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This paper elucidates the role of anger in error management (EM) and organizational learning behaviors. The study explores how anger can catalyze learning, emphasizing its strategic implications.

A double-layered moderated-mediated model was developed and tested using data from 744 Chinese CEOs. The psychometric properties of the survey instrument were rigorously examined through structural equation modeling, and hypotheses were tested using Hayes's PROCESS macros.

The findings reveal that anger is a precursor for recognizing the value of significant errors, leading to a positive association with learning behavior among top management team members. Additionally, the study uncovers a triple interaction effect of anger, EM culture and supply chain disruptions on the value of learning from errors. Extensive experience and positive grieving strengthen the relationship between recognizing value from errors and learning behavior.

This study uniquely integrates affect-cognitive theory and organizational learning theory, examining anger in EM and learning. The authors provide empirical evidence that anger can drive error value recognition and learning. The authors incorporate a more fine-grained approach to leadership when including executive anger as a trigger to learning behavior. Factors like experience and positive grieving are explored, deepening the understanding of emotions in learning. The authors consider both negative and positive emotions to contribute to the complexity of organizational learning.

The emerging business partner role of management accountants (MAs) results in an increased requirement of MAs to make business decisions. Frame dependence cognitive biases regularly influence decisions made in conditions of uncertainty, as is the case in business decision-making. Consequently, this study aims to examine susceptibility of MAs to frame dependence bias.

A survey was conducted among an international sample of practising MAs. The proportion of MAs influenced by framing bias was analysed and compared to findings in other populations. Logistic regression was then used to determine whether MAs who exhibit a higher preference for evidence-based (as opposed to intuitive) decision-making are more susceptible to framing bias.

Despite a comparatively high preference for evidence-based decision-making, the prevalence of framing bias among MAs is comparable to that of other populations. A higher preference for evidence-based decision-making was found to only be associated with higher susceptibility to endowment effect bias.

To the best of the authors’ knowledge, this is the first study to comprehensively examine framing bias for MAs as a group of decision-makers. Additionally, this study’s sample consists of practising MAs, and not only students.

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.

The purpose of this paper is to structure existing research on knowledge management (KM) in small- and medium-sized enterprises (SMEs) to offer a comprehensive overview of research strands and topics in KM in SMEs to determine their evolution over time.

The paper, which is considered a follow-up literature review, is based on a systematic literature review that covers 180 scientific papers that were published since the review paper by Durst and Edvardsson in 2012 that covered 36 papers.

The findings of this review and those of the aforementioned review are brought together in the form of an overview that structures research on KM in SMEs based on themes that, in turn, allow the derivation of promising research directions and research questions aimed at structuring future research on KM in SMEs.

By combining the findings of this review with the findings from the review published in this journal in 2012, this paper offers, to the best of the authors’ knowledge, the most comprehensive literature review on KM in SMEs produced to date.

A stylized fact in finance literature is the belief in positive relationship between ex ante return and risk. Hence, a rational investor, by utility preference axiom can only consider committing fund in asset which promises commensurate higher return for higher risk. Questions have been asked as to whether this holds true across securities, sectors and markets. Empirical evidence appears less convincing, especially in developing markets. Accordingly, the author investigates the nature of reward for taking risk in the Nigerian Capital Market within the context of individual assets and markets.

The author employed ex post design to collect weekly stock prices of firms listed on the Premium Board of Nigerian Stock Exchange for period 2014–2022 to attempt to answer research questions. Data were analyzed using a unique M Vec TGarch-in-Mean model considered to be robust in handling many assets, and hence portfolio management.

The study found that idea of risk-expected return trade-off is perhaps more general than as depicted by traditional finance literature. The regression revealed that conditional variance and covariance risks reveal minimal or no differences in sign and sizes of coefficients. However, standard errors were also found to be large suggesting somewhat inconclusive evidence of existence of defined incentive structure for taking additional risk in the market.

In terms of choice of methodology and outcomes, this research adds substantial value to body of knowledge. The adapted multivariate model used in this paper is a rare approach especially for management of portfolios in developing markets. Remarkably, the research found empirical evidence that positive risk-expected return trade-off, as known in mainstream literature, is not supported especially using a typical developing country data.

Digitalization accelerates the need of tourism and hospitality ecosystems to reframe business models in line with a data-driven orientation that can foster value creation and innovation. Since the question of data-driven business models (DDBMs) in hospitality remains underexplored, this paper aims at (1) revealing the key dimensions of the data-driven redefinition of business models in smart hospitality ecosystems and (2) conceptualizing the key drivers underlying the emergence of innovation in these ecosystems.

The empirical research is based on semi-structured interviews collected from a sample of hospitality managers, employed in three different accommodation services, i.e. hotels, bed and breakfast (B&Bs) and guesthouses, to explore data-driven strategies and practices employed on site.

The findings allow to devise a conceptual framework that classifies the enabling dimensions of DDBMs in smart hospitality ecosystems. Here, the centrality of strategy conducive to the development of data-driven innovation is stressed.

The study thus developed a conceptual framework that will serve as a tool to examine the impact of digitalization in other service industries. This study will also be useful for small and medium-sized enterprises (SMEs) managers, who seek to understand the possibilities data-driven management strategies offer in view of stimulating innovation in the managers' companies.

The paper reinterprets value creation practices in business models through the lens of data-driven approaches. In this way, this paper offers a new (conceptual and empirical) perspective to investigate how the hospitality sector at large can use the massive amounts of data available to foster innovation in the sector.

This paper aims to clarify how organizations manage their participation in networks to share and jointly create knowledge but also risk unwanted knowledge spillovers at the same time. As formal governance, trust and observation are less applicable in informal networks, the authors need to understand how members address the need to protect knowledge by informal practices. The study aims to investigate how the application of knowledge protection practices affects knowledge sharing in networks. The insights are relevant for organizational and network management to control knowledge risks but harvest the benefits of network engagement.

The authors opted for an exploratory study based on 60 semi-structured interviews with members of 10 networks. In two rounds, network managers, representatives and members of the networks were interviewed. The second round of interviews was used to validate the intermediate findings. The data were complemented by documentary analysis, including network descriptions.

Through analyzing and building on the theory of psychological contracts, two informal practices of knowledge protection were found in networks of organizations: exclude crucial topics and share on selected topics and exclude details and share a selected level of detail. The authors explored how these two practices are enacted in networks of organizations with psychological contracts.

Counter to intuition that the protection of knowledge can be strengthened only at the expense of knowledge sharing and vice versa, networks benefitted from more focused and increased knowledge sharing while reducing the risk of losing competitive knowledge by performing these knowledge protection practices.

The purposes of this study were (1) to explore empirical challenges in dimensioning safety buffers and their implications and (2) to organise those challenges into a framework.

In a multiple-case study following an exploratory, qualitative and empirical approach, 20 semi-structured interviews were conducted in six cases. Representatives of all cases subsequently participated in an interactive workshop, after which a questionnaire was used to assess the impact and presence of each challenge. A cross-case analysis was performed to situate empirical findings within the literature.

Ten challenges were identified in four areas of dimensioning safety buffers: decision management, responsibilities, methods for dimensioning safety buffers and input data. All challenges had both direct and indirect negative implications for dimensioning safety buffers and were synthesised into a framework.

This study complements the literature on dimensioning safety buffers with qualitative insights into challenges in dimensioning safety buffers and implications in practice.

Practitioners can use the framework to understand and overcome challenges in dimensioning safety buffers and their negative implications.

This study responds to the scarcity of qualitative and empirical studies on dimensioning safety buffers and the absence of any overview of the challenges therein.

This paper aims to explore how the maieutic role of management accounting (MA) can be sustained in the context of MA digitalization.

The paper begins with practitioners’ descriptions of the context that makes the MA support of non-routine decisions maieutic. To understand how the maieutic characteristics can be sustained in future MA digitalization, the authors then analyze the discourses these practitioners have about artificial intelligence (AI) in providing MA support.

As a basis, the authors’ data show various maieutic characteristics within the use of MA answers in decision-making as well as within the MA process of generating such answers. The paper then identifies three MA digitalization discourses, namely, “computation,” “judgment” and human-AI “interaction” discourse, each with their unique agendas on how AI should be used.

The paper is based on the premises that AI and digitalization are often discussed without sufficient understanding about the context being digitalized. The authors’ data suggest that MA support in non-routine decision-making is fundamentally maieutic, and AI – as it currently stands – is not expected to change this by providing perfect answers. The authors provide novel insights about maieutic MA support and the current discourses on using AI in MA support, and how digitalization does not necessarily compromise maieutic MA support but instead has the potential to sustain or even enhance it.

While artificial intelligence (AI) has shown its promise in assisting human decision, there exist barriers to adopting AI for decision-making. This study aims to identify barriers in the adoption of AI for automated organisational decision-making. AI plays a key role, not only by automating routine tasks but also by moving into the realm of automating decisions traditionally made by knowledge or skilled workers. The study, therefore, selected respondents who experienced the adoption of AI for decision-making.

The study applied an interpretive paradigm and conducted exploratory research through qualitative interviews with 13 senior managers in South Africa from organisations involved in AI adoption to identify potential barriers to using AI in automated decision-making processes. A thematic analysis was conducted, and AI coding of transcripts was conducted and compared to the manual thematic coding of transcripts with insights into computer vs human-generated coding. A conceptual framework was created based on the findings.

Barriers to AI adoption in decision-making include human social dynamics, restrictive regulations, creative work environments, lack of trust and transparency, dynamic business environments, loss of power and control, as well as ethical considerations.

The study uniquely applied the adaptive structuration theory (AST) model to AI decision-making adoption, illustrated the dimensions relevant to AI implementations and made recommendations to overcome barriers to AI adoption. The AST offered a deeper understanding of the dynamic interaction between technological and social dimensions.