Search results

This paper aims to propose a research model with eight constructs, i.e. BDA leadership, BDA talent quality, BDA security quality, BDA privacy quality, innovation, financial performance, market performance and customer satisfaction.

The research model focuses on whether (1) Big Data Analytics (BDA) leadership influences BDA talent quality, (2) BDA talent quality influences BDA security quality, (3) BDA talent quality influences BDA privacy quality, (4) BDA talent quality influences Innovation and (5) innovation influences a firm's performance (financial, market and customer satisfaction). An instrument was designed and administered electronically to a diverse set of employees (N = 188) in various organizations in the USA. Collected data were analyzed through a partial least square structural equation modeling.

Results showed that leadership significantly and positively affects BDA talent quality, which, in turn, significantly and positively impacts security quality, privacy quality and innovation. Moreover, innovation significantly and positively impacts firm performance. The theoretical and practical implications of the findings are discussed. Recommendations for future research are provided.

The study provides empirical evidence that leadership significantly and positively impacts BDA talent quality. BDA talent quality, in turn, positively impacts security quality, privacy quality and innovation. This is important, as these are all critical factors for organizations that collect and use big data. Finally, the study demonstrates that innovation significantly and positively impacts financial performance, market performance and customer satisfaction. The originality of the research results makes them a valuable addition to the literature on big data analytics. They provide new insights into the factors that drive organizational success in this rapidly evolving field.

The advancements in internet technologies and the use of sophisticated digital devices in supply chain operations incessantly generate enormous amounts of data, which is termed as big data (BD). The BD technologies have brought about a paradigm shift in the supply chain decision-making towards profitability and sustainability. The aim of this work is to address the issue of implementation of the big data analytics (BDA) in sustainable supply chain management (SSCM) by identifying the relevant factors and developing a structural model for this purpose.

Through a comprehensive literature review and experts’ opinion, the crucial factors are found using the PESTEL framework, which covers political, economic, social, technological, environmental and legal factors. The structural model is developed based on the results of the total interpretive structural modelling (TISM) procedure and MICMAC analysis.

The policy support regarding IT, culture of data-based decision-making, inappropriate selection of BDA technologies and the laws related to data security and privacy are found to affect most of the other factors. Also, the company’s vision towards environmental performance and willingness for material and energy optimization are found to be crucial for the environmental and social sustainability of the supply chain.

The study is focused on the manufacturing supply chain in emerging economies. It may be extended to other industry sectors and geographical areas. Also, additional factors may be included to make the model more robust.

The proposed model imparts an understanding of the relative importance and interrelationship of factors. This may be useful to managers to assess their strengths and weaknesses and ascertain their priorities in the context of their organization for developing a suitable investment plan.

The study establishes the importance of BDA for conservation and management of energy and material. This is crucial to develop strategies for enhancing eco-efficiency of the supply chain, which in turn enhances the economic returns for the society.

This study addresses the implementation of BDA in SSCM in the context of emerging economies. It uses the PESTEL framework for identifying the factors, which is a comprehensive framework for strategic planning and decision-making. This study makes use of the TISM methodology for model development and deliberates on the social and environmental implications too, apart from theoretical and managerial implications.

Considering the limited understanding of determinants influencing the adoption of smart mobile tourism app (SMTA) featuring augmented reality (AR) and big data analytics (BDA), privacy concern (PC) and the risk of privacy information disclosure (PI) have threatened SMTA adoption. This study aims to propose an expanded consumer acceptance and use of information technology (UTAUT2) model by including new contextual components, integrated with privacy calculus theory (PCT) model to examine the determinants influencing behavioural intention (BI) to use SMTA.

Personal innovativeness (IN) and privacy information disclosure (PI) are incorporated in UTAUT2 model to determine its effect on SMTA featuring AR and BDA technologies from smart perspective. Both privacy concern (PC) and privacy risk (PR) derived from PCT model are also included to determine its influences on an individual's willingness to disclose privacy information for better-personalised services. We collected responses from 392 targeted participants, resulting in a strong response rate of 84.66%. These responses were analysed statistically using structural equation modeling in both SPSS 22.0 and SmartPLS 3.0.

Findings showed that personal innovativeness (IN), habit (HT) and performance expectancy (PE) significantly affect behavioural intention (BI) while privacy concern (PC) significantly affect privacy information disclosure (PI) to use SMTA. In contrast, effort expectancy (EE), hedonic motivation (HM) and privacy information disclosure (PI) had no significant effects on behavioural intention (BI) while privacy risk (PR) had no significant effects on privacy information disclosure (PI) to use SMTA.

The study findings help tourism practitioners in better comprehending recent trends of SMTA adoption for establishing targeted marketing strategies on apps to improve service quality. In addition, it enables app development companies acquire app users’ preferences to enhance their app development for leading app usage.

This paper aims to identify, synthesise and critically examine the extant academic research on the relation between big data analytics (BDA), corporate accountability and non-financial disclosure (NFD) across several disciplines.

This paper uses a structured literature review methodology and applies “insight-critique-transformative redefinition” framework to interpret the findings, develop critique and formulate future research directions.

This paper identifies and critically examines 12 research themes across four macro categories. The insights presented in this paper indicate that the nature of the relationship between BDA and accountability depends on whether an organisation considers BDA as a value creation instrument or as a revenue generation source. This paper discusses how NFD can effectively increase corporate accountability for ethical, social and environmental consequences of BDA.

This paper presents the results of a structured literature review exploring the state-of-the-art of academic research on the relation between BDA, NFD and corporate accountability. This paper uses a systematic approach, to provide an exhaustive analysis of the phenomenon with rigorous and reproducible research criteria. This paper also presents a series of actionable insights of how corporate accountability for the use of big data and algorithmic decision-making can be enhanced.

This paper discusses how NFD can reduce negative social and environmental impact stemming from the corporate use of BDA.

To the best of the authors’ knowledge, this paper is the first one to provide a comprehensive synthesis of academic literature, identify research gaps and outline a prospective research agenda on the implications of big data technologies for NFD and corporate accountability along social, environmental and ethical dimensions.

With big data (BD), traditional supply chain is shifting to digital supply chain. This study aims to address the issues and challenges in the way toward the implementation of big data analytics (BDA) in sustainable supply chain management (SSCM).

The factors that affect the implementation of BDA in SSCM are identified through a widespread literature review. The PESTEL framework is used for this purpose as it covers all the political, economic, social, technological, environmental and legal factors. These factors are then finalized by means of experts' opinion and analyzed using structural equation modeling (SEM).

A total of 10 factors are finalized with 31 sub-factors, of which sustainable performance, competitive advantage, stakeholders' involvement and capabilities, lean and green practices and improvement in environmental performance are found to be the critical factors for the implementation of BDA in SSCM.

This research has taken up the case of Indian manufacturing industry. It can be diversified to other geographical areas and industry sectors. Further, the quantitative analysis may be undertaken with structured or semi-structured interviews for validation of the proposed model.

This research provides an insight to managers regarding the implementation of BDA in SSCM by identifying and examining the influencing factors. The results may be useful for managers for the implementation of BDA and budget allocation for BDA project.

The result includes green practices and environmental performance as critical factors for the implementation of BDA in SSCM. Thus the research establishes a positive relationship between BDA and sustainable manufacturing that ultimately benefits the environment and society.

This research addresses the challenges in the implementation of BDA in SSCM in Indian manufacturing sector, where such application is at its nascent stage. The use of PESTEL framework for identifying and categorizing the factors makes the study more worthwhile, as it covers full spectrum of the various factors that affect the strategic business decisions.

The study aims to identify the potential drivers of big data analytics (BDA) practices in the supply chain and develop a sustainability evaluation model to evaluate drivers of big data for sustainability development.

The mixed-method approach was applied to assess sustainability dimensions and calculate the score using two phases. In Phase I, the BDA drivers in the e-commerce industry were finalised using the partial least square based structural equation modelling (PLS-SEM) method. In Phase II, a case study in the Indian fashion e-commerce industry was carried out to evaluate sustainability dimensions with respect to drivers of BDA and the sustainability score was calculated using the fuzzy analytical hierarchical process (AHP) method.

The index for economic sustainability (0.220), social sustainability (0.142) and environmental sustainability (0.182) were derived. The higher index value of economic sustainability compared to social sustainability and environmental sustainability signified those drivers of big data bring social and environmental uncertainty along with economic sustainability.

The study will help practitioners promote BDA use for developing environmental/social/economic sustainability in supply chains. Policymakers must ensure whether the integration of BDA practices brings down cost and brings strategic value for ensuring big data success. The study will help managers decide a constant trade-off between the requirement for social, environmental and economic performance.

The study corroborates and adds to the BDA literature by emphasising the positive role of BDA in sustainability development in the supply chain area and highlighting the significant role of different drivers of BDA in sustainability development.

The proposed article is aimed at exploring the opportunities, challenges and possible outcomes of incorporating big data analytics (BDA) into health-care sector. The purpose of this study is to find the research gaps in the literature and to investigate the scope of incorporating new strategies in the health-care sector for increasing the efficiency of the system.

Fora state-of-the-art literature review, a systematic literature review has been carried out to find out research gaps in the field of healthcare using big data (BD) applications. A detailed research methodology including material collection, descriptive analysis and categorization is utilized to carry out the literature review.

BD analysis is rapidly being adopted in health-care sector for utilizing precious information available in terms of BD. However, it puts forth certain challenges that need to be focused upon. The article identifies and explains the challenges thoroughly.

The proposed study will provide useful guidance to the health-care sector professionals for managing health-care system. It will help academicians and physicians for evaluating, improving and benchmarking the health-care strategies through BDA in the health-care sector. One of the limitations of the study is that it is based on literature review and more in-depth studies may be carried out for the generalization of results.

There are certain effective tools available in the market today that are currently being used by both small and large businesses and corporations. One of them is BD, which may be very useful for health-care sector. A comprehensive literature review is carried out for research papers published between 1974 and 2021.

This study aims to propose a model based on philosophical thoughts of Dr S.R Ranganathan and the lean-startup method for the execution of big data analytics (BDA) in libraries. The research paves a way to understand the role and required competencies of Library and Information Science (LIS) professionals for the implementation of BDA in libraries.

In the BDA analytics context, a session with a proposed model was presented to the audience to get the response of librarians about the required competencies and skills. The research tool was developed based on the literature review to know the role of LIS professionals and their required competencies/skills for BDA. The questionnaire was distributed in the BDA session to collect the responses of the participating audience on the variables that focused on the role and core competencies of LIS professionals in BDA. In the analysis of results, the independent t-test was applied to know the mean value of the overall response rate.

The findings show that perceptions of LIS professionals in the understanding of BDA ranked high in data privacy, data availability, data organization and data literacy. Digital data curation, policies supervision and providing the data consultancy also showed a significant relationship among these variables. Besides, the correlation between the required skills for BDA, metadata skills, data ethics, data acquisition, data cleaning, data organization, data analysis, digital curation, data clustering, data protection rules and digital visualization also showed a beneficial relationship.

This study also helps to understand the perspective of LIS professionals for the implementation of BDA in libraries and to fill the literature gap in the respective.

This study aims to examine the key antecedents influencing the private firm’s intention to adopt big data analytics (BDA) in developing economies. To do so, the study follows the sequential explanatory approach.

To test the hypothesized model that draws on the technology–organization–environment (TOE) framework paired with the diffusion of innovation (DOI) theory, a purposive sampling technique was applied to gather data from 156 IT and management domain experts from the private firms that intend to adopt BDA and operate in Pakistan’s service industry, including telecommunication, information technology, agriculture, and e-commerce. The data were analysed using the partial least squares structural equations modelling (PLS-SEM) technique and complemented with qualitative analysis of 10 semi-structured interviews in NVIVO 12 based on grounded theory.

The empirical findings revealed that the two constructs – perceived benefits and top management support – are the powerful drivers of a firm’s intention to adopt BDA in the private sector, whereas IT infrastructure, data quality, technological complexity and financial readiness, along with the moderators, BDA adoption of competitors and government policy and regulation, do not significantly influence the intention. In addition, the qualitative analysis validates and further complements the SEM findings.

Unlike the previous studies on technology adoption, this study proposed a unique research model with contextualized indicators to measure the constructs relevant to private firms, based on the TOE framework and DOI theory, to investigate the causal relationship between drivers and intention. Furthermore, the findings of PLS-SEM were complemented by qualitative analysis to validate the causation. The findings of this study have both theoretical and practical implications.

Regardless of the diverse research on big data analytics (BDA) across different supply chains, little attention has been paid to exploit this information across service supply chains. The healthcare supply chains, where supply chain operations consume the second highest expenditures, have not completely attained the potential gains from data analytics. So, this paper explores the challenges of BDA at various levels of healthcare supply chains.

Drawing on the resource-based view (RBV), this research explores the various challenges of big data at organizational and operational level of different nodes in healthcare supply chains. To demonstrate the links among supply chain nodes, the authors have used a supplier-input-process-output-customer (SIPOC) chart to list healthcare suppliers, inputs (such as employees) supplied and used by the main healthcare processes, outputs (products and services) of these processes, and customers (patients and community).

Using thematic analysis, the authors were able to identify numerous challenges and commonalities among these challenges for the case of healthcare supply chains across United Arab Emirates (UAE). An applicable exploration on organizational (Socio-technical) and operational challenges to BDA can enable healthcare managers to acclimate efficient and effective strategies.

The identified common socio-technical and operational challenges could be verified, and their impacts on the sustainable performance of various supply chains should be explored using formal research methods.

This research advances the body of literature on BDA in healthcare supply chains in that (1) it presents a structured approach for exploring the challenges from various stakeholders of healthcare chain; (2) it presents the most common challenges of big data across the chain and finally (3) it uses the context of UAE where government is focusing on medical tourism in the coming years.

Originality of this work stems from the fact that most of the previous academic research in this area has focused on technology perspectives, a clear understanding of the managerial and strategic implications and challenges of big data is still missing in the literature.