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Bibliometric analysis has recently become a popular and rigorous technique used for exploring and analyzing the literature in business and management. Prior studies principally focused on ‘how to do bibliometric analysis’, presenting an overview of the bibliometric methodology along with various techniques and step-by-step guidelines that can be relied on to rigorously conduct bibliometric analysis. However, the current body of evidence is limited in its ability to provide practical knowledge that can enhance the design and performance of bibliometric research. This claim is supported even by the fact that relevant studies refer to their work as ‘bibliometric analysis’ rather than ‘bibliometric research’. Accordingly, we endeavor to offer a more functional framework for researchers who wish to design/conduct bibliometric research on any field of research, especially business and management. To do this, we followed a twofold way. We first outlined the main stages and steps of typical bibliometric research. Then, we proposed a comprehensive framework for specifying how to design/conduct the research and under what headings the relevant stages (step-by-step) will be used and/or presented. Thus, the current paper is expected to be a useful source to gain insights into the available techniques and guide researchers in designing/conducting bibliometric research.

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.

Systematic literature reviews (SLRs) have become a standard tool in many fields of management research but are often considerably less stringently presented than other pieces of research. The resulting lack of replicability of the research and conclusions has spurred a vital debate on the SLR process, but related guidance is scattered across a number of core references and is overly centered on the design and conduct of the SLR, while failing to guide researchers in crafting and presenting their findings in an impactful way. This paper offers an integrative review of the widely applied and most recent SLR guidelines in the management domain. The paper adopts a well-established six-step SLR process and refines it by sub-dividing the steps into 14 distinct decisions: (1) from the research question, via (2) characteristics of the primary studies, (3) to retrieving a sample of relevant literature, which is then (4) selected and (5) synthesized so that, finally (6), the results can be reported. Guided by these steps and decisions, prior SLR guidelines are critically reviewed, gaps are identified, and a synthesis is offered. This synthesis elaborates mainly on the gaps while pointing the reader toward the available guidelines. The paper thereby avoids reproducing existing guidance but critically enriches it. The 6 steps and 14 decisions provide methodological, theoretical, and practical guidelines along the SLR process, exemplifying them via best-practice examples and revealing their temporal sequence and main interrelations. The paper guides researchers in the process of designing, executing, and publishing a theory-based and impact-oriented SLR.

In highly volatile and uncertain times, organizations need to develop a resilience capacity which enables them to cope effectively with unexpected events, bounce back from crises, and even foster future success. Although academic interest in organizational resilience has steadily grown in recent years, there is little consensus about what resilience actually means and how it is composed. More knowledge is particularly needed about organizational capabilities that constitute resilience, as well as conditions for their development. This paper aims to make a contribution to this heterogeneous research field by deepening the understanding of the complex and embedded construct of organizational resilience. We conceptualize resilience as a meta-capability and decompose the construct into its individual parts. Inspired by process-based studies, we suggest three successive resilience stages (anticipation, coping, and adaptation) and give an overview of underlying capabilities that together form organizational resilience. Based on this outline, we discuss relationships and interactions of the different resilience stages as well as main antecedents and drivers. We formulate propositions that can act as a foundation for future empirical work.

We investigate whether environmental, social, and governance (ESG) ratings predict future ESG news and the associated market reactions. We find that the consensus rating predicts future news, but its predictive ability diminishes for firms with large disagreement between raters. The relation between news and market reaction is moderated by the consensus rating. In the presence of high disagreement between raters, the relation between news and market reactions weakens, while the rating with the most predictive power predicts future stock returns. Overall, while rating disagreement hinders the incorporation of value-relevant ESG news into prices, ratings predict future news and proxy for market expectations of future news.

The introduction of ChatGPT in November 2022 by OpenAI has stimulated substantial discourse on the implementation of artificial intelligence (AI) in various domains such as academia, business, and society at large. Although AI has been utilized in numerous areas for several years, the emergence of generative AI (GAI) applications such as ChatGPT, Jasper, or DALL-E are considered a breakthrough for the acceleration of AI technology due to their ease of use, intuitive interface, and performance. With GAI, it is possible to create a variety of content such as texts, images, audio, code, and even videos. This creates a variety of implications for businesses requiring a deeper examination, including an influence on business model innovation (BMI). Therefore, this study provides a BMI perspective on GAI with two primary contributions: (1) The development of six comprehensive propositions outlining the impact of GAI on businesses, and (2) the discussion of three industry examples, specifically software engineering, healthcare, and financial services. This study employs a qualitative content analysis using a scoping review methodology, drawing from a wide-ranging sample of 513 data points. These include academic publications, company reports, and public information such as press releases, news articles, interviews, and podcasts. The study thus contributes to the growing academic discourse in management research concerning AI's potential impact and offers practical insights into how to utilize this technology to develop new or improve existing business models.

Necessary condition analysis (NCA) is an increasingly used or suggested method in many business and management disciplines including, for example, entrepreneurship, human resource management, international business, marketing, operations, public and nonprofit management, strategic management, and tourism. In the light of this development, our work delivers a review of the topics analyzed with NCA or in which NCA is proposed as a method. The review highlights the tremendous possibilities of using NCA, which hopefully encourages other researchers to try the method. To support researchers in future NCA studies, this article also provides detailed guidelines about how to best use NCA. These cover eight topics: theoretical justification, meaningful data, scatter plot, ceiling line, effect size, statistical test, bottleneck analysis, and further descriptions of NCA.

This study assesses whether the accrual-generating process is adequately described by a linear model with respect to a range of underlying determinants examined by prior literature. We document substantial departures from linearity across the distributions of accrual determinants, including measures of size, performance, and growth. To incorporate non-linear relations, we employ a recently developed multivariate matching approach (entropy balancing) to adjust for determinants in place of relying on a linear model. Entropy balancing identifies weights for the control sample to equalize the distribution of determinants across treatment and control samples. In simulations drawing random samples from deciles where a linear model displays poor fit, we find that entropy balancing significantly improves accrual model specification by reducing coefficient bias relative to linear and propensity-score matched models. Consistent with entropy balancing retaining sufficient power, we find that its estimates detect seeded accrual manipulations and explain variation in accruals around equity issuances.

This study collates potential economic effects of mandated disclosure and reporting standards for corporate social responsibility (CSR) and sustainability topics. We first outline key features of CSR reporting. Next, we draw on relevant academic literatures in accounting, finance, economics, and management to discuss and evaluate the potential economic consequences of a requirement for CSR and sustainability reporting for U.S. firms, including effects in capital markets, on stakeholders other than investors, and on firm behavior. We also discuss issues related to the implementation and enforcement of CSR and sustainability reporting standards as well as two approaches to sustainability reporting that differ in their overarching goals and materiality standards. Our analysis yields a number of insights that are relevant for the current debate on mandatory CSR and sustainability reporting. It also points scholars to avenues for future research.

This paper studies a model in which investors’ information acquisition and the manager’s investment choice (hence the moments of the firm’s cash flow) are jointly determined. I show that a lower information acquisition cost alters the information environment in a way that motivates the manager to prioritize reducing the variance of cash flow over improving its mean. I present conditions under which a decrease in the cost of information acquisition reduces stock valuations and investors’ welfare. The analysis highlights the importance of considering the joint determination of firm risk in studying investors’ information acquisition. The model’s predictions are relevant to the growing literature that studies technological advancements and regulatory requirements that lower the cost for investors to acquire and process information.