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The study aims to determine the long and short-term causal relationships between the variables associated with the adjustment of monetary policy and the stock market in India in the presence of structural breaks.

The study employed the autoregressive distributed lag (ARDL) bounds test and the Error Correction Model to assess long- and short-term causal relationships. The study also used non-frequentist Bayesian inferences for the validity of estimation robustness. The Bai–Perron test is used to identify breakpoint dates for the Indian stock market index, and the Granger Causality test is employed to ascertain the direction of causality.

The F-bounds test reveals cointegration among the variables throughout the examined period. Specifically, the weighted average call money rate (WACR), inflation (WPI), currency exchange rate (EXE), and broad money supply (M3) exhibit statistical significance with precise signs. Furthermore, the study identifies the negative impact of the COVID-19 outbreak in March 2020 on the Indian stock market.

Although the study provides significant insights, it is not exempt from constraints. A significant limitation is selecting a relatively limited time period, specifically from April 2008 to September 2023. The limited time frame of this study may restrict the applicability of the results to more comprehensive economic settings, as dynamics between the monetary policy and the stock market can be influenced by multiple factors over varying time periods. Furthermore, the utilisation of the Weighted Average Call Money Rate (WACR) rather than policy rates such as the Repo rate presents an additional constraint as it may not comprehensively account for the impacts of particular policy initiatives, thereby disregarding essential complexities in the connection between monetary policy variables and financial markets.

The findings of the study suggest that investors and portfolio managers should consider economic issues while developing long-term investing plans. Reserve Bank of India should exercise prudence to prevent any discretionary measures that may lead to a rise in interest rates since this adversely affects the stock market. To mitigate risk, investors should closely monitor the adjustment of monetary policy variables.

The study has important social implications, especially regarding the lower levels of financial literacy among investors in India. Considering the complex nature of the study’s emphasis on monetary policy adjustments and their impact on the stock market. Investors face the risk of significant losses due to unexpected adjustments in monetary policy. Many individuals may need help understanding how policy changes impact their investments. Therefore, RBI must consider both price and financial stability when formulating monetary policies. Furthermore, market participants should consider the potential impact of fluctuating monetary policy variables when devising their long-term investment strategies. Given that adjustments in interest rates can markedly affect stock market dynamics, investors must carefully assess the implications of monetary policy decisions on their portfolios.

The study uses dummy variables in the ARDL model to represent structural breaks that emerged from the COVID-19 pandemic (as determined by the Bai–Perron multiple breakpoint test). The study also used the Perron unit root test to find out the stationary of the series in the presence of structural breaks. Additionally, the study also employed Bayesian inferences to affirm the robustness of the estimates.

The characteristics of the Internet of Things (IoT) are such that traditional models of trust developed within interpersonal, organizational, virtual and information systems contexts may be inappropriate for use within an IoT context. The purpose of this paper is to offer empirically generated understandings of trust within potential IoT applications.

In an attempt to capture and communicate the complex and all-pervading but frequently inconspicuous nature of ubiquitous technologies within potential IoT techno-systems, propositions developed are investigated using a novel mixed methods research design combining a videographic projective technique with a quantitative survey, sampling 1,200 respondents.

Research findings suggest the dimensionality of trust may vary according to the IoT techno-service context being assessed.

The contribution of this paper is twofold. First, and from a theoretical perspective, it offers a conceptual foundation for trust dimensions within potential IoT applications based upon empirical evaluation. Second, and from a pragmatic perspective, the paper offers insights into how findings may guide practitioners in developing appropriate trust management systems dependent upon the characteristics of particular techno-service contexts.

This paper aims to conduct an interdisciplinary systematic literature review (SLR) of fake news research and to advance the socio-technical understanding of digital information practices and platforms in business and management studies.

The paper applies a focused, SLR method to analyze articles on fake news in business and management journals from 2010 to 2020.

The paper analyzes the definition, theoretical frameworks, methods and research gaps of fake news in the business and management domains. It also identifies some promising research opportunities for future scholars.

The paper offers practical implications for various stakeholders who are affected by or involved in fake news dissemination, such as brands, consumers and policymakers. It provides recommendations to cope with the challenges and risks of fake news.

The paper discusses the social consequences and future threats of fake news, especially in relation to social networking and social media. It calls for more awareness and responsibility from online communities to prevent and combat fake news.

The paper contributes to the literature on information management by showing the importance and consequences of fake news sharing for societies. It is among the frontier systematic reviews in the field that covers studies from different disciplines and focuses on business and management studies.

With the progress of new technologies of information and communication, more and more producers of data exist. On the other hand, the web forms a huge support of all these kinds of data. Unfortunately, existing data is not proper due to the existence of the same information in different sources, as well as erroneous and incomplete data. The aim of data integration systems is to offer to a user a unique interface to query a number of sources. A key challenge of such systems is to deal with conflicting information from the same source or from different sources. We present, in this paper, the resolution of conflict at the instance level into two stages: references reconciliation and data fusion. The reference reconciliation methods seek to decide if two data descriptions are references to the same entity in reality. We define the principles of reconciliation method then we distinguish the methods of reference reconciliation, first on how to use the descriptions of references, then the way to acquire knowledge. We finish this section by discussing some current data reconciliation issues that are the subject of current research. Data fusion in turn, has the objective to merge duplicates into a single representation while resolving conflicts between the data. We define first the conflicts classification, the strategies for dealing with conflicts and the implementing conflict management strategies. We present then, the relational operators and data fusion techniques. Likewise, we finish this section by discussing some current data fusion issues that are the subject of current research.

The poor leadership style is a key obstacle to the effective implementation of Industry 4.0 technologies. To successfully apply the Industry 4.0 technologies, which can enhance the sustainability of firms, senior management needs to be inspiring and transformational. On the other hand, numerous factors can hinder the Industry 4.0 transition and “Circular Supply Chain (CSC)” transformation. Therefore, the main purpose of this study is to evaluate the related barriers of CSCs in the era of Industry 4.0 transition.

The current study developed an innovative decision-making approach with the help of the “Combined Compromise Solution (CoCoSo)” method and “Criteria Importance Through Intercriteria Correlation (CRITIC)” method on the “q-Rung Orthopair Fuzzy Sets (q-ROFSs).” CRITIC in this combined method was used to predict the importance or weighting degrees of the CSCs barriers in the age of Industry 4.0 transition.

The results of this study found that the absence of knowledge about the Industry 4.0 technologies and circular approaches was the first barrier followed by the problems associated with data security in relationship management in circular flows, the deficiency of knowledge regarding the data management among stakeholders and the lack of awareness about the potential benefits of autonomous systems in labor-oriented “End-of-Life (EOL)” activities for CSCs in the era of Industry 4.0 transition.

A limitation may be that despite the generalizability of the proposed framework, the results may differ when it is implemented in different sectors. By emphasizing the obstacles to sustainable operations of supply chains (SCs) in the context of circular economy (CE) and Industry 4.0, researchers working in the same domain may be encouraged to find ways to remove such obstacles in different settings. As suggested in this study, the priority of various barriers helps researchers suggest effective strategies for the sustainable development of companies within the current dynamic business atmosphere.

The findings of this paper can aid industry practitioners in fixing their attention on the digitization or automation of their systems in the context of sustainability or resource circularity. Note that within the current context of CE, one of the crucial issues is how to conserve the existing resources; the answer to this question can save the environment.

The current paper proposed a new multi-criteria decision-making method using q-ROFSs to analyze, rank and evaluate the CSC barriers in the age of Industry 4.0 transition. To this end, a new decision-making approach with the help of CRITIC and CoCoSo methods on q-ROFSs called q-ROF-CRITIC-CoCoSo was introduced to evaluate the CSCs barriers in the era of Industry 4.0 transition.

Business models are increasingly recognized as a concept to support innovation in organizations. The implementation and operation of a new or altered business model involves the (re-)design of an organization's business processes and their successful execution. This study reviews and synthesizes the existing body of literature to guide organizations in systematically moving from a business model design to the implementation and operation of the business model through their underlying business processes.

A systematic literature review of the methods that bridge business models and business processes is performed. The selected 34 studies are classified according to the method's characteristics and the support in the design, implementation and operation of business models.

The results of the systematic review provide an overview of existing methods that organizations can adopt when moving from business model design into the implementation and operation of their business model using processes.

This work provides a comprehensive overview and detailed insight into the existing methods that align business models and business processes. It increases the understanding on how these two concepts can be synthesized to support more effective digital innovation in organizations. Based on the review results, knowledge gaps are identified and an agenda for future research bridging the fields of business models and business processes is proposed.

The purpose of the study was to detect trends in human resource management (HRM) research presented in journals during the 2000–2020 timeframe. The research question is: How are the interests of researchers changing in the field of HRM and which topics have gained popularity in recent years?

The approach adopted in this study was designed to overcome all the limitations specific to the systematic literature reviews and bibliometric studies presented in the Introduction. The full texts of papers were analyzed. The text-mining tools detected first clusters and then trends, moreover, which limited the impact of a researcher's bias. The approach applied is consistent with the general rules of systematic literature reviews.

The article makes a threefold contribution to academic knowledge. First, it uses modern methodology to gather and synthesize HRM research topics. The proposed approach was designed to allow early detection of nascent, non-obvious trends in research, which will help researchers address topics of high value for both theory and practice. Second, the results of our study highlight shifts in focus in HRM over the past 19 years. Third, the article suggests further directions of research.

In this study, the approach designed to overcome the limitations of using systematic literature review was presented. The analysis was done on the basis of the full text of the articles and the categories were discovered directly from the articles rather than predetermined. The study's findings may, however, potentially be limited by the following issues. First, the eligibility criteria included only papers indexed in the Scopus and WoS database and excluded conference proceedings, book chapters, and non-English papers. Second, only full-text articles were included in the study, which could narrow down the research area. As a consequence, important information regarding the research presented in the excluded documents is potentially lost. Third, most of the papers in our database were published in the International Journal of Human Resource Management, and therefore such trends as “challenges for international HRM” can be considered significant (long-lasting). Another – the fourth – limitation of the study is the lack of estimation of the proportion between searches in HRM journals and articles published in other journals. Future research may overcome the above-presented limitations. Although the authors used valuable techniques such as TF-IDF and HDBSCAN, the fifth limitation is that, after trends were discovered, it was necessary to evaluate and interpret them. That could have induced researchers' bias even if – as in this study – researchers from different areas of experience were involved. Finally, this study covers the 2000–2020 timeframe. Since HRM is a rapidly developing field, in a few years from now academics will probably begin to move into exciting new research areas. As a consequence, it might be worthwhile conducting similar analyses to those presented in this study and compare their results.

The present study provides an analysis of HRM journals with the aim of establishing trends in HRM research. It makes contributions to the field by providing a more comprehensive and objective review than analyses resulting from systematic literature reviews. It fills the gap in literature studies on HRM with a novel research approach – a methodology based on full-text mining and a big data toolset. As a consequence, this study can be considered as providing an adequate reflection of all the articles published in journals strictly devoted to HRM issues and which may serve as an important source of reference for both researchers and practitioners. This study can help them identify the core journals focused on HRM research as well as topics which are of particular interest and importance.

Managers must make numerous strategic decisions in order to initiate and implement a business model innovation (BMI). This paper examines how managers perceive the management team interacts when making BMI decisions. The paper also investigates how group biases and board members’ risk willingness affect this process.

Empirical data were collected through 26 in-depth interviews with German managing directors from 13 companies in four industries (mobility, manufacturing, healthcare and energy) to explore three research questions: (1) What group effects are prevalent in BMI group decision-making? (2) What are the key characteristics of BMI group decisions? And (3) what are the potential relationships between BMI group decision-making and managers' risk willingness? A thematic analysis based on Gioia's guidelines was conducted to identify themes in the comprehensive dataset.

First, the results show four typical group biases in BMI group decisions: Groupthink, social influence, hidden profile and group polarization. Findings show that the hidden profile paradigm and groupthink theory are essential in the context of BMI decisions. Second, we developed a BMI decision matrix, including the following key characteristics of BMI group decision-making managerial cohesion, conflict readiness and information- and emotion-based decision behavior. Third, in contrast to previous literature, we found that individual risk aversion can improve the quality of BMI decisions.

This paper provides managers with an opportunity to become aware of group biases that may impede their strategic BMI decisions. Specifically, it points out that managers should consider the key cognitive constraints due to their interactions when making BMI decisions. This work also highlights the importance of risk-averse decision-makers on boards.

This qualitative study contributes to the literature on decision-making by revealing key cognitive group biases in strategic decision-making. This study also enriches the behavioral science research stream of the BMI literature by attributing a critical influence on the quality of BMI decisions to managers' group interactions. In addition, this article provides new perspectives on managers' risk aversion in strategic decision-making.

In this study the authors aim to outline new ways of information extraction for automated valuation models, which in turn would help to increase transparency in valuation procedures and thus contribute to more reliable statements about the value of real estate.

The authors hypothesize that empirical error in the interpretation and qualitative assessment of visual content can be minimized by collating the assessments of multiple individuals and through use of repeated trials. Motivated by this problem, the authors developed an experimental approach for semi-automatic extraction of qualitative real estate metadata based on Comparative Judgments and Deep Learning. The authors evaluate the feasibility of our approach with the help of Hedonic Models.

The results show that the collated assessments of qualitative features of interior images show a notable effect on the price models and thus over potential for further research within this paradigm.

To the best of the authors’ knowledge, this is the first approach that combines and collates the subjective ratings of visual features and deep learning for real estate use cases.

This paper aims to investigate actor roles and public–private interactions in networks. Role dynamics are explored in two settings: the current development network and the future implementation network to which actors are transitioning.

The paper builds on the industrial marketing and purchasing approach to business markets and uses a qualitative methodology. A case study of a network developing geofencing applications in the context of sustainable transport was used. The main source of data was interviews with 26 respondents from public and private organizations.

Roles in development and implementation of geofencing are identified, where private and public actors may take on one or several roles in the developing setting. When transitioning to the implementation setting, the expectations of public actors vary and there is ambiguity over their roles, which range from active to inactive. This detailed empirical case study shows the complexity of multi-actor involvement when developing digital technology for the transport system.

The study highlights the transition from firm-centric innovation to network-centric innovation and its implications on actor roles.

Organizations participating in public–private innovation networks need to be aware of the multiple roles public organizations play and the complexities they face.

The paper explores role dynamics within and between the development and implementation settings of geofencing. Within the current development setting, roles are identified at different organizational levels with limited change in role dynamics. When transitioning to a new setting, actors’ role dynamics may range from “limited” to “path-breaking.” In future settings, actors enter and exit networks and their roles may change dramatically.