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This study aims to explain how negative workplace interactions are formed by the application of a performance management system (PMS).

The study draws from unique in-depth interviews with service workers who resigned from an accounting shared service centre (SSC), discussing the reasons behind the resignations. Following an abductive approach, organisational justice theory is used to analyse the service workers' perceptions of negative interactions and to link the negative interactions to the use of the PMS.

The findings suggest that negative workplace interactions are characterised by cost consciousness, inequality and competitiveness. These interactions are attributed to the use of a PMS in the centre and are related to perceptions of distributive, procedural and interactional injustice.

Managers and leaders of shared service–type organisations should not rely on PMSs as an all-encompassing solution; instead, they should acknowledge the fairness of the use of PMSs. Moreover, HR professionals should choose and train employees to apply PMSs fairly. Fairness is important in work allocation, resourcing, monitoring, giving feedback, recognising good performance, promotion and interaction between peers.

This study contributes to the literature by taking an overall perspective on PMSs to analyse and explain the unintended negative consequences of a PMS in a highly scripted and monitored work environment that is usually considered appropriate for such a system's use. Through the analysis, the study highlights pitfalls in the use of a PMS and the importance of interactional injustice not only between but also within organisational levels.

Due to the practical complexity and fragmented nature of the construction industry, digitalisation, like other innovations, is not easily achieved. This study aims to explore organisational influences on digitalisation within construction firms.

The study uses structured open-ended questions as a data collection tool for a qualitative investigation. The qualitative approach enabled participants to express their inputs and maximise the diversity of data, offering new insights and discussions that are distinct from previous works.

Construction professionals from 22 organisations provided their perspectives on digital transformation and their organisations. Under four constructs – structure, culture, leadership and internal processes, findings uncovered 16 determinants critical to digitalisation in construction firms. The study offers a theoretical perspective supported by empirical data to explore the complex dynamics and internal interactions of organisational influence on the uptake of digitalisation in the construction industry.

This paper offers arguments from a theoretical lens by applying the organisational influence model and capturing the variables under each construct in an exploratory manner to highlight the reasoning behind the low digital uptake in construction firms. This research aids academia and practice on the pressure points responsible for enhancing, or undermining, digital uptake in construction firms at an organisational level.

This article argues the value of integrating pragmatism in applying behavioural science to complex challenges. We describe a behaviour change-led knowledge co-production process in the specific context of climate change in Australia. This process was led by an interdisciplinary research team who struggled with the limitations of the prevailing deterministic behaviour change paradigms, such as the “test, learn, adapt” model, which often focuses narrowly on individual behaviours and fails to integrate multiple interpretations from diverse stakeholders into their knowledge co-production process.

This article uses collaborative reflection as a method of inquiry. We document the team’s experience of a recent challenge-led, programatic research initiative that applied behaviour change strategies to reduce climate vulnerabilities. We demonstrate the necessity of critical reflection and abductive reasoning in the face of the complexities inherent in knowledge co-production addressing complex problems. It underscores the importance of accommodating diverse perspectives and contextual nuances over a one-size-fits-all method.

The article shares lessons learnt about integrating collaborative and critical reflection throughout a project cycle and demonstrates the capacity of abductive reasoning to ease the challenges arising from the tension between behaviour change paradigms and knowledge co-production principles. This approach allows for a more adaptable and context-sensitive application, acknowledging the multiplicity of understandings and the dynamic nature of behavioural change in relation to climate adaptation.

This reflection contributes original insights into the fusion of pragmatism with behaviour change strategies, proposing a novel framework that prioritises flexibility, context-specificity and the recognition of various stakeholder perspectives in the co-production of knowledge.

There is a growing need for methodological plurality in advancing operations management (OM), especially with the emergence of machine learning (ML) techniques for analysing extensive textual data. To bridge this knowledge gap, this paper introduces a new methodology that combines ML techniques with traditional qualitative approaches, aiming to reconstruct knowledge from existing publications.

In this pragmatist-rooted abductive method where human-machine interactions analyse big data, the authors employ topic modelling (TM), an ML technique, to enable constructivist grounded theory (CGT). A four-step coding process (Raw coding, expert coding, focused coding and theory building) is deployed to strive for procedural and interpretive rigour. To demonstrate the approach, the authors collected data from an open-source professional project management (PM) website and illustrated their research design and data analysis leading to theory development.

The results show that TM significantly improves the ability of researchers to systematically investigate and interpret codes generated from large textual data, thus contributing to theory building.

This paper presents a novel approach that integrates an ML-based technique with human hermeneutic methods for empirical studies in OM. Using grounded theory, this method reconstructs latent knowledge from massive textual data and uncovers management phenomena hidden from published data, offering a new way for academics to develop potential theories for business and management studies.

Critical realism is an increasingly popular “lens” through which complex events, entities and phenomena can be studied. Yet detailed operationalisations of critical realism are at present relatively scarce. This study's objective here is built on existing debates by developing an open systems model of reality, a basis for designing appropriate, internally consistent methodologies.

The authors use a qualitative case study examining changing practices for client contact management in professional services firms during restrictions imposed by the COVID-19 crisis to show how the model can be operationalised across all stages of a research study.

This study contributes to the literature on qualitative applications of critical realism by providing a detailed example of how the research paradigm influenced choices at every stage of the case study process.

More importantly, this model of reality as an open system provides a tool for other researchers to use in their own operationalisation of critical realism in a variety of different settings.

This study aims to analyze the factors that drive or prevent interorganizational data sharing in the context of digital transformation (DT). Data sharing appears as a precondition for companies to capture emerging opportunities in supply chain management and for product-related servitization; however, there are ongoing concerns, and data are often perceived as the “new oil.” It is thus important to gain a better understanding of the determinants of firms’ decisions.

The authors develop an embedded case study analysis involving 16 firms within an extended supply network in the automotive industry. The authors focus on the peculiarities of the new context, as opposed to elements highlighted by research prior to the advent of the latest technologies. Abductive reasoning is applied to the theoretical foundations of the resource-based view, resource dependence theory and the complex adaptive systems perspective.

Data sharing is largely underpinned by factors identified prior to DT, such as data specificity, dependence dynamics and protection mechanisms and the dynamism of the business context. DT, however, can influence the extent of data sharing. New factors concern complementarities whenever data are pooled from different sources and digital platforms, as well as different forms of data ownership protection.

This study stresses that data sharing in the context of DT can be explained through established theoretical lenses, providing the integration of elements accounting for new technological opportunities.

Design science research (DSR) is a structured approach for solving complex ill-structured problems in organizations through the development of an artefact followed by its validation. This paper aims to evaluate existing DSR methodology and propose specific accents to promote DSR for environmental, social and governance (ESG)-oriented operational excellence (OPEX) initiatives within organizations.

This commentary paper is based on an abductive reasoning approach to evaluate and understand DSR and assess its effectiveness for developing solutions to typical ESG-oriented OPEX-based problems within organizations.

Existing literature on DSR is reviewed, after which it is evaluated on its ability to contribute to the implementation of sustainable solutions for ESG-oriented OPEX-based problems. Based on the review, specific DSR methodological accents are proposed for the development of ESG-oriented OPEX-based solutions in organizations.

This conceptual paper contributes to the conceptual understanding of the applicability, limitations and contextual preconditions for applying DSR. This paper proposes an explicit and, in some ways, alternative view on DSR research for OPEX researchers to apply and further the body of knowledge on matters of sustainability (ESG) in operations management.

Currently, there is limited understanding and application of the DSR methodology for OPEX-based problem-solving initiatives, as appears in the scant literature on DSR applied for the implementation of OPEX based initiatives for ESG purposes. This paper aims to challenge and provide accents for DSR applied to OPEX-related problems by means of a DSR framework and thereby promotes intervention-based studies among researchers.

The proposed step-by-step methodology contains novel elements and is expected to be of help for OPEX-oriented academicians and practitioners in implementing DSR methodology for practical related problems which need research interventions from academics from Higher Education Institutions.

This paper aims to outline the role that serendipity can play in providing a complementary and previously unrepresented vector in deliberate and emergent strategies within organizations.

The paper is conceptual in nature and draws upon the serendipity pattern in sociological theory and serendipitous relations in developmental sciences to provide a framework for executives to consider when examining the process of strategy formation. Two case vignettes are used to illustrate the difference between luck and serendipity and the paper also traces key micro foundations of serendipity by returning to the original serendipity fable and a famed science experiment producing “floppy-eared” rabbits.

The notion of chance favoring the “prepared firm” is espoused where the prepared organizational mind is positioned as an antecedent of serendipitous strategy formation. This is based on Louis Pasteur’s famous aphorism, “chance favors the prepared mind.” Components of the prepared firm include deep domain knowledge, anticipatory mindset, noticing, abductive reasoning, elaboration and relations development.

The paper is a conceptual articulation of a novel concept that now requires deeper empirical case development and ultimately statistical validation. The paper suggests linkages between serendipity and theories of absorptive capacity and the attention-based view of the firm.

Several mindsets, capabilities and relations for architecting organizational serendipity are suggested for executives using a stylized framework.

From a strategy process perspective, the Mintzberg and Waters seminal article “Of strategies deliberate and emergent” is complemented by considering “floppy-eared” strategy characterized by unexpected, anomalous and strategic datum.

This historical example of the creation of the arms industry in the Connecticut River Valley in the 1800s provides new insights into the value of government venture capital (GVC) and government demand in creating a new industry. Since current theoretical explanations of the best uses of governmental venture capital are still under development, there is considerable need for further theory development to explain and predict the creation of an industry and especially those industries where failures in private capital supply necessitates governmental involvement in new firm creation. The purpose of this paper is to provide an in depth historical review of how the arms industry evolved spurred by GVC and government created demand.

This study uses abductive inference as the best way to build and test emerging theories and advancing theoretical explanations of the best uses of GVC and governmental demand to achieve socially required outcomes.

By observing this specific historical example in detail, the authors add to the understanding of value creation caused by governmental venture capital funding of existing theory. A major contribution of this paper is to advance theory based on detailed observation.

The relatively limited research literature and theory development on governmental venture capital funding and the critical success factors in startups are enriched by this abductive investigation of the creation of the historically important arms industry and its spillover into creating the specialized machine industry.

The applications of Artificial Intelligence (AI) in various areas of professional and knowledge work are growing. Emotions play an important role in how users incorporate a technology into their work practices. The current study draws on work in the areas of AI-powered technologies adaptation, emotions, and the future of work, to investigate how knowledge workers feel about adopting AI in their work.

We gathered 107,111 tweets about the new AI programmer, GitHub Copilot, launched by GitHub and analysed the data in three stages. First, after cleaning and filtering the data, we applied the topic modelling method to analyse 16,130 tweets posted by 10,301 software programmers to identify the emotions they expressed. Then, we analysed the outcome topics qualitatively to understand the stimulus characteristics driving those emotions. Finally, we analysed a sample of tweets to explore how emotional responses changed over time.

We found six categories of emotions among software programmers: challenge, achievement, loss, deterrence, scepticism, and apathy. In addition, we found these emotions were driven by four stimulus characteristics: AI development, AI functionality, identity work, and AI engagement. We also examined the change in emotions over time. The results indicate that negative emotions changed to more positive emotions once software programmers redirected their attention to the AI programmer's capabilities and functionalities, and related that to their identity work.

Overall, as organisations start adopting AI-powered technologies in their software development practices, our research offers practical guidance to managers by identifying factors that can change negative emotions to positive emotions.

Our study makes a timely contribution to the discussions on AI and the future of work through the lens of emotions. In contrast to nascent discussions on the role of AI in high-skilled jobs that show knowledge workers' general ambivalence towards AI, we find knowledge workers show more positive emotions over time and as they engage more with AI. In addition, this study unveils the role of professional identity in leading to more positive emotions towards AI, as knowledge workers view such technology as a means of expanding their identity rather than as a threat to it.