; and develop models and applications to address changing needs.Thus, the flexibility of a firm's BDAC depends on two components: the first component is connectivity among different business units in sourcing and analyzing a variety of data from different functions (e.g., supply chain management, customer relationship management, etc.).For example, banks in the big data environment often improve customer service operations by combining data from automated teller machine (ATM) transactions, online queries, social media comments, and customer complaints (Barton and Court, 2012).The second component, compatibility, enables continuous flows of information for real-time decisions.It also helps clean-up operations to synchronize and merge overlapping data and to fix missing information.For example, Amazon embraces compatibility in the BDAC platform by using cloud technologies which help in collaboration, experimentation, and rapid analysis (Davenport and Harris, 2007a). Modularity embodies flexible platform development which allows the addition, modification or removal of features to, or from, the model as needed.It helps in tapping business opportunities and improving FPER.
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“Four core themes were found to constitute perceptions of BDAMAC; these were termed as BDA planning, investment, coordination, and control.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 3.1 BDA management capability (BDAMAC) BDAMAC is an important aspect of BDAC ensuring that solid business decisions are made applying proper management framework.Four core themes were found to constitute perceptions of BDAMAC; these were termed as BDA planning, investment, coordination, and control.The BDAMAC starts with the proper BDA planning process which identifies business opportunities and determines how the big data-based models can improve firm performance (FPER) (Barton and Court, 2012).For example, Amazon planned to engage a type of predictive modelling technique called 'collaborative filtering' using customer data to generate 'you might also want' prompts for each product bought or visited.Amazon revealed at one point that 30% of sales were generated through its recommendation engine (Manyika et al., 2011).Similarly, BDA investment decisions are critical aspects of BDAMAC as they reflect cost-benefit analyses.For example, Netflix Inc. transformed its BDAC by investing in web data of over one billion movie reviews in categories such as liked, loved, hated, etc. to recommend movies that optimize the ability to meet customer preferences (Davenport and Harris, 2007b).According to Ramaswamy (2013), "[w]e found that companies with huge investments in Big Data are generating excess returns and gaining competitive advantages, putting companies without significant investments in Big Data at risk".Thus, it is important to manage this capability in order to enhance revenue-generating activities, as have been applied by Netflix, General Electric, and LinkedIn, to drive growth.In addition, BDA coordination receives increased attention in the big data environment, representing a form of routine capability that structures the cross-functional synchronization of analytics activities across the firm (Kiron et al., 2014).For example, analysts of Procter & Gamble work in coordination across operations, the supply chain, sales, consumer research, and marketing to improve total business performance (Davenport, 2006).Finally, BDA controlling functions are performed by ensuring proper commitment and utilization of resources, including budgets and human resources.For example, the controlling functions in Amazon represent an evaluation of BDA proposals with reference to BDA plans, clarification of the responsibilities of the BDA unit, development of performance criteria for BDA, and continuous performance monitoring of the BDA unit (Schroeck et al., 2012).
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“BDA technology capability (BDATEC) was identified as a key predictor of BDAC, emphasizing the need for versatility of the analytics platform so that it connects data from various functions across the firm, ensures information flow, and develops robust models.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 6 Discussion
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“Similarly, we investigated the impact of control variables (COVA) on FPER, however, the results show insignificant impact of COVA as the R 2 change is very small after including this construct in the model.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 5.3 Structural Model
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“According to the guidelines of Cohen (1988), the size of the moderating effect is medium (f 2 =0.23) but significant at p<0.05.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 5.3 Structural Model
What the sources say
- Not checked yetcites Cohen, 1988
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“The degree of variance of the third-order BDAC construct was explained by its second-order antecedents, that is, BDAMAC (88%), BDATEC (83%), and BDATLC (90%).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 5.2 Higher-Order Measurement Model
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In the paper
“The results provide a standardized beta of 0.709 for the BDAC-FPER path in the main, thus supporting H1.”
- Also stated in: § 6 Discussion · boosted
“Overall, the findings of the structural model confirm that BDAC is a significant predictor of FPER (explaining 50% of the variance).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 5.3 Structural Model
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- Rests on an assumption: The observed associations between BDAC, ACBSA and FPER reflect causal effects rather than mere association.
- Also stated in: § 6 Discussion · boosted
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In the paper
“For example, the degree of explained variance of BDAMAC was explained by BDAPL (82%), BDAID (79%), BDACO (72%), and BDACT (85%).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 5.2 Higher-Order Measurement Model
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“Big data analytics capability (BDAC) was found to have a positive association with all the primary dimensions with BDA talent capability (BDATLC) emerging as the strongest.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 6 Discussion
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In the paper
“The empirical findings of our study answer this question, and are consistent with the conceptual findings ofKiron et al. (2014, p.10) who state that”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > Reflective ACBSA4
What the sources say
- Not checked yetcites Kiron et al., 2014
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In the paper
“Secondly, despite the paucity of empirical modeling in big data research, our research extends this stream by conceptualizing a multidimensional BDAC model drawing on the RBT and sociomaterialism which substantiate the fact that BDAC is a hierarchical construct having a strong influence on firm performance (FPER).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > Reflective ACBSA4
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In the paper
“The findings therefore contribute to answering”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > Reflective ACBSA4
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In the paper
“Based on the RBT, some scholars propose that internal business processes could be important factors linking BDAC and firm performance (FPER) (Dehning and Richardson, 2002;Melville et al., 2004).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 1 Introduction
What the sources say
- Not checked yetcites Dehning and Richardson, 2002
- Not checked yetcites Melville et al., 2004
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In the paper
“As a result of greater synchronization in the big data environment, it is possible to leverage BDAC by overcoming cognitive, structural and political challenges.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 3.5 Analytics Capability, Business Strategy Alignment and Firm Performance
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In the paper
“Rather, the study embraces the relational ontology of sociomaterialism which posits that the organizational (i.e., BDA management), physical (i.e., IT infrastructure), and human (e.g., analytics skill or knowledge) dimensions are so interwoven that it is difficult to measure their individual contributions in isolation (Orlikowski and Scott, 2008).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 2.2.3 Entanglement view of Sociomaterialism In addition to the RBT, the current study's theoretical framework is based on the concept of sociomateriality which refers to the ontological integration of social and material.This viewpoint does not show that the material influences the social (i.e., technological determinism view) or the social influences the material (i.e., social construction view), or a recursive relationship between the social and material (i.e., socio-technical view).Rather, the study embraces the relational ontology of sociomaterialism which posits that the organizational (i.e., BDA management), physical (i.e., IT infrastructure), and human (e.g., analytics skill or knowledge) dimensions are so interwoven that it is difficult to measure their individual contributions in isolation (Orlikowski and Scott, 2008).Orlikowski (2007Orlikowski ( , p.1437) ) clarifies that "the social and the material are inextricably related".With this conceptualization, we argue that the BDAC dimensions do not act in isolation; rather, they act together.This view also posits that no properties are native to each constituent dimension because BDAC dimensions are constitutively entangled (Orlikowski, 2007) and mutually supportive (Barton and Court, 2012).Indeed, the individual capability dimension is the manifestation of the overall BDAC building blocks as a whole.The study presents the summary of entanglement view of sociomaterialism in Table 3, which indicates that reality does not represent independent objects (social or material), but the joint agency of both. Table 3: Foundations of entanglement view using sociomaterialism Foundations of Entanglement view Definitions using sociomateriality (Latour, 2005;Orlikowski, 2007;Orlikowski and Scott, 2008;Stein et al., 2014) Ontology Human and non-human are inextricably entangled to work together.
What the sources say
- Not checked yetcites Orlikowski and Scott, 2008 source
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In the paper
“Overall, capabilities represent a special type of resources whose objective is to increase productivity of other resources possessed by the firm (Morgan et al., 2009).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > Organization
What the sources say
- Not checked yetcites Morgan et al., 2009
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In the paper
“In addition to these two assumptions, the logic of RBT embraces the VRIO framework which clearly states that firm performance depends on the extent to which a firm possesses simultaneously valuable (V), rare (R), imperfectly imitable (I) resources which are properly organized (O) (Amit and Schoemaker, 1993;Barney et al., 2001).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 2.2.1 Resource based theory (RBT) The RBT relies on two core assumptions about firm-based resources to show why some firms perform better than others and how to enhance firm performance.First, even when firms operate within the same industry, they possess a varied mixture of resources (Peteraf and Barney, 2003).This assumption of resource heterogeneity indicates the capability of some firms in accomplishing certain functions with the help of their unique resources.Second, these differences in resources are facilitated by the difficulty of exchanging resources across firms.This assumption indicates resource immobility which highlights the fact that the synergistic benefits from various resources are sustained over time (Barney and Hesterly, 2012).In addition to these two assumptions, the logic of RBT embraces the VRIO framework which clearly states that firm performance depends on the extent to which a firm possesses simultaneously valuable (V), rare (R), imperfectly imitable (I) resources which are properly organized (O) (Amit and Schoemaker, 1993;Barney et al., 2001).First, the valuable dimension of resources enables a firm to enhance net revenues and reduce net costs (Barney and Arikan, 2001), which in other words helps firms capitalize upon an opportunity and minimize a threat (Barney and Hesterly, 2012).Second, the rare dimension indicates that the resources are possessed by a small number of firms to achieve competitive advantages.Third, the imperfectly imitable dimension suggests that firms cannot directly copy or substitute such resources because they are costly to imitate.Research suggests that resource complementarity among resources within a firm make it difficult for competitors to duplicate (Morgan et al., 2009).Resource complementarity occurs when the presence of one resource enables another to leverage firm performance.Finally, the organization dimension focuses on the proper management of valuable, rare and imperfectly imitable resources to leverage their full competitive potential (Barney and Clark, 2007).Resources are defined as tangible and intangible assets used by the firms to conceive of and implement its strategies. A subset of resources, which represent an "organizationally embedded non-transferable firm-specific resource whose purpose is to improve the productivity of the other resources possessed by the firm" (p.389).
What the sources say
- Not checked yetcites Amit and Schoemaker, 1993 source
- Not checked yetcites Barney et al., 2001
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The second component, compatibility, enables continuous flows of information for real-time decisions.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 3.2 BDA technology capability (BDATEC) BDATEC refers to the flexibility of the BDA platform (e.g., connectivity of cross-functional data, compatibility of multiple platforms, modularity in model building, etc.) in relation to enabling data scientists to quickly develop, deploy, and support a firm's resources.Three core themes underpin perceptions of BDATEC: connectivity, compatibility and modularity.It is important to tackle volatile business conditions (e.g., changes in competition, market dynamics, or consumer behaviour) and align resources with long-term and short-term business strategies (e.g., new product development, diversification, etc.).With a flexible BDATEC, firms can source and connect various data points from remote, branch, and mobile offices; create compatible data-sharing channels across various functions; and develop models and applications to address changing needs.Thus, the flexibility of a firm's BDAC depends on two components: the first component is connectivity among different business units in sourcing and analyzing a variety of data from different functions (e.g., supply chain management, customer relationship management, etc.).For example, banks in the big data environment often improve customer service operations by combining data from automated teller machine (ATM) transactions, online queries, social media comments, and customer complaints (Barton and Court, 2012).The second component, compatibility, enables continuous flows of information for real-time decisions.It also helps clean-up operations to synchronize and merge overlapping data and to fix missing information.For example, Amazon embraces compatibility in the BDAC platform by using cloud technologies which help in collaboration, experimentation, and rapid analysis (Davenport and Harris, 2007a). Modularity embodies flexible platform development which allows the addition, modification or removal of features to, or from, the model as needed.It helps in tapping business opportunities and improving FPER.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“Second, these differences in resources are facilitated by the difficulty of exchanging resources across firms.This assumption indicates resource immobility which highlights the fact that the synergistic benefits from various resources are sustained over time (Barney and Hesterly, 2012).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 2.2.1 Resource based theory (RBT) The RBT relies on two core assumptions about firm-based resources to show why some firms perform better than others and how to enhance firm performance.First, even when firms operate within the same industry, they possess a varied mixture of resources (Peteraf and Barney, 2003).This assumption of resource heterogeneity indicates the capability of some firms in accomplishing certain functions with the help of their unique resources.Second, these differences in resources are facilitated by the difficulty of exchanging resources across firms.This assumption indicates resource immobility which highlights the fact that the synergistic benefits from various resources are sustained over time (Barney and Hesterly, 2012).In addition to these two assumptions, the logic of RBT embraces the VRIO framework which clearly states that firm performance depends on the extent to which a firm possesses simultaneously valuable (V), rare (R), imperfectly imitable (I) resources which are properly organized (O) (Amit and Schoemaker, 1993;Barney et al., 2001).First, the valuable dimension of resources enables a firm to enhance net revenues and reduce net costs (Barney and Arikan, 2001), which in other words helps firms capitalize upon an opportunity and minimize a threat (Barney and Hesterly, 2012).Second, the rare dimension indicates that the resources are possessed by a small number of firms to achieve competitive advantages.Third, the imperfectly imitable dimension suggests that firms cannot directly copy or substitute such resources because they are costly to imitate.Research suggests that resource complementarity among resources within a firm make it difficult for competitors to duplicate (Morgan et al., 2009).Resource complementarity occurs when the presence of one resource enables another to leverage firm performance.Finally, the organization dimension focuses on the proper management of valuable, rare and imperfectly imitable resources to leverage their full competitive potential (Barney and Clark, 2007).Resources are defined as tangible and intangible assets used by the firms to conceive of and implement its strategies. A subset of resources, which represent an "organizationally embedded non-transferable firm-specific resource whose purpose is to improve the productivity of the other resources possessed by the firm" (p.389).
What the sources say
- Not checked yetcites Barney and Hesterly, 2012
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Due to the unpredictable nature of big data, strategy researchers have always emphasized establishing the strategic fit or alignment, viewing the firm as a collection of resources, interlinked by a specific governance structure (Peteraf, 1993).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 3.5 Analytics Capability, Business Strategy Alignment and Firm Performance
What the sources say
- Not checked yetcites Peteraf, 1993
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It helps in tapping business opportunities and improving FPER.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 3.2 BDA technology capability (BDATEC) BDATEC refers to the flexibility of the BDA platform (e.g., connectivity of cross-functional data, compatibility of multiple platforms, modularity in model building, etc.) in relation to enabling data scientists to quickly develop, deploy, and support a firm's resources.Three core themes underpin perceptions of BDATEC: connectivity, compatibility and modularity.It is important to tackle volatile business conditions (e.g., changes in competition, market dynamics, or consumer behaviour) and align resources with long-term and short-term business strategies (e.g., new product development, diversification, etc.).With a flexible BDATEC, firms can source and connect various data points from remote, branch, and mobile offices; create compatible data-sharing channels across various functions; and develop models and applications to address changing needs.Thus, the flexibility of a firm's BDAC depends on two components: the first component is connectivity among different business units in sourcing and analyzing a variety of data from different functions (e.g., supply chain management, customer relationship management, etc.).For example, banks in the big data environment often improve customer service operations by combining data from automated teller machine (ATM) transactions, online queries, social media comments, and customer complaints (Barton and Court, 2012).The second component, compatibility, enables continuous flows of information for real-time decisions.It also helps clean-up operations to synchronize and merge overlapping data and to fix missing information.For example, Amazon embraces compatibility in the BDAC platform by using cloud technologies which help in collaboration, experimentation, and rapid analysis (Davenport and Harris, 2007a). Modularity embodies flexible platform development which allows the addition, modification or removal of features to, or from, the model as needed.It helps in tapping business opportunities and improving FPER.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Drawing on the RBT, the literature in IT capabilities recognizes that competence in leveraging IT-based resources is a source of competitive advantage and differentiates firm performance (Bharadwaj, 2000a;Piccoli and Ives, 2005).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 2.2.2 IT capability theories using RBT
What the sources say
- Not checked yetcites Bharadwaj, 2000a
- Not checked yetcites Piccoli and Ives, 2005
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Since IT is acknowledged as a critical component of BDAC, drawing on the IT capability literature, we argue that competence in mobilizing and deploying various BDAC resources differentiates firm performance (FPER) and creates competitive advantage (Piccoli and Ives, 2005).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > First-Order Model
What the sources say
- Not checked yetcites Piccoli and Ives, 2005
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Previous research also highlights the importance of analytics capability-business strategy alignment (ACBSA) in big data environment, which is defined as the extent to which analytics strategies are aligned with the overall business strategy of the organization (Agarwal and Dhar, 2014;McAfee and Brynjolfsson, 2012).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 1 Introduction
What the sources say
- Not checked yetcites Agarwal and Dhar, 2014
- Not checked yetcites McAfee and Brynjolfsson, 2012
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, the flexibility of a firm's BDAC depends on two components: the first component is connectivity among different business units in sourcing and analyzing a variety of data from different functions (e.g., supply chain management, customer relationship management, etc.).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 3.2 BDA technology capability (BDATEC) BDATEC refers to the flexibility of the BDA platform (e.g., connectivity of cross-functional data, compatibility of multiple platforms, modularity in model building, etc.) in relation to enabling data scientists to quickly develop, deploy, and support a firm's resources.Three core themes underpin perceptions of BDATEC: connectivity, compatibility and modularity.It is important to tackle volatile business conditions (e.g., changes in competition, market dynamics, or consumer behaviour) and align resources with long-term and short-term business strategies (e.g., new product development, diversification, etc.).With a flexible BDATEC, firms can source and connect various data points from remote, branch, and mobile offices; create compatible data-sharing channels across various functions; and develop models and applications to address changing needs.Thus, the flexibility of a firm's BDAC depends on two components: the first component is connectivity among different business units in sourcing and analyzing a variety of data from different functions (e.g., supply chain management, customer relationship management, etc.).For example, banks in the big data environment often improve customer service operations by combining data from automated teller machine (ATM) transactions, online queries, social media comments, and customer complaints (Barton and Court, 2012).The second component, compatibility, enables continuous flows of information for real-time decisions.It also helps clean-up operations to synchronize and merge overlapping data and to fix missing information.For example, Amazon embraces compatibility in the BDAC platform by using cloud technologies which help in collaboration, experimentation, and rapid analysis (Davenport and Harris, 2007a). Modularity embodies flexible platform development which allows the addition, modification or removal of features to, or from, the model as needed.It helps in tapping business opportunities and improving FPER.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“First, even when firms operate within the same industry, they possess a varied mixture of resources (Peteraf and Barney, 2003).This assumption of resource heterogeneity indicates the capability of some firms in accomplishing certain functions with the help of their unique resources.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 2.2.1 Resource based theory (RBT) The RBT relies on two core assumptions about firm-based resources to show why some firms perform better than others and how to enhance firm performance.First, even when firms operate within the same industry, they possess a varied mixture of resources (Peteraf and Barney, 2003).This assumption of resource heterogeneity indicates the capability of some firms in accomplishing certain functions with the help of their unique resources.Second, these differences in resources are facilitated by the difficulty of exchanging resources across firms.This assumption indicates resource immobility which highlights the fact that the synergistic benefits from various resources are sustained over time (Barney and Hesterly, 2012).In addition to these two assumptions, the logic of RBT embraces the VRIO framework which clearly states that firm performance depends on the extent to which a firm possesses simultaneously valuable (V), rare (R), imperfectly imitable (I) resources which are properly organized (O) (Amit and Schoemaker, 1993;Barney et al., 2001).First, the valuable dimension of resources enables a firm to enhance net revenues and reduce net costs (Barney and Arikan, 2001), which in other words helps firms capitalize upon an opportunity and minimize a threat (Barney and Hesterly, 2012).Second, the rare dimension indicates that the resources are possessed by a small number of firms to achieve competitive advantages.Third, the imperfectly imitable dimension suggests that firms cannot directly copy or substitute such resources because they are costly to imitate.Research suggests that resource complementarity among resources within a firm make it difficult for competitors to duplicate (Morgan et al., 2009).Resource complementarity occurs when the presence of one resource enables another to leverage firm performance.Finally, the organization dimension focuses on the proper management of valuable, rare and imperfectly imitable resources to leverage their full competitive potential (Barney and Clark, 2007).Resources are defined as tangible and intangible assets used by the firms to conceive of and implement its strategies. A subset of resources, which represent an "organizationally embedded non-transferable firm-specific resource whose purpose is to improve the productivity of the other resources possessed by the firm" (p.389).
What the sources say
- Not checked yetcites Peteraf and Barney, 2003 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus we present RBT to argue that firm performance in a data economy is enhanced only when capabilities are valuable, rare, imperfectly imitable and when the firm's organization or management exploits the potential of resources.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > Organization
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A larger amount of synchronization between BDAC and business strategies increases the synergy among different functional units and positively impacts FPER.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 3.5 Analytics Capability, Business Strategy Alignment and Firm Performance
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“This view also posits that no properties are native to each constituent dimension because BDAC dimensions are constitutively entangled (Orlikowski, 2007) and mutually supportive (Barton and Court, 2012).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 2.2.3 Entanglement view of Sociomaterialism In addition to the RBT, the current study's theoretical framework is based on the concept of sociomateriality which refers to the ontological integration of social and material.This viewpoint does not show that the material influences the social (i.e., technological determinism view) or the social influences the material (i.e., social construction view), or a recursive relationship between the social and material (i.e., socio-technical view).Rather, the study embraces the relational ontology of sociomaterialism which posits that the organizational (i.e., BDA management), physical (i.e., IT infrastructure), and human (e.g., analytics skill or knowledge) dimensions are so interwoven that it is difficult to measure their individual contributions in isolation (Orlikowski and Scott, 2008).Orlikowski (2007Orlikowski ( , p.1437) ) clarifies that "the social and the material are inextricably related".With this conceptualization, we argue that the BDAC dimensions do not act in isolation; rather, they act together.This view also posits that no properties are native to each constituent dimension because BDAC dimensions are constitutively entangled (Orlikowski, 2007) and mutually supportive (Barton and Court, 2012).Indeed, the individual capability dimension is the manifestation of the overall BDAC building blocks as a whole.The study presents the summary of entanglement view of sociomaterialism in Table 3, which indicates that reality does not represent independent objects (social or material), but the joint agency of both. Table 3: Foundations of entanglement view using sociomaterialism Foundations of Entanglement view Definitions using sociomateriality (Latour, 2005;Orlikowski, 2007;Orlikowski and Scott, 2008;Stein et al., 2014) Ontology Human and non-human are inextricably entangled to work together.
What the sources say
- Not checked yetcites Orlikowski, 2007 source
- Not checked yetcites Barton and Court, 2012
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Indeed, the individual capability dimension is the manifestation of the overall BDAC building blocks as a whole.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 2.2.3 Entanglement view of Sociomaterialism In addition to the RBT, the current study's theoretical framework is based on the concept of sociomateriality which refers to the ontological integration of social and material.This viewpoint does not show that the material influences the social (i.e., technological determinism view) or the social influences the material (i.e., social construction view), or a recursive relationship between the social and material (i.e., socio-technical view).Rather, the study embraces the relational ontology of sociomaterialism which posits that the organizational (i.e., BDA management), physical (i.e., IT infrastructure), and human (e.g., analytics skill or knowledge) dimensions are so interwoven that it is difficult to measure their individual contributions in isolation (Orlikowski and Scott, 2008).Orlikowski (2007Orlikowski ( , p.1437) ) clarifies that "the social and the material are inextricably related".With this conceptualization, we argue that the BDAC dimensions do not act in isolation; rather, they act together.This view also posits that no properties are native to each constituent dimension because BDAC dimensions are constitutively entangled (Orlikowski, 2007) and mutually supportive (Barton and Court, 2012).Indeed, the individual capability dimension is the manifestation of the overall BDAC building blocks as a whole.The study presents the summary of entanglement view of sociomaterialism in Table 3, which indicates that reality does not represent independent objects (social or material), but the joint agency of both. Table 3: Foundations of entanglement view using sociomaterialism Foundations of Entanglement view Definitions using sociomateriality (Latour, 2005;Orlikowski, 2007;Orlikowski and Scott, 2008;Stein et al., 2014) Ontology Human and non-human are inextricably entangled to work together.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
In the paper
“According to the RBT, the competency of a firm depends on its capabilities to effectively manage its critical resources (both human and other resources) to achieve firm performance (FPER) (Grant, 2002).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > Organization
What the sources say
- Not checked yetcites Grant, 2002
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“Wixom et al. (2013) show that BDAC can improve FPER by improving productivity both in tangible (i.e., less paper reporting) and intangible (company reputation) benefits.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > First-Order Model
What the sources say
- Not checked yetcites Wixom et al., 2013
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Confirmatory factor analysis (CFA) was used to verify the convergent and discriminant validity of the first-order measurement model (Table 8).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 5.1 Measurement Model
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“Thus, the study confirmed that all the item loadings and values for CR and AVE exceed their respective cut-off values, thus ensuring adequate reliability and convergent validity (Fornell and Larcker, 1981).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 5.1 Measurement Model
What the sources say
- Not checked yetcites Fornell and Larcker, 1981 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Composite reliability (CR) and AVE of all scales are either equal to or exceed 0.80 and 0.50 cut-off values, respectively (Fornell and Larcker, 1981;Hair et al., 2013).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 5.1 Measurement Model
What the sources say
- Not checked yetcites Fornell and Larcker, 1981 source
- Not checked yetcites Hair et al., 2013
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Subsequently, eight experienced analytics academics conducted the content validity of the survey.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 4.1 Scale Development
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Firstly, our study suggests that BDAC is an important enabler of improved FPER, thus confirming the relationship between high-level BDAC and firm performance (FPER).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 6.2 Managerial Contributions
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“We confirm the significance of the moderator because the path coefficient of the BDAC*ACBSA-FPER is significant, independently of BDAC-FPER and ACBSA-FPER relationships in the interaction model (Henseler and Fassott, 2010).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 5.3 Structural Model
What the sources say
- Not checked yetcites Henseler and Fassott, 2010
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Here, the lowest AVE is 0.600 for BDA technology and the lowest CR is 0.902 for BDA connectivity: all of these values exceed the recommended thresholds.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 5.1 Measurement Model
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“For this study, all measurement items were taken from the existing literature and were adapted to fit the big data analytics context (Appendix 1).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 4.1 Scale Development
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We controlled for the demographic variables relevant to the analytics managers such as, age, gender, education, experience, industry and the size of organization to avoid any bias due to demographics.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 4.1 Scale Development
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Initially, the study calculated all the item loadings which exceeded the cut-off values of 0.7 and were significant at p < 0.001.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 5.1 Measurement Model
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Overall, the findings of the study help to minimize confusions regarding the role of strategic alignment in the RBT framework (Teece, 2014).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 6.1 Theoretical Contributions
What the sources say
- Not checked yetcites Teece, 2014
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
None of the sources cited for this claim have been checked yet.
In the paper
“The study applied nonparametric bootstrapping (Chin, 1998a;Efron and Tibshirani, 1993;Tenenhaus et al., 2005) with 5,000 replications to obtain the standard errors of the estimates (Hair Jr et al., 2013).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > MVs LVs
What the sources say
- Not checked yetcites Chin, 1998a
- Not checked yetcites Efron and Tibshirani, 1993
- Not checked yetcites Tenenhaus et al., 2005 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“A recent big data study (involving 2,037 professionals and interviews with more than 30 executives in 100 countries and 25 industries) reflects the importance of analytics management as 87% of its respondents suggest focusing on elevating their organizations to the next level of analytics management (Kiron et al., 2014).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 6 Discussion
What the sources say
- Not checked yetcites Kiron et al., 2014
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These findings confirm ACBSA as the significant moderator or the necessary condition for strong firm performance (FPER).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 6 Discussion
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The size of estimated power (0.99) compellingly exceeds the cut-off value of 0.80 (Cohen, 1992), confirming that the associations are truly significant.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 5.4 Additional Analyses The findings of the study confirm the impact of BDAC and ACBSA on FPER, thereby ensuring the nomological validity of the overall research model.The study also conducted few additional analyses to ensure overall validity of the findings.First, to address the concern of non-response bias, we checked the profiles of the survey respondents and those on the panel in terms of organization size and industry, and no non-response bias was found through the chi-square tests (Kim et al., 2012).We also compared the early (20 percent) and late (20 percent) response groups, and the paired t-test did not provide any significant difference on the survey items between two groups.Second, we checked common method variance (CMV) by applying Harman's one-factor test (Podsakoff and Organ, 1986) on the 11 first-order constructs and the results did not provide any significant common factor loading on all the measures.Third, we applied Stone-Geisser's Q 2 to test the predictive validity of the model (Akter et al., 2011).Using the cross-validated redundancy approach (omission distance = 7), this study obtained a Q 2 0.503 for FPER, which adequately demonstrate the predictive validity of the BDAC construct on FPER in the big data environment (Chin, 2010).Finally, the study conducted power analysis (1-β) to validate the empirical findings on the 152 responses in the sample.Power (1-β) indicates the probability of successfully rejecting a null hypothesis (Cohen, 1992).Using G*Power 3.1.3by Faul et al. (2009), the study conducted the power test (post hoc) to estimate the validity of the hypothesized relationships.Cohen (1988) suggests that a threshold level of 0.80 be used as estimated power for behavioral research.The study estimated the power of 0.99 for the research model with the sample size of 152 (N), 0.05 significance level (α) and 0.10 effect size (ES).The size of estimated power (0.99) compellingly exceeds the cut-off value of 0.80 (Cohen, 1992), confirming that the associations are truly significant.
What the sources say
- Not checked yetcites Cohen, 1992 source
boostedcorrelationaln = 152- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The findings of the study confirm the impact of BDAC and ACBSA on FPER, thereby ensuring the nomological validity of the overall research model.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 5.4 Additional Analyses The findings of the study confirm the impact of BDAC and ACBSA on FPER, thereby ensuring the nomological validity of the overall research model.The study also conducted few additional analyses to ensure overall validity of the findings.First, to address the concern of non-response bias, we checked the profiles of the survey respondents and those on the panel in terms of organization size and industry, and no non-response bias was found through the chi-square tests (Kim et al., 2012).We also compared the early (20 percent) and late (20 percent) response groups, and the paired t-test did not provide any significant difference on the survey items between two groups.Second, we checked common method variance (CMV) by applying Harman's one-factor test (Podsakoff and Organ, 1986) on the 11 first-order constructs and the results did not provide any significant common factor loading on all the measures.Third, we applied Stone-Geisser's Q 2 to test the predictive validity of the model (Akter et al., 2011).Using the cross-validated redundancy approach (omission distance = 7), this study obtained a Q 2 0.503 for FPER, which adequately demonstrate the predictive validity of the BDAC construct on FPER in the big data environment (Chin, 2010).Finally, the study conducted power analysis (1-β) to validate the empirical findings on the 152 responses in the sample.Power (1-β) indicates the probability of successfully rejecting a null hypothesis (Cohen, 1992).Using G*Power 3.1.3by Faul et al. (2009), the study conducted the power test (post hoc) to estimate the validity of the hypothesized relationships.Cohen (1988) suggests that a threshold level of 0.80 be used as estimated power for behavioral research.The study estimated the power of 0.99 for the research model with the sample size of 152 (N), 0.05 significance level (α) and 0.10 effect size (ES).The size of estimated power (0.99) compellingly exceeds the cut-off value of 0.80 (Cohen, 1992), confirming that the associations are truly significant.
boosteddirectn = 152- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“A pilot study of the survey was then conducted with a total of 61 respondents enrolled from various big data analytics groups on LinkedIn.This allowed our proposed model to be tested for robustness before the final data collection.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 4.1 Scale Development
plainn = 61- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“The study estimates the model using partial least squares (PLS) path modeling which is able to ensure more theoretical parsimony and less model complexity (Wetzels et al., 2009).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > MVs LVs
What the sources say
- Not checked yetcites Wetzels et al., 2009 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“In other words, each item loads more on its own construct than on other constructs and, therefore, all constructs share a substantial amount of variance with their own items (Chin, 1998b;Fornell and Bookstein, 1982).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 5.1 Measurement Model
What the sources say
- Not checked yetcites Chin, 1998b
- Not checked yetcites Fornell and Bookstein, 1982
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“To be more precise, an invitation to participate in the study was sent on to a random sample of 826 people who were using big data in the USA and who were members of the 'business analysts', 'big data analytics', and 'IT professionals' groups.A total of 668 panel members agreed to participate in the study.After a careful analysis of all responses, 152 valid questionnaires were considered to have”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 4.2 Data Collection
plainn = 826- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
- Rests on an assumption: The 152 valid questionnaires are representative of the population of big data-using firms in the USA, allowing generalization.
In the paper
“For example, by improving BDAC and aligning strategy, managers could better serve customer needs (79%); increase sales and revenue (76%); create new products and services (70%); and expand into new markets (72%), with the help of quality information and robust insights (Columbus, 2014b).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 6.2 Managerial Contributions
What the sources say
- Not checked yetcites Columbus, 2014b
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
In the paper
“For example, by improving BDAC and aligning strategy, managers could better serve customer needs (79%); increase sales and revenue (76%); create new products and services (70%); and expand into new markets (72%), with the help of quality information and robust insights (Columbus, 2014b).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 6.2 Managerial Contributions
What the sources say
- Not checked yetcites Columbus, 2014b
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Therefore, one notable observation is that few scholars disagree with the inclusion of BDA management capability, BDA infrastructure capability, and BDA talent capability as key dimensions of BDAC.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > Reflective ACBSA4
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“its scope was limited to exploring BDAC dimensions and modeling the impact of BDAC on FPER with ACBSA as a moderator.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 6.3 Limitations and Future Research Directions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“the study does not evaluate unobserved heterogeneity in the structural equation model (SEM).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 6.3 Limitations and Future Research Directions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Although BDAC dimensions differ in their terminology, the taxonomy schemes proposed by the literature are similar as they reflect BDA management capability, BDA infrastructure capability and BDA talent capability-related aspects.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > Reflective ACBSA4
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, the study leads to a better understanding of capability-strategy-performance in data economy and is likely to open new avenues of research into academic and corporate policy and practices.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 6.4 Conclusion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thirdly, by applying the RBT and the sociomaterialism perspective in conceptualizing BDAC within the big data environment, our research proves its utility in portraying the entanglement phenomenon in BDAC dynamics.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > Reflective ACBSA4
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our research specifically addresses this challenge by presenting the third-order BDAC model to capture the variations in firm performance (FPER).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > Reflective ACBSA4
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Rests on an assumption: The emerging BDA research has been struggling to encapsulate and prove the significance of BDAC as a source of firm performance.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This research builds a theory of BDAC strategy that shows how to leverage the BDAC dimensions and sub-dimensions in order to build an overall BDAC climate.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 6.4 Conclusion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Consequently, there is a growing focus on the BDAC-ACBSA-FPER link in the BDA environment across various industries.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 6.2 Managerial Contributions
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although several studies highlight the importance of management, technology and talent capability in big data environment, our work illuminates the role of RBT and entanglement view in proposing an integrated BDAC model and its overall impact on firm performance.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 6.4 Conclusion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This research applies RBT as a unifying paradigm for combining other theories (e.g., sociomaterialism and IT capability) and presents a parsimonious foundation for multiple theoretical perspectives.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > Reflective ACBSA4
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“With the growing interests in business analytics across various industries, the current study advances BDAC conceptualization and the role of ACBSA in enhancing FPER.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 6.4 Conclusion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“A notable strength of the current study is that data were collected from multiple industries to empirically test the model.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 6.4 Conclusion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“this study used a 7-point Likert scale to measure all the items, which may introduce the so-called 'acquiescence bias' (Chin et al., 2008).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 6.3 Limitations and Future Research Directions
What the sources say
- Not checked yetcites Chin et al., 2008
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“For example, by improving BDAC and aligning strategy, managers could better serve customer needs (79%); increase sales and revenue (76%); create new products and services (70%); and expand into new markets (72%), with the help of quality information and robust insights (Columbus, 2014b).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 6.2 Managerial Contributions
What the sources say
- Not checked yetcites Columbus, 2014b
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
In the paper
“For example, by improving BDAC and aligning strategy, managers could better serve customer needs (79%); increase sales and revenue (76%); create new products and services (70%); and expand into new markets (72%), with the help of quality information and robust insights (Columbus, 2014b).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 6.2 Managerial Contributions
What the sources say
- Not checked yetcites Columbus, 2014b
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“certain burning issues (e.g., the analytics climate, privacy, surveillance and democracy) could not be encapsulated into this study but might be investigated in future research.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 6.3 Limitations and Future Research Directions
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Secondly, the findings of our study emphasize not only the importance of BDAC development but also a strong alignment between BDAC and ACBSA in order to achieve improved firm performance (FPER).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 6.2 Managerial Contributions
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Secondly, despite the paucity of empirical modeling in big data research, our research extends this stream by conceptualizing a multidimensional BDAC model drawing on the RBT and sociomaterialism which substantiate the fact that BDAC is a hierarchical construct having a strong influence on firm performance (FPER).”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > Reflective ACBSA4
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Rests on an assumption: There is a paucity of empirical modeling in big data research.
The paper relies on this without support.
There is a paucity of research in the big data literature that has explored and encapsulated BDAC dimensions by applying the entanglement view under sociomaterialism.
“To the best of our knowledge, in the big data literature, there is a paucity of research which has explored and encapsulated BDAC dimensions by applying the entanglement view under sociomaterialism.”
The paper relies on this without support.
BDAC pays off for some companies but not for others.
“However, BDAC pays off for some companies but not for others.”
The paper relies on this without support.
Very few companies have achieved a big impact through big data.
“It appears that very few have achieved a big impact through big data.”
The paper relies on this without support.
Investment in big data still poses a lot of challenges due to the missing link between analytics capabilities and firm performance.
“Yet, investment in big data still poses a lot of challenges due to the missing link between analytics capabilities and firm performance.”
The paper relies on this without support.
The observed associations between BDAC, ACBSA and FPER reflect causal effects rather than mere association.
“Overall, the findings of the structural model confirm that BDAC is a significant predictor of FPER (explaining 50% of the variance).”
The paper relies on this without support.
The 152 valid questionnaires are representative of the population of big data-using firms in the USA, allowing generalization.
“an invitation to participate in the study was sent on to a random sample of 826 people who were using big data in the USA and who were members of the 'business analysts', 'big data analytics', and 'IT professionals' groups.”
The paper relies on this without support.
There is a paucity of empirical modeling in big data research.
“despite the paucity of empirical modeling in big data research”
The paper relies on this without support.
The emerging BDA research has been struggling to encapsulate and prove the significance of BDAC as a source of firm performance.
“The emerging BDA research has been struggling to encapsulate and prove the significance of BDAC as a source of firm performance.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Despite the increasing popularity of emerging technologies, like big data analytics and artificial intelligence, there is ambiguity about how the adoption of business analytics impacts firm performance (Akter et al., 2016; Fosso Wamba et al., 2017; Ramanathan et al., 2017; Aydiner et al., 2019; Dubey et al., 2019).”
From Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations (2020) · cites this paper as Akter et al., 2016
In the citing paper
“Furthermore, the limited number of studies on organisations adopting emerging technologies that do exist generally focus on understanding the direct impact on operational performance (see Akter et al., 2016; Fosso Wamba et al., 2017) or on the indirect impact (Aydiner et al., 2019). These studies do not go deeper in trying to understand the impact of entrepreneurial orientation on the adoption of”
From Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations (2020) · cites this paper as Akter et al., 2016
In the citing paper
“While operations-line managers might be highly interested and motivated to invest and build BDA capability ( Akter et al., 2016 ;Mikalef et al., 2019 )”
From Applications of big data in emerging management disciplines: A literature review using text mining (2021) · cites this paper as Akter et al., 2016
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
“an invitation to participate in the study was sent on to a random sample of 826 people who were using big data in the USA and who were members of the 'business analysts', 'big data analytics', and 'IT professionals' groups.A total of 668 panel members agreed to participate in the study.After a careful analysis of all responses, 152 valid questionnaires were considered to have been correctly filled out and appropriate for further analysis, thus giving a response rate of 37.72%.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 4.2 Data Collection
Reported 37.72 · recomputed 18.401937
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 3 RESEARCH MODEL AND HYPOTHESES
“As such, we conducted two Delphi studies to explore the subdimensions of BDAC under each primary dimension identified in the review.Round one of the Delphi study was conducted in November 2014 (n=51) and round two in February 2015 (n=43)”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 4.2 Data Collection
“The data collection was conducted in April, 2014.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 5.1 Measurement Model
“the study found that the factor weights of age, gender, education, experience, industry and firm size are significant at p< 0.01.”
§ HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY > 5.1 Measurement Model
“Age Gender education experience industry Firm size 0.580 0.439 0.685 0.096 -0.142 0.022 1.841 1.517 2.549 0.293 0.448 0.075”
Citations
Cites
Evaluating Structural Equation Models with Unobservable Variables and Measurement Error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.2307/3151312 · Cited by 109,314
Evaluating structural equation models with unobservable variables and measurement error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.1177/002224378101800104 · Cited by 109,190
Statistical Power Analysis for the Behavioral Sciences
Muller, K. E., & Cohen, J. (1989) · Technometrics · doi:10.2307/1270020 · Cited by 99,603
Statistical power analysis for the behavioral sciences
Cohen, J. (1990) · Computers Environment and Urban Systems · doi:10.1016/0198-9715(90)90050-4 · Cited by 65,417
Firm Resources and Sustained Competitive Advantage
Barney, J. (1991) · Journal of Management · doi:10.1177/014920639101700108 · Cited by 48,334
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500
Firm resources and sustained competitive advantage
Barney, J. B. (2004) · Advances in strategic management · doi:10.1016/s0742-3322(00)17018-4 · Cited by 38,216
Statistical power analyses using G∗Power 3.1: tests for correlation and regression analyses
Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009) · Behavior Research Methods · doi:10.3758/brm.41.4.1149 · Cited by 37,615
Pls-Sem: indeed a silver bullet
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011) · Journal of Marketing Theory and Practice · doi:10.2753/mtp1069-6679190202 · Cited by 21,631
Self-Reports in Organizational Research: Problems and Prospects
Podsakoff, P. M., & Organ, D. W. (1986) · Journal of Management · doi:10.1177/014920638601200408 · Cited by 17,102
Research works printed but not linked: 11
- Bharadwaj, 2000, A resource-based perspective on information technology capability and firm performance: an empirical investigation, MIS Q., 24, 169, 10.2307/3250983 · DOI · Paper page
- Chin, 1998, Commentary: issues and opinion on structural equation modeling, MIS Q.
- Court, 2015, Getting big impact from big data, McKinsey Q., 1
- Davenport, 2012, How ‘big data’ is different, MIT Sloan Manag. Rev., 54, 43
- Davenport, 2007, The dark side of customer analytics, Harv. Bus. Rev., 85
- Davenport, 2012, Data scientist: the sexiest job of the 21st century, Harv. Bus. Rev., 90, 70
- Goes, 2014, Big data and IS research, MIS Q., 38, 3
- Grant, R.M., 2002. The resource-based theory of competitive advantage. In Strategy: Critical Perspectives on Business and Management. Faulkner, David (Eds.), 135, pp.122-148.
- Johnson, 2012, Big data+big analytics=big opportunity, Financ. Exec., 28, 50
- Kiron, 2012, Creating business value with analytics, MIT Sloan Manag. Rev., 53, 57
- Lavalle, 2011, Big data, analytics and the path from insights to value, Mit. Sloan Manag. Rev., 52, 21
Cited by
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Metadata
- Authors
- Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J.
- Affiliations
- University of Wollongong (AU)
- Université de Toulouse (FR)
- University of Massachusetts Dartmouth (US)
- Symbiosis International University (IN)
- University of Plymouth (GB)
- Corresponding author
- Gunasekaran, Angappa
- Venue
- International Journal of Production Economics
- Publisher
- Elsevier BV
- Published
- 2016-12
- Volume
- 182
- Pages
- 113-131
- ISSN
- 0925-5273 (ISSN-L)
- OpenAlex
- W2517745273
- Semantic Scholar
- abf4c7acd0d2729e3e78c2d515f588502c9858e8
- Cited by
- 1,624
- References
- 114
- Citations by year
- 2026: 259 · 2025: 213 · 2024: 206 · 2023: 223 · 2022: 214 · 2021: 171 · 2020: 147 · 2019: 98 · 2018: 65 · 2017: 26 · 2016: 2
- FWCI
- 103.39
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- International Journal of Production Economics (publishedVersion)
- PEARL (University of Plymouth) (submittedVersion)
- Research Online (University of Wollongong) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Topics
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Social Sciences › Business, Management and Accounting › Management Information Systems › Information Technology Governance and Strategy
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Keywords
- big data analytics capability, firm performance, business strategy alignment, resource-based view, management capability, technological capabilities, business analysts, moderating effect
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-11, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-11 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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