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The pandemic has severely affected sharing economy businesses specially ride-hailing services due to high levels of human-touch points. This research investigates the impact of COVID-19 infodemic, identification, and emotional support on intention to use ride-hailing. The authors further examine the moderating effects of internalization, compliance, social currency and informational support in the proposed conceptual model.

The data were collected from 412 respondents from Delhi in India, and hypothesized relationships were tested using hierarchical regression.

The results indicate that COVID-19 infodemic has a negative association with intention to use ride-hailing. Identification and emotional support show a positive association with intention to use ride-hailing. The results also support internalization and compliance as moderators between infodemic, identification and intention to use ride-hailing. Further, social currency and information support moderate the relationship between emotional support and intention to use.

This is the first research, which adds to the expanding literature on ride-hailing, especially during the post-pandemic period of “new normal”.

The purpose of this paper is to improve the understanding of the integration of business process management (BPM), business process re-engineering (BPR) and business process innovation (BPI) with big data. It focusses on synthesizing research published in the period 2006-2016 to establish both what the authors know and do not know about this topic, identifying areas for future research.

The research is based on a review of 49 published papers on big data, BPM, BPR and BPI in the top journals in the field 2006-2016.

In this paper, the authors have identified the most influential works based on citations and PageRank methods. Through network analysis the authors identify four major clusters that provide potential opportunities for future investigation.

It is important for practitioners to be aware of the benefits of big data, BPM, BPR and BPI integration. This paper provides valuable insights for practitioners.

This paper is based on a comprehensive literature review, which gives big data researchers the opportunity to understand business processes in depth. In addition, highlighting many gaps in the current literature and developing an agenda for future research, will save time and effort for readers looking to research topics within big data and business processes.

The purpose of this paper is to illustrate the systematic role played by Six Sigma methodology in improving the quality of healthcare. The literature review identifies the relevant opportunities for successful introduction and development of Six Sigma approach in healthcare sector.

A systematic methodology to identifying literature on Six Sigma in healthcare is presented. Web of Science, Medline, Emerald Insight, ASQ and ProQuest databases (1998-2016) were searched, and 68 papers of fair methodological quality were identified.

The findings of the systematic review reveal a growing interest in research on Six Sigma adoption in healthcare. The findings indicate that Six Sigma applications in healthcare have been focused on the entire hospital with no real focus on a particular department or function. The key findings on benefits, success factors, challenges and common tools of Six Sigma from the existing literature are also presented in the paper.

The papers included in the systematic review were peer-reviewed papers available in English. Due to these limitations, relevant papers may have been excluded. Moreover, the authors have excluded all conference and white papers for their inclusion in this study.

This paper can serve as a guide on how Six Sigma approach can be applied to improve the quality of healthcare. The authors also believe that this is possibly the most comprehensive systematic literature review on the topic and will set the foundation for various research avenues based on the key findings of this study.

The purpose of paper is twofold. First, it provides a consolidated overview of the existing literature on “big data” and second, it presents the current trends and opens up various future directions for researchers who wish to explore and contribute in this rapidly evolving field.

To achieve the objective of this study, the bibliographic and network techniques of citation and co-citation analysis was adopted. This analysis involved an assessment of 57 articles published over a period of five years (2011-2015) in ten selected journals.

The findings reveal that the number of articles devoted to the study of “big data” has increased rapidly in recent years. Moreover, the study identifies some of the most influential articles of this area. Finally, the paper highlights the new trends and discusses the challenges associated with big data.

This study focusses only on big data concepts, trends, and challenges and excludes research on its analytics. Thus, researchers may explore and extend this area of research.

To the knowledge of the authors, this is the first study to review the literature on big data by using citation and co-citation analysis.

Big data and predictive analytics (BDPA) has received great attention in terms of its role in making business decisions. However, current knowledge on BDPA regarding how it might link organizational capabilities and organizational performance (OP) remains unclear. Drawing from the resource-based view, the purpose of this paper is to propose a model to examine how information technology (IT) deployment (i.e. strategic IT flexibility, business–BDPA partnership and business–BDPA alignment) and HR capabilities affect OP through BDPA.

To test the proposed hypotheses, structural equation modeling is applied on survey data collected from 159 Indian firms.

The results show that BDPA diffusion mediates the influence of IT deployment and HR capabilities on OP. In addition, there is a direct effect of IT deployment and HR capabilities on BDPA diffusion, which also has a direct relationship with OP.

Through this study, authors demonstrate that IT deployment and HR capabilities have an indirect impact on OP through BDPA diffusion.

Although there is a growing body of literature on human resource (HR) outsourcing, there are still unresolved issues regarding the level of outsourcing HR activities initiated in firms having different strategies and structures. This is relevant to examine because firms with different orientations (strategy and structure) cannot do HR outsourcing at the same level. The paper aims to discuss these issues.

A theoretical framework is developed and empirically validated using survey data from 184 manufacturing firms in India. The analysis involved testing a number of hypotheses through statistical techniques using the confirmatory factor analysis and structural equation modeling.

The results indicate that firms having prospector orientation are more likely to outsource non-core HR activities as compared to firms having analyzer and defender orientation strategies, in that order. However, in comparison to non-core HR activities, core HR activities are less likely to be outsourced by firms with different strategic orientations (prospector, analyser, and defender).

This research study underscores the linkage between HR outsourcing and strategy and structure of a firm by developing a theoretical framework.

The purpose of this paper is to review the existing literature on supply chain performance measures and metrics (PMMs). It provides a critical evaluation of 234 articles published in past 24 years.

The paper examines the studies published from 1991 to 2014 by adopting the bibliometric technique of citation and co-citation analysis.

The analysis of the results indicate that the number of articles on supply chain PMMs is increasing at its fastest pace in the past few years. Furthermore, the study identifies some of the most influential articles on performance measurement and metrics. Finally, it concludes that there has been a transition from traditional to more sophisticated performance measurement system.

This study focuses only on supply chain performance measurement and metrics and excludes research on performance management and control. Thus, researchers may explore and extend this area of research.

To the knowledge of the authors, this is the first study to review the literature on supply chain PMMs by using citation and co-citation analysis. The study includes 234 articles over the time of 24 years (1991-2014).

The COVID-19 pandemic era has severely hampered the economy over the globe. However, the manufacturing organizations across all the countries have struggled heavily, as they were among the least who worked on online mode. The organizations are adopting various innovative quality methodologies to improve their performance. In this regard, they are adopting the Sustainable Lean Six Sigma (SLSS) concept and Industry 4.0 technologies to develop products at a faster rate. The use of Industry 4.0 technologies may reduce material movement and supply chain disruptions with the help of smart intelligent systems. There is a strong synergy between SLSS and Industry 4.0 technologies, resulting in an integrated approach for adoption. This study aims to develop a framework that practitioners can use to adopt Industry 4.0-SLSS practices effectively.

This study portrays 31 Industry 4.0-SLSS practices and 22 performance metrics identified through a literature review to improve the manufacturing supply chain performance. To compute the weights of these practices, the Robust Best–Worst Method (RBWM) is used. The Pythagorean fuzzy combined compromise solution (PF-CoCoSo) method is used to rank performance metrics.

According to the RBWM results, “Process Development Practices (PDP)” are first among the major criteria, followed by “Organizational Management Practices (OMP)” at second, “Technology Adoption Practices (TAP)” at third, “Strategy Management Practices (SMP)” at fourth and “Executive Management Practices (EMP)” at fifth, whereas the PF-CoCoSo method resulted in the performance metric “On time product delivery” ranking first.

The identified practices have the potential to significantly improve the performance of the manufacturing supply chain. Practices that encourage a sustainable manufacturing supply chain and the usage of emerging technology will benefit organizational effectiveness. Managers can assess performance using prioritized performance metrics.

During the COVID-19 pandemic era, this is one of the unique attempts to provide a framework to improve the manufacturing supply chain performance. This study integrates and identifies Industry 4.0-SLSS practices and performance metrics for enhancing overall performance.

The emergent field of Internet of Things (IoT) has been evolving rapidly with a geometric growth in the number of academic publications in this field. The purpose of this paper is to review the literature of IoT in past 16 years using rigorous bibliometric and network analysis tools, offering at the same time future directions for the IoT research community and implications for managers and decision makers.

The authors adopted the techniques of bibliometric and network analysis. The paper reviewed the articles published on IoT from 2000 to 2015.

This study identifies top contributing authors; key research topics related to the field; the most influential works based on citations and PageRank; and established and emerging research clusters. Scholars are encouraged to further explore this topic.

This study focusses only on vision and applications of IoT. Scholars may explore various other aspects of this area of research.

To the best of authors’ knowledge, this is the first study to review the literature on IoT by using bibliometric and network analysis techniques. The study is unique as it spans a long time period of 16 years (2000-2015). The study proposes a five-cluster classification of research themes that may inform current and future research in IoT.