Human-related capabilities in big data analytics: a taxonomy of human factors with impact on firm performance

Influencing mechanism of the intellectual capability of big data analytics on the operational performance of enterprises

In the era of big data, data processing capability is key to gaining a competitive advantage for businesses. With appropriate technical and organizational resources in place, enterprises can extract considerable value from the vast amount of available data, thereby increasing their competitive advantage. Therefore, to utilize big data resources effectively, enterprises should focus on improving the intellectual abilities of big data analysts. Big data analytics intellectual capability (BDAIC) refers to the specialized skills and knowledge that employees of the enterprise possess, including technical, technical management, business, and relational knowledge, that would enable them to use analytics tools to accomplish organizational tasks and shape the core competitiveness of an enterprise. This study constructs a theoretical model that focuses on the mediating role of person-tool fit and examines the mechanisms by which BDAIC affects an enterprise's operational performance. The results show that BDAIC, which contains four basic categories of knowledge, positively influences an enterprise's operational efficiency. Additionally, person-tool matching mediates BDAIC's effect on an enterprise's operational performance. These findings explore the latest avenues of exploration in the research paradigm of big data analytics. Furthermore, this study has important implications for practitioners trying to use big data to improve business performance.

Measuring and managing service productivity: a meta-analysis

Despite service productivity’s scholarly prominence and practical relevance, past research in marketing has primarily adopted isolated perspectives from which disjointed empirical findings reign supreme. As the acquisition of knowledge about service productivity accelerates, the collective evidence becomes more interdisciplinary but also more fragmented. This study uses a meta-analysis to integrate the substantial empirical record on service productivity. We formulate hypotheses on the moderators of service productivity-determinant relationships and meta-analyze 77 articles, relying on 81 independent samples with a cumulative sample size of 30,238 participants to test our predictions. Our meta-analysis provides empirical evidence that service quality and internal efficiency must be considered jointly, not in isolation, to maximize profitability. Thus, relying on one aspect in isolation is less appropriate for measurement purposes and might not lead to positive outcomes. This important finding should concern service scholars and managers because falling profit margins require service firms to move beyond the traditional manufacturing productivity that separates service quality from internal efficiency and consider service productivity as a profitability concept. In sum, our findings provide a viable model to explain the main service productivity determinants and moderating variables, offering valuable insights for practitioners that aim to deliver cost-efficient service quality and promising future research directions.

Leadership's long arm: The positive influence of digital leadership on managing technology-driven change over a strengthened service innovation capacity

Introduction

In this qualitative study, we examine digital leadership (DL) capabilities and their positive influence on the management of technology-driven change by leveraging service innovations. The context of digital transformation (DT) has triggered a new leadership paradigm, among others referred to as digital leadership (DL). However, despite its practical relevance, leadership research has yet paid little attention to conceptualise DL as an approach to digitally transform organisations.

Methods

Drawing on mid- and top-level mangers' experiences with service innovation projects, and based on Grounded Theory, we develop a taxonomy of DL-related capabilities and a conceptual framework which exemplifies their influences on dynamic service innovation capabilities (DSICs). DSICs build on the dynamic capabilities view (DCV) and represent the "organisational muscle" to repeatedly deliver service innovations indicating an effective management of technology-driven change.

Results and Discussion

Taxonomy results show that aggregated dimensions in terms of a digital leader's personal, social, and organisational capital serve as underpinnings (DL-related capabilities) to drive strategic change in DT contexts. The conceptual framework further reveals that especially the personal and organisational capital of a digital leader owns several strong and moderate influences on DSICs which demonstrates DL's "long arm" on the management of technology-driven change. Our findings contribute to leadership research by advancing the conceptualisation of DL and by adding a novel micro-foundational perspective towards the DCV discourse. As organisations struggle to realise the full benefits of DT initiatives, our results also provide a valuable contribution for practitioners by supporting them to strategically prepare for the human-related challenges of DT.

Big data and decision quality: the role of management accountants’ data analytics skills

Purpose

Existing research on the relationship between big data and organizational decision quality is still few and far between, and what does exist often assumes direct effects of big data on decision quality. More recent research indicates that such direct effects may be too simplistic, and in particular, an organization’s overall human skills are often not considered sufficiently. Inspired by the knowledge-based view, we therefore propose that interactions between three aspects of big data usage and management accountants’ data analytics skills may be key to reaching high-quality decisions. The purpose of this study is to test these predictions based on a survey of US firms.

Design/methodology/approach

The authors draw on survey data from 140 US firms. This survey has been conducted via MTurk in 2020.

Findings

The results of the study show that the quality of big data sources is associated with higher perceived levels of decision quality. However, according to the results, the breadth of big data sources and a data-driven culture only improve decision quality if management accountants’ data analytics skills are highly developed. These results point to the important, but so far unexamined role of an organization’s management accountants and their skills for translating big data into high-quality decisions.

Practical implications

The present study highlights the importance of an organization’s human skills in creating value out of big data. In particular, the findings imply that management accountants may need to increasingly draw on data analytics skills to make the most out of big data for their employers.

Originality/value

This study is among the first, to the best of the authors’ knowledge, to provide empirical proof of the relevance of an organization’s management accountants and their data analytics skills for reaching desirable firm-level outcomes. In addition, this study thus adds to the further advancement of the knowledge-based view by providing evidence that in contemporary big-data environments, interactions between tacit and explicit knowledge seem crucial for driving desirable firm-level outcomes.

Literature reviews as independent studies: guidelines for academic practice

Review articles or literature reviews are a critical part of scientific research. While numerous guides on literature reviews exist, these are often limited to the philosophy of review procedures, protocols, and nomenclatures, triggering non-parsimonious reporting and confusion due to overlapping similarities. To address the aforementioned limitations, we adopt a pragmatic approach to demystify and shape the academic practice of conducting literature reviews. We concentrate on the types, focuses, considerations, methods, and contributions of literature reviews as independent, standalone studies. As such, our article serves as an overview that scholars can rely upon to navigate the fundamental elements of literature reviews as standalone and independent studies, without getting entangled in the complexities of review procedures, protocols, and nomenclatures.

The Impact of Big Data Management Capabilities on the Performance of Manufacturing Firms in Asian Economy During COVID-19: The Mediating Role of Organizational Agility and Moderating Role of Information Technology Capability

The main purpose of this study is to examine the impact of the big data management capabilities on the performance of manufacturing firms in the Asian Economy during coronavirus disease 2019 (COVID-19). In addition to this, this study is also planned to examine the mediating role of organizational agility in the relationship between the big data management capabilities and the performance of Chinese manufacturing firms during COVID-19. Last, this study has examined the moderating role of information technology capability in the relationship between the big data management capabilities and performance of Chinese manufacturing firms during COVID-19. This study adopted the quantitative method of research with a cross-sectional technique. This study employed a questionnaire to gather the data as a research instrument. This study has used the purposive sampling method by keeping in mind the context of this study. Employees of the Chinese SMEs that were at least 10 years old were the population of this study. The research model was being analyzed by employing the "partial least squares" technique through statistical software the Smart PLS version 3. The results are in line with the proposed hypothesis. This study contributed to the literature by suggesting characteristics that promote or prevent the organization from successfully implementing big data and pointed out that showing resistance in information management system implementation may have different effects on the organization. Besides, the study also discussed the relationship between such information systems and the organization. Findings of these two factors provide insights for the practitioners and researchers in assessing the success or failure of organizations for using big data.

Identifying the Key Big Data Analytics Capabilities in Bangladesh’s Healthcare Sector

The study explores the crucial big data analytics capabilities (BDAC) for healthcare in Bangladesh. After a rigorous and extensive literature review, we list a wide range of BDAC and empirically examine their applicability in Bangladesh’s healthcare sector by consulting 51 experts with ample domain knowledge. The study adopted the DEcision MAking Trial and Evaluation Laboratory (DEMATEL) method. Findings highlighted 11 key BDAC, such as using advanced analytical techniques that could be critical in managing big data in the healthcare sector. The paper ends with a summary and puts forward suggestions for future studies.