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This study aims to investigate the implications of applying critical realism to the study of organizational learning. It considers critical realism as an alternate theoretical science foundation to the domains of empirical realism and social constructivism that characterize most of the field of organizational learning.

This study adopts the approach of a philosophical/conceptual discussion.

This study finds that the critical realist approach makes it possible for organizational learning researchers to apply a prescriptive change agenda. It requires researchers to study the context in which organizational learning occurs. These two features enable the researcher to propose what the world must be like for organizational learning to occur. Hence, a critical realist foundation moves organizational learning theory a step closer to its theoretical sibling, the learning organization.

This study reveals the potential in applying critical realism to the study of organizational learning and identifies its related strengths.

The purpose of this study is to analyse how academic staff cope with the new culture of performance measurement and assessment in universities. In particular, the study aims to shed light on how external pressures related to measurement of research performance are translated into organisational and individual academic responses within the university and the extent to which these responses are related specifically to the operational features of performance measurement systems (PMS).

The study is based on a case study conducted in an Italian public university and based on interviews with a cross-disciplinary sample of faculty members.

The study provides insights into how linking financial incentives and career progression to research performance metrics at the system and organisational levels may have important reorientation effects on individual behaviours and epistemic consequences for the academic work.

The study is based on interviews, so one limitation is related to the risk of researcher and interviewee personal bias. Moreover, this study is focused on one single case of a specific university setting, which cannot be fully representative of the experiences of others.

The study contributes to the literature on management accounting by exploring the factors that might explain why the unintended effects of PMS on academics’ behaviour reported by several studies might occur. From a practitioner’s point of view, it shows features of PMS that may produce unintended effects on academic activities. It also highlights the need to rethink PMS for the evaluation of university performance through the involvement of different stakeholders.

The purpose of the paper is to explore the meaning of Human Resources (HR) analytics and introduce the HR analytics cycle as a proactive and systematic process for ethically gathering, analyzing, communicating and using evidence-based HR research and analytical insights to help organizations achieve their strategic objectives.

Conceptual review of the current state and meaning of HR analytics. Using the HR analytics cycle as a framework, the authors describe a seven-step process for building evidence-based and ethical HR analytics capabilities.

HR analytics is a nascent discipline and there are a multitude of monikers and competing definitions. With few exceptions, these definitions lack emphasis on evidence-based practice (i.e. the use of scientific research findings in adopting HR practices), ethical practice (i.e. ethically gathering and using HR data and insights) and the role of broader HR research and experimentation. More importantly, there are no practical models or frameworks available to help guide HR leaders and practitioners in doing HR analytics work.

The HR analytics cycle encompasses a broader range of HR analytics practices and data sources including HR research and experimentation in the context of social, behavioral and organizational science.

This paper introduces the HR analytics cycle as a practical seven-step approach for making HR analytics work in organizations.

Considering personality as changeable through a bottom-up process of altering states, habits and traits, constitutes a shift in the predominant paradigm within personality psychology. The purpose of this paper is to reconsider Bateson’s theory of learning and organizational triple-loop learning in light of this recent empirical evidence.

This paper uses a multi-disciplinary conceptual approach. Based on an integrative analysis of literature from recent work in personality psychology, four dimensions (process, content, time and context) are identified that allow linking personality change and triple-loop learning.

Identifying a bottom-up process of changing states, habits and traits as being central to change personality, allows for reconsidering Bateson’s theory of learning as a theory of personality development (Learning II) and personality change (Learning III). Functionally equivalent, organizational triple-loop learning is conceptualized as a change in an organization’s identity over time that may be facilitated through a change in responding to events and a change in the organization’s routines.

Interventions that change how organizations respond to events and that change the routines within an organization may be suitable to facilitate triple-loop learning in terms of changing organizational identity over time.

This paper contributes to the discussion on Bateson’s theory of learning and organizational triple-loop learning. As interest in personality change grows in organization studies, this paper aims to transfer these findings to organizational learning.

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.

This study aims to better inform environmental management at universities by applying and validating the policy integration processes theory through a case study of Manchester Metropolitan University.

Social network analyses were used to identify, differentiate and categorise working networks of individuals and departments and the interconnections between them.

In an organisation, networks can be developed and active at departmental level but not at individual level. High numbers of departments can be doing work related to sustainable development whilst having low and medium levels of interconnections between departments. Influence of stakeholders throughout the network suggests levels of sustainable development policy integration at individual and departmental.

New insights provide evidence for universities’ environmental managers of the need of developing and implementing strategies that involve individuals’ work between departments by providing incentives, supporting capacity building and staff empowerment.

This paper applied and validated the theory of policy integration processes, showing that work at individual level and between departments needs more attention.

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.

Factor analysis is the most used tool in organizational research and its widespread use in scale validations contribute to decision-making in management. However, standard factor analysis is not always applied correctly mainly due to the misuse of ordinal data as interval data and the inadequacy of the former for classical factor analysis. The purpose of this paper is to present and apply the Bayesian factor analysis for mixed data (BFAMD) in the context of empirical using the Bayesian paradigm for the construction of scales.

Ignoring the categorical nature of some variables often used in management studies, as the popular Likert scale, may result in a model with false accuracy and possibly biased estimates. To address this issue, Quinn (2004) proposed a Bayesian factor analysis model for mixed data, which is capable of modeling ordinal (qualitative measure) and continuous data (quantitative measure) jointly and allows the inclusion of qualitative information through prior distributions for the parameters’ model. This model, adopted here, presents considering advantages and allows the estimation of the posterior distribution for the latent variables estimated, making the process of inference easier.

The results show that BFAMD is an effective approach for scale validation in management studies making both exploratory and confirmatory analyses possible for the estimated factors and also allowing the analysts to insert a priori information regardless of the sample size, either by using the credible intervals for Factor Loadings or by conducting specific hypotheses tests. The flexibility of the Bayesian approach presented is counterbalanced by the fact that the main estimates used in factor analysis as uniqueness and communalities commonly lose their usual interpretation due to the choice of using prior distributions.

Considering that the development of scales through factor analysis aims to contribute to appropriate decision-making in management and the increasing misuse of ordinal scales as interval in organizational studies, this proposal seems to be effective for mixed data analyses. The findings found here are not intended to be conclusive or limiting but offer a useful starting point from which further theoretical and empirical research of Bayesian factor analysis can be built.

A topic currently receiving significant academic and practitioner attention is called evidence-based management. The purpose of this paper is to suggest that this approach is sometimes over-sold and may be a fad. Additionally, evidence-based management fails to fully recognize the importance of tacit knowledge, what Kahneman calls system 1. Evidence-based management does provide tools to better use what Kahneman calls system 2, rationality. Decision-makers need to take advantage of both rational and beyond rational processes.

This is an essay, it is not a report of a study. At this point in time, this paper needs thinking, reflection, pondering, more than a data-based study.

Advocates promote evidence-based management in part to help avoid fads, yet evidence-based management itself has many of the characteristics of a fad. Evidence-based management is based on an objective rational view of the world and suggests highly rational methods of decision-making. However, a rational fact-based might not give sufficient credit to instinct and feelings. Decision-makers should take into account facts, evidence, when making decisions, but not ignore intuition, hunches and feelings. This study is learning that decisions use a galaxy of approaches, with both cognitive and affective flexibility.

As with any opinion-based paper, this lacks empirical support. Proponents ask us to believe in evidence-based management. Neither we, the authors of this paper, nor the proponents of evidence-based management can empirically support the ideas offered. An additional limitation is that the paper is written in one language, English. Translation into another language might yield different meanings.

There are advantages for scholars and practitioners to look at the best available evidence. There can be disadvantages in overlooking non-quantifiable factors.

Those who use evidence-based management should also take into account feelings, ethics, aesthetics, creativity, for the betterment of society. To solve wicked problems one needs more than facts and rational analysis.

The overwhelming majority of those writing about evidence-based management are supporters. This study offers a different view. This paper brings new ideas and new thinking to both the extensive fad literature and the huge evidence-based management literature. Evidence-based management is discussed widely. Google Scholar lists more than two million papers in 2019, 2020 and 2021 on evidence-based management. Readers of this journal should critically evaluate this popular set of ideas.

Interruptions are prevalent in knowledge work, and their negative consequences have driven research to find ways for interruption management. However, these means almost always leave the responsibility and burden of interruptions with individual knowledge workers. System-level approaches for interruption management, on the other hand, have the potential to reduce the burden on employees. This paper’s objective is to pave way for system-level interruption management by showing that data about factual characteristics of work can be used to identify interrupting situations.

The authors provide a demonstration of using trace data from information and communications technology (ICT)-systems and machine learning to identify interrupting situations. They conduct a “simulation” of automated data collection by asking employees of two companies to provide information concerning situations and interruptions through weekly reports. They obtain information regarding four organizational elements: task, people, technology and structure, and employ classification trees to show that this data can be used to identify situations across which the level of interruptions differs.

The authors show that it is possible to identifying interrupting situations from trace data. During the eight-week observation period in Company A they identified seven and in Company B four different situations each having a different probability of occurrence of interruptions.

The authors extend employee-level interruption management to the system-level by using “task” as a bridging concept. Task is a core concept in both traditional interruption research and Leavitt's 1965 socio-technical model which allows us to connect other organizational elements (people, structure and technology) to interruptions.