Big data, human leadership, and firm success: exploring the servant leadership advantage

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.

PurposeIn this digital age, the rapid technological innovation and adoption, with the increasing use of big data analytics, has raised concerns about the ability of small and medium enterprises (SMEs) to sustain the competition and innovation performance (IP). To narrow the research gap, this paper investigates the role of big data analytics capability (BDAC) in moderating the relationship between digital innovation (DI) and SME innovation performance.Design/methodology/approachThis research has been carried forward through a detailed theory and literature analysis. Data were analyzed through confirmatory factor analysis and structural equation models using a two-stage approach in smartPLS-4.FindingsResults highlight that digital service capability (DSC) significantly mediates the relationship between DI and IP. Additionally, value co-creation (VCC) directly affects digital transformation (DT), while DI has a stronger effect on DSC than IP. Furthermore, BDAC significantly moderates the relation between DSC → IP and DT → IP, whereas it has a detrimental effect on the relation between DI and IP. In addition to that, VCC, DSC, DT, DI and BDAC have a direct, significant and positive effect on IP.Practical implicationsThis research was motivated by the practical relevance of supporting SMEs in adopting DT and the resource-based view (RBV) and technology acceptance model (TAM). This study shows that all direct and indirect measures significantly affect innovation performance, including BDAC as moderator. These findings refresh the perspective on what DT, DI, VCC, DSC and BDAC can bring to a firm's innovation performance.Originality/valueThis paper has contributed to DT by empirically validating a theoretical argument that suggests the acceptance and adoption of new technology. This paper aims to fill theoretical gaps in understanding BDAC and DT by incorporating the RBV and TAM theories on BDAC and DT.

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.

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.

PurposeThe prevailing conceptualization of information system (IS) capabilities, rooted in the resource-based view (RBV) framework, tends to focus on unique firm resources. In the digital age, as emphasized by dynamic capabilities (DC), resource reconfiguration is critical in maintaining strategic advantage. This paper focuses on big data analytics capabilities (BDAC) from a DC perspective to present a novel conceptualization of BDAC–DC. We examine its effects on product, business model and business process innovation, including the effects of enterprise architecture (EA) on the BDAC business model innovation relationship.Design/methodology/approachThis research presents a novel DC-based BDAC conceptualization, operationalized as a hierarchical construct. A survey-based approach is used for data collection and data analysis is done using partial least squares structural equation modeling (PLS-SEM).FindingsThe novel conceptualization and the effects of BDAC DC on BDA sensing-seizing and reconfiguration capacities support BDAC’s functional and evolutionary roleplay. Empirical results confirm the positive effects of BDAC–DC on first-order value targets (innovation) and the moderating effects of EA.Research limitations/implicationsThe novel BDAC–DC conceptualization has several implications for BDAC, DC, EA and business value research. Practicing managers must adopt a multifaceted approach to BDAC development by considering non-technical and organizational factors, collaborate with their business counterparts to explore unique big data ideas, initiate proof-of-concept projects to secure support and allocate resources synchronously, considering a multidimensional view of the process, product and business model innovation.Practical implicationsPracticing managers must adopt a multifaceted approach to BDAC development by considering non-technical and organizational factors, collaborate with their business counterparts to explore unique big data ideas, initiate proof-of-concept projects to secure support and allocate resources synchronously, considering a multidimensional view of the process, product and business model innovation for synergistic outcomes.Originality/valueTo the best of our knowledge, this research is the first attempt toward DC-based BDAC conceptualization, empirical validation of first-order effects on various forms of innovation and the often-overlooked role of critical EA capability.

PurposeFollowing the rapid expansion of data volume, velocity and variety, techniques and technologies, big data analytics have achieved substantial development and a surge of companies make investments in big data. Academics and practitioners have been considering the mechanism through which big data analytics capabilities can transform into their improved organizational performance. This paper aims to examine how big data analytics capabilities influence organizational performance through the mediating role of dual innovations.Design/methodology/approachDrawing on the resource-based view and recent literature on big data analytics, this paper aims to examine the direct effects of big data analytics capabilities (BDAC) on organizational performance, as well as the mediating role of dual innovations on the relationship between (BDAC) and organizational performance. The study extends existing research by making a distinction of BDACs' effect on their outcomes and proposing that BDACs help organizations to generate insights that can help strengthen their dual innovations, which in turn have a positive impact on organizational performance. To test our proposed research model, this study conducts empirical analysis based on questionnaire-base survey data collected from 309 respondents working in Chinese manufacturing firms.FindingsThe results support the proposed hypotheses regarding the direct and indirect effect that BDACs have on organizational performance. Specifically, this paper finds that dual innovations positively mediate BDACs' effect on organizational performance.Originality/valueThe conclusions on the relationship between big data analytics capabilities and organizational performance in previous research are controversial due to lack of theoretical foundation and empirical testing. This study resolves the issue by provides empirical analysis, which makes the research conclusions more scientific and credible. In addition, previous literature mainly focused on BDACs' direct impact on organizational performance without making a distinction of BDAC's three dimensions. This study contributes to the literature by thoroughly introducing the notions of BDAC's three core constituents and fully analyzing their relationships with organizational performance. What's more, empirical research on the mechanism of big data analytics' influence on organizational performance is still at a rudimentary stage. The authors address this critical gap by exploring the mediation of dual innovations in the relationship through survey-based research. The research conclusions of this paper provide new perspective for understanding the impact of big data analytics capabilities on organizational performance, and enrich the theoretical research connotation of big data analysis capabilities and dual innovation behavior.

Literature suggests that big data is a new competitive advantage and that it enhance organizational performance. Yet, previous empirical research has provided conflicting results. Building on the resource-based view and the organizational inertia theory, we develop a model to investigate how big data and big data analytics capability affect innovation success. We show that there is a trade-off between big data and big data analytics capability and that optimal balance of big data depends upon levels of big data analytics capability. We conduct a four-year empirical research project to secure empirical data on 1109 data-driven innovation projects from the United States and China. This research is the first time reporting the empirical results. The study findings reveal several surprising results that challenge traditional views of the importance of big data in innovation. For U.S. innovation projects, big data has an inverted U-shaped relationship with sales growth. Big data analytics capability exerts a positive moderating effect, that is, the stronger this capability is, the greater the impact of big data on sales growth and gross margin. For Chinese innovation projects, when big data resource is low, promoting big data analytics capability increases sales growth and gross margin up to a certain point; developing big data analytics capability beyond that point may actually inhibit innovation performance. Our findings provide guidance to firms on making strategic decisions regarding resource allocations for big data and big data analytics capability.

Purpose Diving into big data analytics (BDA) involves systematically collecting, examining, and analyzing vast data volumes, unveiling market trends, valuable insights, and discernible patterns. For manufacturers, BDA offers real-time insights into production processes, identifying inefficiencies, minimizing waste, and optimizing throughput to enhance overall operational efficiency. In an era where BDA is growing, academics and business professionals are exploring strategies to leverage these technologies’ revolutionary potential to gain a competitive edge. This study investigates the nuanced effect of BDA capabilities (BDACs) on organizational creativity, organizational impact, and digital transformation in manufacturing firms. Design/methodology/approach Through a survey, we collected data from manufacturing personnel (n = 404), and data was evaluated using a metanalytical approach (Partial Least Squares Structural Equation Modeling (PLS-SEM), IPMA, and SPSS). Findings This study’s findings suggested that BDACs positively impact organizational creativity, digital transformation, and organizational impact. Organizational creativity, impact, and digital transformation positively affect organizational performance (OP). Furthermore, the findings emphasized that digital transformation (DT) and organizational impact partially mediate BDACs and organizational creativity (OC). Practical implications Our findings illuminate the essential components comprising a BDA capability, revealing the profound impact of nurturing these capabilities on pivotal organizational functions, thereby influencing overall organizational performance. Originality/value This study offers unique insights by investigating the often-overlooked interconnections between Big Data Analytic Capabilities (BDACs), digital transformation, organizational impact, and creativity within manufacturing contexts. While prior research has emphasized BDA’s operational benefits, our study bridges a critical gap by examining how BDACs foster creativity and drive digital transformation, which in turn amplify organizational impact and performance. By uncovering BDACs' mediating effects, this research provides manufacturers with a framework to leverage analytics for not just efficiency but also innovation and sustainable competitive advantage in a rapidly evolving digital landscape.

PurposeThe literature mainly concentrates on the relationships between externally oriented digital transformation (ExtDT), big data analytics capability (BDAC) and business model innovation (BMI) from an intra-organizational perspective. However, it is acknowledged that the external environment shapes the firm's strategy and affects innovation outcomes. Embracing an external environment perspective, the authors aim to fill this gap. The authors develop and test a moderated mediation model linking ExtDT to BMI. Drawing on the dynamic capabilities view, the authors' model posits that the effect of ExtDT on BMI is mediated by BDAC, while environmental hostility (EH) moderates these relationships.Design/methodology/approachThe authors adopt a quantitative approach based on bootstrapped partial least square-path modeling (PLS-PM) to analyze a sample of 200 Italian data-driven SMEs.FindingsThe results highlight that ExtDT and BDAC positively affect BMI. The findings also indicate that ExtDT is an antecedent of BMI that is less disruptive than BDAC. The authors also obtain that ExtDT solely does not lead to BDAC. Interestingly, the effect of BDAC on BMI increases when EH moderates the relationship.Originality/valueAnalyzing the relationships between ExtDT, BDAC and BMI from an external environment perspective is an underexplored area of research. The authors contribute to this topic by evaluating how EH interacts with ExtDT and BDAC toward BMI.

BackgroundAs the uptake of health information technologies increased, most healthcare organizations have become producers of big data. A growing number of hospitals are investing in the development of big data analytics (BDA) capabilities. If the promises associated with these capabilities are high, how hospitals create value from it remains unclear. The present study undertakes a scoping review of existing research on BDA use in hospitals to describe the path from BDA capabilities (BDAC) to value and its associated challenges.MethodsThis scoping review was conducted following Arksey and O’Malley’s 5 stages framework. A systematic search strategy was adopted to identify relevant articles in Scopus and Web of Science. Data charting and extraction were performed following an analytical framework that builds on the resource-based view of the firm to describe the path from BDA capabilities to value in hospitals.ResultsOf 1,478 articles identified, 94 were included. Most of them are experimental research (n=69) published in medical (n=66) or computer science journals (n=28). The main value targets associated with the use of BDA are improving the quality of decision-making (n=56) and driving innovation (n=52) which apply mainly to care (n=67) and administrative (n=48) activities. To reach these targets, hospitals need to adequately combine BDA capabilities and value creation mechanisms (VCM) to enable knowledge generation and drive its assimilation. Benefits are endpoints of the value creation process. They are expected in all articles but realized in a few instances only (n=19).ConclusionsThis review confirms the value creation potential of BDA solutions in hospitals. It also shows the organizational challenges that prevent hospitals from generating actual benefits from BDAC-building efforts. The configuring of strategies, technologies and organizational capabilities underlying the development of value-creating BDA solutions should become a priority area for research, with focus on the mechanisms that can drive the alignment of BDA and organizational strategies, and the development of organizational capabilities to support knowledge generation and assimilation.

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.

Purpose In today’s business landscape, drawing upon the critical role of environmental sustainability, this study investigates the intricate relationship between green human resource management practices (GHRMP), big data analytics capability (BDAC), green competitive advantage (GCA) and environmental performance (EP), further moderated by managerial environmental concern (MEC). Design/methodology/approach This study employs a quantitative approach using the latest version of SmartPLS 4 version 4.0.9.6 on a data sample of 467 participants representing a diverse range of manufacturing SMEs. Data were collected from managers and directors using a structured questionnaire and analyzed using structural equation modeling (SEM). This study contributes to the existing knowledge by integrating GHRMP and BDAC within the GCA framework, providing a comprehensive understanding of how these practices enhance SME`s sustainability. Findings The findings provide valuable insights into the manufacturing sector, aiming to enhance SMEs' green competitive advantage. Implementing GHRMP fosters environmental awareness within the workforce, and building BDAC allows for effectively translating that GHRMP into actionable insights, maximizing the potential for achieving GCA. Furthermore, recognizing MEC’s moderating role strengthens positive environmental outcomes associated with GCA. The findings confirm that GHRMP and BDAC are valuable resources and key drivers contributing to competitive advantage in sustainability of enterprises. Practical implications For SMEs, our findings suggest that strategically integrating GHRMP with BDAC not only boosts environmental stewardship but also improves operational efficiency and market positioning. This research outlines actionable steps for SMEs aiming to achieve sustainability targets while enhancing profitability. This research provides actionable insights for SMEs in strategic decision-making and policy formulation, aiding SMEs in navigating the complexities of sustainable development effectively. Originality/value This study contributes to the existing knowledge by integrating GHRMP and BDAC within the GCA framework, providing a robust theoretical explanation of how HRM practices and BDAC help SMEs gain green competitiveness. The implication of this study reveals that SMEs implementing and integrating green HRM practices with advanced data analytics are more likely to gain competitive advantage. This study draws theoretical support from the resource-based view (RBV) theory, positing that a firm’s sustainable competitive advantage stems from its unique and valuable resources and capabilities that are difficult for competitors to imitate or substitute.

This study examines the antecedent role of organizational culture and the mediating role of digital transformation when promoting big data analytics capabilities. Employing the Competing Values Framework, we scrutinize the influence of various cultural typologies, including digital culture on the successful deployment of digital transformation and the enhancement of big data analytics capabilities. Our analysis utilizes Partial Least Squares Structural Equation Modeling on a dataset of 183 firms to evaluate our hypotheses. The findings reveal that adhocratic, digital and hierarchical cultures significantly foster big data analytics capabilities mediated by digital transformation, which is a dynamic process that needs supportive digital and innovative values. In contrast, market and clan cultures exhibit weaker linkages. By providing empirical evidence and practical implications, this study highlights how organizations with a strong adhocratic and digital cultures outperform those with traditional cultures in their digital transformation and big data analytics capabilities efforts.

Digital transformation has encouraged companies to optimize their digital platform capabilities and big data analytics as strategic resources in creating innovation excellence. This study aims to examine the influence of Digital Platform Capability (DPC) and Big Data Analytics Capability (BDAC) on Innovation Performance (IP) in the telecommunications industry in Indonesia. Data was collected from 331 managerial respondents through a survey and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The results show that both DPC (β = 0.316; T = 5.282; P < 0.001) and BDAC (β = 0.484; T = 8.033; P < 0.001) have a significant positive effect on IP. These findings emphasize the importance of companies' ability to manage digital platform integration and utilize big data analytics to strengthen innovation performance. Theoretically, this study expands on the Resource-Based View (RBV) and Dynamic Capability View (DCV) by emphasizing the role of DPC and BDAC as dynamic resources that support innovation. The practical implications suggest that telecommunications companies need to develop integrated digital strategies, strengthen their analytical infrastructure, and foster a data-driven culture to enhance their competitiveness.

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.