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This study demonstrates how artificial intelligence (AI) shapes the strategic planning process in volatile, uncertain, complex and ambiguous (VUCA) business environments. Having adopted various domains of the Cynefin framework, the research explores AI's transformative potential and provide insights regarding how organisations can harness AI-driven solutions for strategic planning.

This conceptual paper theorises the role of AI in strategic planning process in a VUCA world by integrating extant knowledge across multiple literature streams. The “model paper” approach was adopted to provide a theoretical framework predicting relationships among considered concepts.

The paper highlights potential application of the Cynefin framework to manage complexities in strategic decision-making process, the transformative impact of AI at different stages of strategic planning, the required strategic planning characteristics within VUCA to be supported by AI and the attendant challenges posed by AI integration in the uncertain business landscape.

This study pioneers a theoretical exploration of AI's role in strategic planning within the VUCA business landscape, guided by the Cynefin framework. Thus, it enriches scholarly discourse and expands knowledge frontiers.

The purpose of this paper is to understand the implicit motivations of entrepreneurial philanthropists during different stages of their lives.

The authors follow a Historical Organization Studies approach by performing a psychobiographical analysis using McClelland’s Thematic Apperception Test on the autobiography of Andrew Carnegie across different stages of his life while considering the historical context.

The configuration of the implicit motivations of entrepreneurial philanthropists change with achievement motivation decreasing and power motivation increasing over time explaining the shift of focus from self to others.

While researchers have been theorizing and using interviews to uncover shifts in entrepreneurial motivations, this is the first paper to longitudinally uncover implicit motivations to explain why successful entrepreneurs give back later in life.

The development of educational models that prioritize the well-being and sustainability of educational communities has become increasingly important in recent years. The purpose of this study was to explore the impact of teachers’ training in strategies for implementing a multidimensional approach to classroom well-being through the Teacher Centred Coaching Model on well-being in the classroom – teachers and students well-being.

Through a qualitative design, the authors conduct the present study to assess the perceptions of elementary teachers regarding their own competency development and that of their students during a 25-h intervention program. The study involved teachers (n = 8) and their 8–15-year-old students (n = 423). The “Teachers Perception of SWCI Impact on students” questionnaire was used to collect student data, while teacher competency data was obtained through individual interviews entitled “Strategies for Well-being in the Classroom Intervention”.

The analysis of the data showed that teachers believed that improving well-being strategies could benefit the personal development of both themselves and their students. Furthermore, the study revealed a positive impact on the personal and professional well-being of the teachers who participated in the program. After completing the program, students displayed improved abilities in attention and concentration, as well as better personal relationships, organizational skills and emotional regulation. The teachers emphasized that self-awareness and self-regulated learning had a positive influence on their own well-being, as well as on that of the educational community, by promoting empathetic communication, sharing experiences and implementing good practices aimed at enhancing well-being and sustainability.

The study involved eight participants, comprising eight females and two males. This number could be seen as a limitation, but in another way, these participants (from all the several regions in Portugal) allow us to reach a level of theoretical saturation of the contents in analysis because they complete all the several moments of training, coaching and implementation of the proposal, and their practices, reflections and actions guarantee the depth of data, as Burmeister and Aitken (2012) and Fusch and Ness (2015) propose.

The teachers emphasized that self-awareness and self-regulated learning had a positive influence on their own well-being, as well as on that of the educational community, by promoting empathetic communication, sharing experiences and implementing good practices aimed at enhancing well-being and sustainability.

After completing the program, students displayed improved abilities in attention and concentration, as well as better personal relationships, organizational skills and emotional regulation.

This study adds to both theoretical and practical development of continuing teacher training about social and emotional learning and executive functioning and its possible role in promote the personal development of teachers and students.

Self-leadership theory predicts that specific self-leadership practices will enable individuals to better accomplish their goals. However, little is known about the role that these practices play in conventional leadership (leading or influencing others). This study compares leaders to non-leaders (N = 318) in nonprofits and examines both the extent to which self-leadership practices are employed and the strength of beliefs concerning their importance.

Online survey of two groups: leaders and non-leaders of nonprofit organizations.

Leaders practiced self-goal setting (d = 0.47) and self-observation (d = 0.45) more than non-leaders. Non-leaders practiced more self-reward (d = 0.33) and self-punishment (d = 0.37) than leaders. The only differences in belief concerning the importance of the self-leadership practices were due to leaders believing self-goal setting (d = 0.46) and self-observation (d = 0.36) were more important than non-leaders did.

If self-leadership practices contribute to leadership effectiveness or emergence, this study indicates that goal setting and self-observation (monitoring progress toward goals) may contribute positively to the leadership of others, whereas self-reward and self-punishment may contribute negatively.

Self-leadership may not be as important to the leadership of others as is often claimed or implied.

This is the first study to look at how self-leadership practices differ between leaders and non-leaders.

Organizations are increasingly deploying algorithmic human resource management (HRM) for decision-making. Despite algorithms beginning to permeate HRM practices, our understanding of how to interpret and leverage the functions of algorithmic HRM remains limited. This study aims to review the stock of knowledge in this field of algorithmic HRM and introduce a theoretical perspective of functional affordance to enhance the understanding of the value of algorithmic HRM.

A systematic literature review was conducted in this study based on 283 articles. The articles are extracted from the Web of Science and Scopus. The content of the articles was then integrated to formulate the framework for this study.

Functional affordance highlights algorithmic HRM can be systematically embedded within the organizational environment, with its characteristics naturally suggesting the functionalities or actions available for HR managers to choose from. The findings of this study demonstrate five features of algorithmic HRM from the perspective of functional affordance: awareness of algorithmic HRM, alignment with business model design, action readiness, adaptation to business context and attribution to individuality.

This study provides a novel perspective for understanding the insufficiently theorized application of algorithmic HRM within organizations. It presents an integrated framework that elucidates the key features of algorithmic HRM and elaborates on how organizations can better develop algorithm-driven capabilities based on functional affordance.

This paper aims to examine the direct and indirect impact of competitive intelligence (CI) practices on sustainable competitiveness (SC) and firm performance (FP) mediated by strategic design collaboration (SDC).

This empirical study is based on a survey of 179 respondents comprising senior managers from pharmaceutical companies operating in Egypt across three sectors: public, private and multinational corporations (MNCs).

The results show that CI has a positive and significant impact on SDC, which, in turn, positively impacts SC and FP. The study also shows that SDC mediates the relationship between CI and both SC and FP.

This study sheds light on the significant and mediating role of SDC in explaining the relationship between CI and both SC and FP.

Based on the sociotechnical systems theory, we examined the human–technology interactions in the context of future works conditioned by machine learning (ML) and artificial intelligence (AI). Skills needed to support career sustainability and the future of the workforce, particularly for the middle-skilled workforce in the contemporary United States America (USA) context, were also studied.

We conducted a scenario analysis to demonstrate the potential roles that human resource professionals may perform to fill the skill gaps given their expertise in the shaping and skilling processes.

Assessing the success of the integration of AI and ML into the middle-skilled workforce requires a multi-faceted approach that considers performance metrics, cost-effectiveness, job satisfaction, environmental impact and innovation. Employees with AI skills can be more competitive in the workforce and forward to high-skilled positions.

Empirical research and related studies focusing on evaluations of reskilling and upskilling processes and outcomes would support career sustainability and the future development of middle-skilled workers.

Through a proactive strategic career development plan with AI integration, middle-skilled workers may enhance their career sustainability and be prepared for future higher-skilled work.

The economic downturn caused by technology-induced unemployment may be addressed by unleashing middle-skilled workforce potentials for future work created by AI and robotics and sustaining economic competitiveness.

This article offers important implications for human resource development theory-minded researchers and scholarly practitioners.

The process performed during a financial statement audit is critical but is unobservable to external stakeholders. This can create challenges in assessing the quality of individual audit engagements. This study’s objective is to introduce and investigate an archival measure based on publicly available information that proxies for audit process ineffectiveness.

We proxy for audit process ineffectiveness using errors in the audit report. We examine audit reports to identify errors because the audit report represents the auditor’s primary communication with financial statement users and is subject to rigorous preparation and review. We first examine if typical factors influencing audit process ineffectiveness are associated with audit report errors. We then examine whether audit reports containing errors are associated with audit quality measures.

We find that errors are more likely to be present in audit reports when time pressure exists and less likely when auditors exert more effort and when audit engagement risk is higher. Results also show that errors in audit reports are positively associated with financial reporting misstatements, measured by subsequently disclosed Big R restatements and out-of-period adjustments.

Collectively, our evidence suggests that an audit report containing an error is a suitable proxy for audit process ineffectiveness. This proxy has audit quality implications because inattentiveness in one area of the audit process could indicate inattentiveness in another area.

The purpose of this study is to investigate the key characteristics of artificial intelligence (AI) in organizational settings, analyze its capacity to reduce customer service jobs in favor of more advanced roles and analyze its efficacy in candidate screening by emphasizing performance.

A comprehensive analysis of 40 papers is performed using the PRISMA method based on data from Web of Science, Scopus, Emerald and Google Scholar.

The findings show optimized human resource management operations such as recruiting and performance monitoring, resulting in increased precision in hiring and decreased employee turnover. Customer service automation redistributes human labor to more intricate positions that need analytical reasoning and empathetic skills.

The study has two key implications. First, AI can streamline customer service, freeing up human workers for more complex tasks. Second, AI may increase candidate screening accuracy and efficiency, improving recruiting outcomes and organizational performance.

The study adds to the current literature by shedding light on the intricate relationships between AI and organizational performance and providing insights into the processes underpinning trust-building in AI technology.

While the impact of artificial intelligence (AI) on organizational innovation is increasingly acknowledged, empirical investigations exploring its effect on product innovation efficiency, particularly through the lens of knowledge innovation at various modes, remain scarce. This study aims to address this gap by empirically examining the influence of AI application on product innovation efficiency within firms. It further investigates the mediating role of knowledge innovation within the SECI model and the moderating effect of technological perception characteristics.

Data were collected through a questionnaire survey administered to managers in intelligent manufacturing enterprises across China. The proposed hypotheses were tested using the PROCESS macro for SPSS, employing the bootstrapping method to assess mediation and moderation effects.

Enterprise application of AI significantly influences both knowledge innovation within the SECI model and product innovation efficiency. Knowledge innovation occurring in the socialization and combination modes of the SECI model mediates the relationship between AI application and product innovation efficiency. Perceived ease of use of AI exhibits a contrasting moderating effect on AI-related relationships, while perceived usefulness does not demonstrate a significant moderating effect.

This study contributes to the existing literature by empirically examining the influence of AI application on product innovation efficiency, mediated by knowledge innovation within the SECI model. Additionally, it explores the nuanced moderating role of AI perception characteristics, highlighting the potential duality of AI technology in driving innovation outcomes. These findings offer valuable insights for managers seeking to leverage AI for enhancing product innovation and knowledge management practices.