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Decision-making plays a vital role in business. Yet as a trait, it is often overlooked when identifying, assessing, onboarding and empowering leadership talent. This study, therefore, aims to focus on the dimensions of organisational decision-making and investigate whether it should be an integral factor in the hiring process. The survey also looks at the relationships between decision-making and executive leadership, talent strategy and employee satisfaction. Could good decision-making as a trait be a factor when it comes to retention, improving organisational thinking and thriving leadership?

A quantitative and qualitative survey was commissioned and conducted by FT Longitude, part of the Financial Times Group. This included a questionnaire sent out to 400 senior executives at C-suite, C-1 and C-2 levels. The respondents were from companies with more than 1,000 employees in 13 industries and from five countries across the Americas, Europe and Asia-Pacific. This was also accompanied by in-depth interviews with three experts from both the public and private sector.

Almost two-thirds (63%) of senior executives have resigned or considered resigning due to frustration with organisational decision-making, while 29% have considered quitting because they were frustrated with the way a company makes decisions. More than a third (34%) resigned for this reason. Yet, a quarter of senior executives say that their decision-making experience was not explicitly discussed before starting their job. Senior executives who were asked about decision-making in interviews were 1.4 times more likely to be satisfied with their jobs. The ability to make decisions should therefore play a central role in hiring senior executives.

Decision-making as a trait has been neglected when hiring executives. For the first time, this research shows how significant it is for leadership teams. If senior executives are to improve organisational decision-making, this trait needs codifying in HR processes. This has led Kingsley Gate to embed it in recruitment profiling.

The purpose of this viewpoint article is to serve as a discussion starting point regarding organizational leadership’s increasing reliance on AI – in particular, how the technology is used as a supplemental tool for supporting rational decision-making. Practical implications and directions for further research are presented.

With its inception in economics, the concept of rationality has a rich history across multiple research domains. Based on that literature, coupled with the recent advancements in AI, the paper asks: will AI afford organizational leadership the ability to move from making bounded rational decisions to making fully rational decisions? The paper only scratches the surface of such a large question; however, the goal is to start the discussion around the topic.

While bounded rationality supports efficient decision-making, a complete understanding of any given decision is typically limited, and as a result, neither accuracy nor effectiveness is guaranteed. As AI systems grow in speed and accuracy, they should provide positive support for organizational leaders to make fully rational decisions. AI’s ability to collect and organize data, analyze it, and offer decision alternatives may help close the gap between bounded and rational decision-making.

Although AI research is not new, the recent developments in natural language processing engines has rapidly brought about new possibilities for their use in rational decision-making in the business and organizational context. This is fertile ground for future research, particularly in the area of organizational decision-making.

The organizational decision-making perspective (ODM) has a legacy regarding its concern for budgeting as an essential organizational routine in decision-making. Budgeting has also become a direct concern to organizational institutional theory (OIT) because of its prominent role in institution building, where budgeting can build trust in inter-organizational relationships. This paper builds on these two perspectives to explore organizational budget processes' formation, disruption, and re-creation over time.

We conducted a comprehensive review and critical analysis of the ODM and OIT perspectives, focusing on a fundamental paradox between ODM's emphasis on stability through organizational routines and OIT's focus on organizational legitimacy through the decoupled expression of organizational values. We then expanded on these paradoxical concerns in the context of budgeting, formalizing them into specific research propositions for future studies.

Tensions around the stability, decay, and re-creation of budgets as organizational routines emerge as a pressing issue requiring further empirical investigation from the ODM perspective. A critical issue in the OIT perspective is the potential for organizational budgets to provide an opportunity to decouple from practice through routinized expressions of rationality and to facilitate loose coupling in practice. These findings offer a fresh perspective and open up new avenues for future research in this area.

This paper contributes to the accounting and organizational research literature by shedding light on how organizations respond to the potential decay of budget routines and the manifestation of organizational values in decoupling processes by further re-creating and elaborating budget processes.

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.

Prior research has revealed that a large share of managers is reluctant towards the use of artificial intelligence (AI) in decision-making. This aversion can be caused by several factors, including individual drivers. The purpose of this paper is to better understand the extent to which individual factors influence managers’ attitudes towards the use of AI and, based on these findings, to propose solutions for increasing AI adoption.

The paper builds on prior research, especially on the factors driving the adoption of AI in companies. In addition, data was collected by means of 16 expert interviews using a semi-structured interview guideline.

The study concludes on four groups of individual factors ranked according to their importance: demographics, familiarity, psychology and personality. Moreover, the findings emphasized the importance of communication and training, explainability and transparency and participation in the process to foster the adoption of AI in decision-making.

The paper identifies four ways to foster AI integration for organizational decision-making as areas for further empirical analysis by business researchers.

This paper offers four ways to foster AI adoption for organizational decision-making: explaining the benefits and training the more adverse categories, explaining how the algorithms work and being transparent about the shortcomings, striking a good balance between automated and human-made decisions, and involving users in the design process.

The study concludes on four groups of individual factors ranked according to their importance: demographics, familiarity, psychology and personality. Moreover, the findings emphasized the importance of communication and training, explainability and transparency and participation in the process to foster the adoption of AI in decision-making.

This study is one of few to conduct qualitative research into the individual factors driving usage intention among managers; hence, providing more in-depth insights about managers’ attitudes towards algorithmic decision-making. This research could serve as guidance for developers developing algorithms and for managers implementing and using algorithms in organizational decision-making.

The purpose of this paper is to study the crisis management process for project-based organizations (PBOs) by developing a comprehensive model and propositions.

This paper is based on a conceptual study. A literature review is considered a primary source for studying contemporary research, including 171 publications in total, which embody qualitative, quantitative, conceptual and theoretical studies. For data analysis, content analysis is used, which is comprised of descriptive and thematic analysis.

This study identifies five imperative elements of crisis management for PBOs which include (1) sense-making (information gathering and crisis interpretation), (2) decision-making (accurate and timely decision), (3) response (reactive response), (4) outcome (success/failure) and (5) learning. Based on these findings, this study proposes an integrative model of the interplay between sense-making, decision-making, response, outcome and learning. Furthermore, the findings lead to propositions for each of the elements. The paper contributes to the literature on dynamic capability theory.

This paper explores the crisis management process for PBOs. The proposed model deepens the understanding of the practices and processes of project-based crisis management.

The new disruption in the form of ChatGPT can be a valuable tool for organizations to enhance their knowledge management and decision-making capabilities. This article explores how ChatGPT can enhance organizations' KM capability for improved decision-making and identifies potential risks and opportunities.

Using existing literature and a small-scale case study, we develop a conceptual framework for implementing artificial intelligence on the internal organizational knowledge base of big data and its integration with a larger knowledge base of ChatGPT.

This viewpoint conceptualizes integrating knowledge management and ChatGPT for improved organizational decision-making. By facilitating efficient information retrieval, personalized learning, collaborative knowledge sharing, real-time decision support, and continuous improvement, ChatGPT can help organizations stay competitive and achieve business success.

This is one of the first studies on the integration of organizational knowledge management systems with ChatGPT. This research work proposes a conceptual model on integration of knowledge management with generative AI which can be further tested in actual work settings to check it's applicability and make further modifications.

The study provided insights to managers and executives who, in collaboration with IT professionals, can devise a mechanism for integrating existing knowledge management systems in organizations with ChatGPT.

This is one of the first studies exploring the linkage between ChatGPT and knowledge management for informed decision-making.

This systematic literature review aims to explore the adoption, global acceptance and implementation of human resources (HR) analytics (HRA) by reviewing literature on the subject. HRA adoption can assist HR professionals in managing complex procedures and making strategic human resource management (SHRM) decisions more effectively. The study also aims to identify the applications of analytics in various disciplines of management.

The review is conducted using a domain-based structured literature review (SLR), emphasizing the diffusion of innovative thinking and the adoption process of HRA among early adopters. The philosophical stances are analyzed with the combination of research onion model and PRISMA protocol. Secondary data are gathered from published journals, books, case studies, conference proceedings, web pages and media stories as the primary source of information.

The study finds that skilled professionals and management assistance can significantly impact adoption intentions, enabling professionals to deal with analytics. The examples and analytical models provided by early adopters allow managers to manage complex processes and make SHRM decisions.

The study suggests that the lack of use of quantitative techniques is a key limitation and should be considered in future studies. Despite the rise in the number of research papers on HRA, its application in the workplace remains limited.

This research can assist managers in implementing HRA and help resolve complex and inefficient processes, making SHRM decisions.

This study adds to the existing body of knowledge on how HRA can aid a company's efficacy and performance and can be considered one of the first to link adoption and HRA.

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.

Although businesses continue to take up artificial intelligence (AI), concerns remain that companies are not realising the full value of their investments. The study aims to provide insights into how AI creates business value by investigating the mediating role of Business Process Management (BPM) capabilities.

The integrative model of IT Business Value was contextualised, and structural equation modelling was applied to validate the proposed serial multiple mediation model using a sample of 448 organisations based in the EU.

The results validate the proposed serial multiple mediation model according to which AI adoption increases organisational performance through decision-making and business process performance. Process automation, organisational learning and process innovation are significant complementary partial mediators, thereby shedding light on how AI creates business value.

In pursuing a complex nomological framework, multiple perspectives on realising business value from AI investments were incorporated. Several moderators presenting complementary organisational resources (e.g. culture, digital maturity, BPM maturity) could be included to identify behaviour in more complex relationships. The ethical and moral issues surrounding AI and its use could also be examined.

The provided insights can help guide organisations towards the most promising AI activities of process automation with AI-enabled decision-making, organisational learning and process innovation to yield business value.

While previous research assumed a moderated relationship, this study extends the growing literature on AI business value by empirically investigating a comprehensive nomological network that links AI adoption to organisational performance in a BPM setting.