Unveiling the relationship of big data analytics capability and performance: the role of strategic orientation ambidexterity and team characteristics

Extant research shows that big data analytics (BDA) capability is often employed as a part of organizational resources to enhance firm performance. Drawing upon the resource-based view, dynamic capabilities, and contingency theory, this study endeavors to examine the alignment between BDA capability and a specific type of procurement strategies (i.e., supplier development) and its impact on firm performance. The study extends the BDA capability research by investigating the direct impact of BDA capability on supplier development and firm performance, respectively, and by exploring both mediating and moderating effects on the relationship between supplier development and firm performance. The main results show that a firm’s BDA capability has not only a direct positive significant impact on supplier development, but also a direct positive significant impact on its business performance. More importantly, the results indicate strong moderating and mediating effects of BDA capability on supplier development, which in turn affects the improvement of firm performance. Theoretical and managerial implications along with future research directions are provided in the end.

Big data analytics capabilities and firm performance: An integrated MCDM approach

PurposeThe purpose of this study is to investigate the impact of big data (BD) analytics capabilities (BDACs) on green supply chain integration (GSCI) and green innovation (GI) in the context of a developing country, Jordan. In addition, the mediating effect of GSCI on the relationship between BDAC and GI is investigated.Design/methodology/approachData collection was carried out through a survey with 300 respondents from food and beverages manufacturing firms located in Jordan. Partial least squares-structural equation modeling (PLS-SEM) technique was applied to analyze the collected data. Natural resource-based view (NRBV) theory was the adopted theoretical lens for this study.FindingsThe results revealed that BDAC positively and significantly affects both GSCI and GI. In addition, the results demonstrated that GSCI positively and significantly affects GI. Further, it is also found that GSCI positively and significantly mediates the relationship between BDAC and GI.Originality/valueThis study developed a theoretical and empirical model to investigate the relationship between BDAC, GSCI and GI. This study offers new theoretical and managerial contributions that add value to the supply chain (SC) management literature by testing the mediation model in food and beverages manufacturing firms located in Jordan.

Purpose: This study aims to investigate the impact of Big Data Analytics Capabilities (BDAC) on Indonesian Firm Performance (FPER), addressing the lack of empirical research in this area despite Indonesia's significant Big Data expenditure growth. The study also proposes a conceptual model as a framework for developing BDAC. Method: This is a quantitative study conducted in Indonesia using purposive and snowball sampling techniques to collect data from 51 Indonesian companies. The research model is based on the IT Capability framework, Sociomaterialism Theory, and incorporates Business Process Agility (BPA) as a mediating variable, as suggested by previous research. Findings: The results demonstrate a significant positive impact of BDAC on FPER. Furthermore, the study confirms that BPA significantly mediates the relationship between BDAC and FPER, highlighting the crucial role of agile business processes in leveraging the benefits of Big Data Analytics for improved firm performance. Practical Implications: This research provides valuable insights for Indonesian companies by: 1) demonstrating the significant positive impact of investing in BDAC on firm performance; 2) highlighting the importance of developing agile business processes to maximize the return on Big Data Analytics investments; and 3) offering a conceptual framework for developing and enhancing BDAC within Indonesian organizations. Originality: This study contributes to the existing literature by: 1) providing empirical evidence on the impact of BDAC on FPER specifically within the Indonesian context; 2) proposing a novel conceptual model for developing BDAC based on a combination of IT Capability framework, Sociomaterialism Theory, and the mediating role of BPA; and 3) addressing the gap in research on the relationship between BDAC and FPER in the Indonesian market. Paper Type: This research can be classified as an empirical study within the field of Information Systems, specifically focusing on Big Data Analytics, firm performance, and business process management. Keywords: Big Data, Firm Performance, Business Process Agility

Toward the development of a big data analytics capability

PurposeThis study investigated the relationships among big data analytics capability (BDAC), low-cost advantage, differentiation advantage, market and operational performance underpinning the resource-based view (RBV) and the entanglement view of sociomaterialism (EVS) theories.Design/methodology/approachA total of 191 responses from members of the Federation of Malaysian Manufacturers were analysed using a structural equation modelling approach.FindingsThis study has conclusively demonstrated that BDAC is indeed a resource bundle comprising human skills, tangible and intangible resources. This study found that BDAC positively influences competitive advantage and firm performance. The differentiation advantage was found to be a key factor in explaining market performance. Theoretically, both RBV and EVS could be used to link BDAC, differentiation advantage and market performance to explain superior firm performance.Research limitations/implicationsFirst, the sample is restricted to the manufacturers in Malaysia. Second, a single independent variable, BDAC, is used as a higher-order capability to influence competitive advantage, and thus, superior firm performance. Third, this study uses a self-reported survey, which means that only one respondent from each firm answered the questions. Fourth, this study excludes the focused strategy as it aims to investigate the competitive strategy used in the broader industry environment, rather than in a specific segment pursuing a focused strategy.Practical implicationsFirst, BDAC is a valuable, rare, inimitable and non-substitutable tool for manufacturers to enhance their firm performance. Second, BDAC is crucial for manufacturing firms to reduce costs and differentiate themselves. Third, a low-cost advantage may not help manufacturers achieve greater market and operational performance.Originality/valueThe relationship among BDAC, low-cost advantage, differentiation advantage, market and operational performance within manufacturing industry is empirically tested.

Purpose This study clarifies the relationship among big data analytics capabilities (BDAC), firm performance (FP), data-driven culture (DDC) and networking capabilities (NC). A theoretical model is proposed to illustrate how manufacturing SMEs can enhance their firm performance by adopting BDAC, DDC and NC. Design/methodology/approach The study employed partial least squares structural equation modelling (PLS-SEM) for hypothesis testing, utilizing questionnaires directed at owners/managers of small and medium-sized enterprises (SMEs). Findings The results indicate that BDAC has a substantial impact on FP. Additionally, a DDC serves as a mediator between BDAC and FP, thereby enhancing firm performance. Nevertheless, there was no significant mediation effect of NC between BDAC and FP. Originality/value Previous studies overlooked the mediating roles of DDC and NC in the relationship between BDAC and FP improvements. This study examined the mediating roles of DDC and NC in the relationship between BDAC and FP, thereby addressing a gap in the existing literature.

Purpose In the era of digital transformation, leveraging big data has become a strategic imperative for sustaining competitive advantage. However, the impact of Big Data Analytics Capability (BDAC) on firm performance depends on the development of complementary organisational capabilities and governance mechanisms. Grounded in the integrated model, this study aims to investigate how BDAC influences firm performance through the mediating roles of organisational capabilities and the moderating role of data governance. Design/methodology/approach Survey data from 227 Forbes Global 2000 firms were analysed using partial least squares hierarchical component modelling (HCM). Findings Dynamic and innovative capabilities significantly mediate the BDAC–performance relationship. Data governance strengthens the effect of BDAC on dynamic and operational capabilities but not on innovative capability. Research limitations/implications HCM improves model parsimony for complex capability–performance relationships, while cross-validated predictive ability test (CVPAT) confirms strong predictive validity and explains the internal mechanisms linking BDAC to performance. These methodological insights strengthen the theoretical understanding of how data-driven capabilities create value. Practical implications The findings suggest that managers should leverage BDAC to enhance dynamic and innovative capabilities, supported by robust data governance that reinforces BDAC's influence on dynamic and operational capabilities. Effective governance fosters organisational agility, enabling firms to sustain performance and support broader sustainability objectives. Originality/value BDAC influences firm performance through dynamic, operational and innovative capabilities, with a contingent effect of data governance. The study advances theory by integrating the resource-based view with the dynamic capabilities perspective and applies rigorous predictive modelling to clarify these mechanisms.

The intermediating role of big data analytics capability between responsive and proactive market orientations and firm performance in the retail industry

The emergence of the Covid-19 pandemic and restrictions on international mobility have negatively impacted the tourism market. Tourism players, particularly the hotel industry, have turned to big data analytics to mitigate uncertainties and offer better products and services. Nonetheless, the central question for researchers and practitioners is how the usage of big data analytics can help the hotel industry improve firm performance. Drawing on the resource-based view and dynamic capability theories, this study analyses the relationship between big data analytics capability and firm performance in the hotel industry. This study expands the current research by examining the role of organizational agility in mediating the relationship between big data analytics capability and firm performance. To empirically test the research model, the author used survey data from 115 star-rated hotels throughout Malaysia. Through partial least square equation modeling, the findings revealed that big data analytics capability positively affects organizational agility and firm performance. The result also demonstrated that organizational agility mediates the relationship between big data analytics capability and firm performance. This study can also guide hoteliers to identify resources required to build big data analytics capability and further highlight the significance of organizational agility in improving firm performance in the hotel industry.

PurposeWhile big data analytics can spur innovation among firms, it is unclear whether it can effectively drive value creation, value proposition, value delivery and value capture to deal with disruptions and the ever-changing demands of customers. This study therefore aims to examine how value creation, value proposition, value delivery and value capture can be improved through big data analytics capability (BDAC). This study advances the discourse by investigating how the market environment and strategic orientations play significant but little-studied roles in enhancing or lessening BDAC’s impact on business model innovation (BMI).Design/methodology/approachDrawing on dynamic capability and contingency perspectives, a model of five hypotheses was developed and validated using survey data from 208 managers of manufacturing firms in Ghana. Covariance-based structural equation modeling was used for the analysis.FindingsThe findings revealed that BDAC and strategic orientation (market and learning) directly influence the dimensions of BMI (value creation, value proposition, value delivery and value capture). The findings further showed that strategic orientations partially mediate the BDAC–BMI link. The authors also noted that the BDAC–BMI link is amplified at high levels of market dynamism.Practical implicationsThe findings suggest that investing in BDA alone may not be sufficient to drive superior business model innovation. However, market orientation and continuous learning are crucial to fully realizing BDAC’s full potential in enabling value creation, value proposition, value delivery and value capture, especially in a dynamic market environment.Originality/valueThis study contributes to existing BMI literature by being the first to examine how BDAC facilitates value creation, value proposition, value delivery and value capture in developing countries. This paper also advances BM literature by theorizing and validating important but rarely studied roles of strategic orientations and market dynamism. Thus, this paper extends the understanding of the conditions and mechanisms through which the effect of BDAC on value creation, value proposition, value delivery and value capture can be optimized.

In recent times, big data analytics (BDA) technologies have provided firms new opportunities and perspectives with the ability to quickly acquire large amounts of data from various source. BDA capability is defined as a firm’s ability to aggregate, integrate, and deploy big data-specific resources. As a knowledge-based dynamic capability, BDA capability is an important organizational capability that provides sustainable competitive advantage in the big data environment. While research suggests a positive relationship between BDA capability and firm performance, studies on how this relationship manifests in different contexts are limited. For example, the role of an intra-organizational factor such as firm size, which has the potential to affect firms’ decisions and behaviors, in this relationship has not been sufficiently explored. In this context, the aim of this study is to investigate the moderating role of firm size in the relationship between BDA capability and firm performance, through the lens of the Knowledge-Based Dynamic Capabilities View. To this end, a cross-sectional field study was conducted on 252 SMEs and large-scale companies in Turkey. Results indicate that firm size plays a moderating role in the relationship between BDA capability and firm performance, with the effect of BDA capability on firm performance increasing as firm size increases. The study concludes with suggestions for theorists and practitioners, and a discussion on how companies can evaluate the potential of BDA.

In order for organisations to generate competitive advantages from big data investments, they need to acquire a unique blend of technology, human skills, financial resources and a data-driven culture. Organisations need to measure their big data analytics capability in order to yield competitive performance. This study sought to examine the relationship between a firm's big data analytics capability (BDAC) and competitive performance through mediating role of dynamic and operational capabilities. To test the proposed research model, we used survey data from 110 employees across 54 insurance companies in Kenya. Using partial least squares structural equation modelling, the results provide evidence that BDAC leads to superior firm performance. Various resources that form big data analytics (BDA) capability have been identified and an instrument to measure BDAC is proposed. The findings from this study provide a roadmap strategy for implementing BDA projects.

PurposeThe study examines how firms may transform big data analytics (BDA) into a sustainable competitive advantage and enhance business performance using BDA. Furthermore, this study identifies various resources and sub-capabilities that contribute to BDA capability.Design/methodology/approachUsing classic grounded theory (GT), resource-based theory and dynamic capability (DC), the authors conducted interviews, which involved an exploratory inductive process. Through a continuous iterative process between the collection, analysis and comparison of data, themes and their relationships appeared. The literature was used as part of the data set in the later phases of data collection and analysis to identify how the study’s findings fit with the extant literature and enrich the emerging concepts and their relationships.FindingsThe data analysis led to developing a conceptual model of BDA capability that described how BDA contributes to firm performance through the mediated impact of organizational learning (OL). The findings indicate that BDA capability is incomplete in the absence of BDA capability dimensions and their sub-dimensions, and expected advancement will not be achieved.Research limitations/implicationsThe research offers insights on how BDA is converted into an enterprise-wide initiative, by extending the BDA capability model and describing the role of per dimension in constructing the capability. In addition, the paper provides managers with insights regarding the ways in which BDA capability continuously contributes to OL, fosters organizational knowledge and organizational abilities to sense, seize and reconfigure data and knowledge to grab digital opportunities in order to sustain competitive advantage.Originality/valueThis article is the first exploratory research using GT to identify how data-driven firms obtain and sustain BDA competitive advantage, beyond prior studies that employed mostly a hypothetico-deductive stance to investigate BDA capability. While the authors discovered various dimensions of BDA capability and identified several factors, some of the prior related studies showed some of the dimensions as formative factors (e.g. Lozada et al., 2019; Mikalef et al., 2019) and some other research depicted the different dimensions of BDA capability as reflective factors (e.g. Wamba and Akter, 2019; Ferraris et al., 2019). Thus, it was found necessary to correctly define different dimensions and their contributions, since formative and reflective models represent various approaches to achieving the capability. In this line, the authors used GT, as an exploratory method, to conceptualize BDA capability and the mechanism that it contributes to firm performance. This research introduces new capability dimensions that were not examined in prior research. The study also discusses how OL mediates the impact of BDA capability on firm performance, which is considered the hidden value of BDA capability.

PurposeImplementing big data analytics (BDA) for supply chain ambidexterity (agility and adaptability) and green supply chain (GRSC) presents various organizational challenges. These include leveraging BDA capabilities to balance agility and adaptability, integrating this combined approach with GRSC and aligning these efforts to enhance firm performance. This study explores the associations between BDA, supply chain agility and adaptability, GRSC and their impact on firm performance.Design/methodology/approachIncorporating a resource-based view and contingency theory, we developed a research framework and validated it with data from 355 Chinese firms. Partial least squares structural equation modeling was used to analyze the data.FindingsThe findings demonstrate that BDA capabilities had direct impact on supply chain agility and adaptability, GRSC and firm performance. Moreover, the combination of supply chain agility and adaptability affected GRSC; which in turn significantly influenced firm performance. Supply chain agility and adaptability mediated the relationship between BDA capabilities and GRSC. Additionally, GRSC mediated the relationship between BDA capabilities, supply chain agility and adaptability and firm performance.Originality/valueThis study offers both a theoretical and empirical examination of the relationships between BDA capabilities, supply chain agility and adaptability, GRSC and firm performance. By assessing the direct and mediating effects of these factors on China’s industrial sector, it presents new theoretical and practical insights into BDA and GRSC, thereby enhancing the value of the existing literature.