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This research sought to document the volume, development trend and geographical distribution of the research on teacher autonomy, identify high-impact journals, authors and documents and reveal the intellectual structure of the field.

This paper analyzed the articles published on the related subject in the Web of Science (WoS) and/or Scopus. Based on certain exclusion criteria, analyses were conducted on a total of 259 articles. The data were then subjected to descriptive analyses and bibliometric analyses.

The review found that the teacher autonomy knowledge base has grown dramatically since 2004. In the co-citation analysis, it was determined that four clusters focused on the themes of professionalism and professional development, leadership and self-efficacy, autonomy in language teaching and learning and self-determination theory. According to the co-word analysis in this review, the most co-occurring keywords were revealed to be “teacher autonomy,” “autonomy,” “teachers,” “teacher professionalism” and “professional development.”

Despite increasing numbers of systematic reviews focusing on educational administration and leadership, this paper represents the first bibliometric review conducted to reveal the development of research on teacher autonomy using both the WoS and Scopus databases. Teacher autonomy can be regarded as an emerging field of study backed up by a theoretical background. Although there are some distinct prominent scholars in the research area, autonomy research still needs more scholars to specialize in the field.

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.

By adopting the “hard” and “soft” project management (PM) approaches from the PM-literature, this paper aims to problematize the expected role of client organizations in driving innovation in the transport infrastructure sector.

Addressing a large public client in Sweden, a case study design was initially applied to provide in-depth insights and perspectives of client project managers’ views and experiences of managing projects expected to drive innovation. In this paper, the concepts of “hard” and “soft” are used to discuss empirical findings on challenges associated with adopting a PM-approach for driving innovation in projects. The empirical material consists of interview data, complemented with observations and archival data.

Findings reveal challenges associated with combining hard and soft approaches, frequently demonstrating difficulties in balancing short-term project expectations with the promotion of innovation. In line with the literature, project managers note that there is a need for soft approaches to promote development and drive innovation. Yet, findings reflect a situation in which operational success criteria predominate, whereas soft approaches are not sufficiently used to create the grounds required for fostering innovation.

Insights are provided into how PM-approaches may impact construction innovation in the infrastructure sector, demonstrating a need for further research on the challenges and implications of applying and combining hard and soft PM-approaches.

This paper aims to conceptualize and define a construct of brand pride and develop a measurement instrument for the construct.

A scale development procedure comprising a qualitative prestudy and four quantitative studies for item generation and content validity, scale refinement using exploratory factor analyses, scale confirmation using confirmatory factor analysis and scale validity using PLS-SEM is followed to develop the measure of brand pride.

The scale development procedure yields a two-dimensional measurement instrument for brand pride with affective and utilitarian dimensions. The proposed scale is reliable and has convergent, discriminant and nomological validity.

This study highlights the need to study brand pride by reviewing literature from both psychology and marketing. The definition and conceptualization of brand pride help uniquely identify and describe the phenomenon. By developing a scale to measure the construct of brand pride, this study will enable research into the phenomenon, its causes and effects and its significance for consumer–brand relationships.

This paper aims to find the effective 3D printing process parameters based on mechanical characteristics such as tensile strength and hardness of poly lactic acid (PLA)/carbon fibre composites (CF-PLA) by implementing intelligent frameworks.

The experiment trials are conducted based on design of experiments (DoE) using Taguchi L9 orthogonal array with three factors (speed, infill % and pattern type) and three levels. The factors have been optimized by solving the regression equation which is obtained from analysis of variance (ANOVA). The contour plots are generated by response surface methodology (RSM). The influencing parameters are found by using Box–Behnken design. The second order response surface model demonstrated the optimal combination of input parameters for higher tensile strength and hardness.

The influencing parameters are found by using Box–Behnken design. The second order response surface model demonstrated the optimal combination of input parameters for higher tensile strength and hardness. The results obtained from RSM are also confirmed by implementing the machine learning classifiers, such as logistic regression, ridge classifier, random forest, K nearest neighbour and support vector classifier (SVC). The results show that the SVC can predict the optimized process parameters with an accuracy of 95.65%.

3D printing parameters which are considered in this work such as pattern types for PLA/CF-PLA composites based on intelligent frameworks has not been attempted previously.

The purpose of this study is to provide evidence to support an emerging theory that interpersonal conflict is the primary cause of workplace stress among a self-selected sample of Irish construction managers.

Eighteen construction managers working in Ireland were recruited for this study. Using semi-structured interviews and interpretative phenomenological analysis as the research methodology, the causes of their workplace stress were investigated.

Participants reported that the principal cause of their workplace stress was high levels of interpersonal conflict between colleagues. The effects of this interpersonal conflict included avoidance behaviour, ill health, absences from the workplace and loss of productivity issues. Deadlines, penalty clauses, lack of appreciation, cliques, costs, communication, temporary contracts and delays were also reported stressors.

A limitation of the study is the small sample of 18 construction managers and the limited geographical area.

The social implications of this study could be to clearly identify that interpersonal conflict may be under reported in the construction industry, and there is a possibility that it is being misclassified as other workplace behaviours such as bullying, harassment and workplace violence. If this is so, this could aid future researchers in addressing this challenging workplace behaviour.

The current consensus in the literature is that the three main causes of workplace stress are bullying, harassment and violence. However, the role and importance of interpersonal conflict as reported in this study, with the exception of North America and China, is not reflected in the wider health and safety research literature. In addition, interpersonal conflict and its reluctance to be reported is largely absent from construction safety research. The findings of this study may be explained if the workplace stress research community is currently misclassifying interpersonal conflict as a manifestation of bullying, harassment or violence. If this is the case, interpersonal conflict needs further research. This is to establish if this cause of construction-related workplace stress needs to be reconsidered as a standalone phenomenon in the wider family of challenging workplace behaviours.