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The purpose of this research is to examine the relationship between employer and employees in a public power sector undertaking through the validation of CODE (compensation, organizational justice, dispute resolution and employee empowerment) and PLE (workforce productivity, employee loyalty and employee engagement) model.

A hypothesized research model was developed and validated by using structural equation modeling (AMOS 20). In total, 303 responses were accumulated by administering a structured questionnaire among the employees of a state-owned power sector.

The results revealed that a harmonious climate of employee relations is prevailing in the public power utility. Additionally, the findings suggest that the CODE and PLE model of employee relations are found to be positive and significant by investigating the impact of compensation, organizational justice, dispute resolution and employee empowerment as the predictors; and workforce productivity, employee loyalty and employee engagement as the critical outcomes of employee relations.

The study recommends some plausible insights for practitioners, decision-makers and policy formulators to develop strategies and policies for nurturing congenial employee relations and also to cultivate a facilitative work environment for generating contented and competent manpower.

Validation of CODE and PLE model of employee relations in the new perspective of power sector undertaking is an epoch-making and novel contribution that offers significant empirical evidence to the extant literature. Moreover, the exploration of employer–employee relations in this context is a unique and innovative effort toward existing research.

This study explores the applicability of Anderson's (1999) code of the street framework to an Asian context by examining the relationship between residents' perceptions of police ineffectiveness, nationality, and street codes in foreigner-concentrated areas in South Korea.

The authors used survey data from 1,865 residents of diverse nationalities living in 20 foreigner-concentrated areas in South Korea. Mixed-effects ordinary least squares (OLS) regression and moderation analysis were used to examine (1) the association between residents' perceptions of police ineffectiveness and their street codes and (2) how this association varies across nationality groups.

The study findings overall indicated that higher resident perceptions of police as ineffective tended to correspond with greater levels of street codes. However, the interaction analysis results found that this relationship was conditioned by nationality. Specifically, perceptions of police ineffectiveness were associated with greater levels of street codes among foreign residents, particularly Korean-Chinese residents, whereas this was not the case among Korean residents.

The relationship between perceptions of the police and street codes described by Anderson (1999) has primarily been examined in the context of the U.S. To the best of the authors' knowledge, this study represents the first attempt to explore the link between residents' perceptions of the police and street codes by different nationality groups within an Asian setting.

This study seeks to deepen the understanding of the political process underlying the establishment and evolution of corporate governance (CG) regulations in a developing country.

Drawing on regulatory space concept (Hancher and Moran, 1989) and Oliver's (1991) typology of strategic responses, the authors identify which actor participated in and benefitted from the establishment of a new transnational CG regulation in Pakistan. Data were collected through interviews and from the published secondary sources.

The findings highlighted regulations are being influenced and shaped up by the political process of negotiation, bargaining, manipulation and domination between powerful and resourceful actors in a given regulatory space. National regulators and regulatees can be indeed fervent opponents to the transnational regulations when it comes to protecting their well-rooted national interests.

This study contributes to the accounting literature by illustrating political processes through which internationally recognised CG practices are resisted, negotiated and implemented in the developing countries. The regulator must pay attention that the outcome of the regulatory change process is the result of carefully crafted and conscious strategies of actors in the regulatory space.

Previous research has identified associations between quality management and employee health. This study's purpose was to (1) examine those associations in a public healthcare organisation and (2) explore and describe the association between digitalisation and employee health.

An online questionnaire including indices to measure quality management values, employee health and digitalisation was answered by 118 managers in Swedish public healthcare. Correlation analysis was used to analyse the data. Based on the survey results, 12 qualitative, in-depth interviews were conducted with healthcare managers.

The findings show that employee health is associated with quality management and digitalisation. Categories were defined to describe the managers' views of the relationship between digitalisation and health.

Causality was not explicitly tested and cannot be assumed. However, the results strengthen the body of research showing that quality management is related to employee health, and associations between health and digitalisation were identified.

The findings and model should be helpful for healthcare managers in a digitalising environment who aim to preserve or enhance employee health whilst ensuring high service quality.

The results were used to create an integrated conceptual model depicting the association between quality, digitalisation and health. This association has not previously been studied.

This study aims to investigate the effects of organizational culture factors on the selection of software process development models and develops a conceptual model for selecting and adopting process development models with an organizational culture approach, using 12 criteria and their sub-criteria defined in Fey and Denison’s model (12 criteria).

The research hypotheses were investigated using statistical analysis, and then the criteria and sub-criteria were selected based on Fey and Denison’s model and the experts’ viewpoints. Afterward, the organizational culture of the selected company was measured using the data from 2016 and 2017, based on Fey and Denison’s questionnaire. Due to the correlation between the criteria, using the decision-making trial and evaluation technique, the correlation between sub-criteria were determined, and by analytical network process method and using Super-Decision software, the process development model was preferred to the 12 common models in information systems development.

Results indicated a significant and positive effect of organizational culture factors (except the core values factor) on the selection of development models. Also, by changing the value of organizational culture, the selected process development model changed either. Sensitivity analysis performed on the sub-criteria implied that by changing and improving some sub-criteria, the organization will be ready and willing to use the agile or risk-based models such as spiral and win-win models. Concerning units where the mentioned indicators were at moderate and low limits, models such as waterfall, V-shaped and incremental worked more appropriately.

While many studies were performed in comparing development models and investigating their strengths and weaknesses, and the impact of organizational culture on the success of information technology projects, literature indicated that the impact of organizational sub-culture prevailing in the selection of development process models has not been investigated. In this study, new factors and indicators were addressed affecting the selection of development models with a focus on organizational culture. Correlation among the factors and indicators was also investigated and, finally, a conceptual model was proposed for proper adoption of the models and methodologies of system development.

Executive isolation, also known as workplace loneliness, its factors and impact are major issues for organizational development, future of work for leadership and learning culture. The purpose of this study is to examine the Executive isolation phenomenon where relationships between power distance, organizational culture and executive isolation of Chief Executive Officers (CEOs) are analysed on how it is considered by their teams. The same is contextualized through the inputs received through interviews conducted with CEOs and employee surveys.

The qualitative in-depth interviews of five CEOs, and survey across 34 of the 50 employees, were undertaken over the course of two phases of this study. The investigation focused on identifying executive isolation of CEOs and perspectives of employees that can impact the leadership and learning progress of organizations based on work culture, power distance and decision-making; awareness and experience of executive isolation; workplace friendliness and rejection; and management development initiatives to minimize the impact of executive isolation. Qualitative data analysis was conducted using MAXQDA 2022 (Verbi Software, Berlin, Germany), which is a qualitative data analysis software.

The findings highlight and expose the significant gap between understanding and analysing of the factors due to which the CEOs undergo executive isolation. It also extends to providing details related to the lack of awareness of the teams’ actions contributing to the CEOs’ isolation. It further highlights the fact that the difference of perspectives between the CEOs and teams leads to the organization slowing in its learning activities due to the leaders’ own challenges of executive isolation The findings also provide immense need of developing knowledge assets and management development initiatives for learning interventions, to help understand, analyse and mitigate executive isolation, in the interest of the organizational learning and development.

Earlier research work have contextualized the executive isolation impact on CEOs ability to be a leader. This study extends it to include the implications of leadership and learning culture on the teams that are affected by organization culture, power distance, decision-making and analysing the gap between the understandings about executive isolation of the CEOs. Eventually, it interprets how CEOs courting the executive isolation impacts the overall developmental culture of the organization. This will help in asserting the serious need of new learning frameworks needed to minimize the impact of CEO-level executive isolation.

The purpose of this paper is to analyse data literacy in the new Digital Competence Framework for Citizens (DigComp 2.2). Mid-2022 the Joint Research Centre of the European Commission published a new version of the DigComp (EC, 2022). This new version focusses more on the datafication of society and emerging technologies, such as artificial intelligence. This paper analyses how DigComp 2.2 defines data literacy and how the framework looks at this from a societal lens.

This study critically examines DigComp 2.2, using the data literacy competence model developed by the Knowledge Centre for Digital and Media Literacy Flanders-Belgium. The examples of knowledge, skills and attitudes focussing on data literacy (n = 84) are coded and mapped onto the data literacy competence model, which differentiates between using data and understanding data.

Data literacy is well-covered in the framework, but there is a stronger emphasis on understanding data rather than using data, for example, collecting data is only coded once. Thematically, DigComp 2.2 primarily focusses on security and privacy (31 codes), with less attention given to the societal impact of data, such as environmental impact or data fairness.

Given the datafication of society, data literacy has become increasingly important. DigComp is widely used across different disciplines and now integrates data literacy as a required competence for citizens. It is, thus, relevant to analyse its views on data literacy and emerging technologies, as it will have a strong impact on education in Europe.

Cybersecurity competitions can effectively develop skills, but engaging a wide learner spectrum is challenging. This study aims to investigate the perceptions of cybersecurity competitions among Reddit users. These users constitute a substantial demographic of young individuals, often participating in communities oriented towards college students or cybersecurity enthusiasts. The authors specifically focus on novice learners who showed an interest in cybersecurity but have not participated in competitions. By understanding their views and concerns, the authors aim to devise strategies to encourage their continuous involvement in cybersecurity learning. The Reddit platform provides unique access to this significant demographic, contributing to enhancing and diversifying the cybersecurity workforce.

The authors propose to mine Reddit posts for information about learners’ attitudes, interests and experiences with cybersecurity competitions. To mine Reddit posts, the authors developed a text mining approach that integrates computational text mining and qualitative content analysis techniques, and the authors discussed the advantages of the integrated approach.

The authors' text mining approach was successful in extracting the major themes from the collected posts. The authors found that motivated learners would want to form a strategic way to facilitate their learning. In addition, hope and fear collide, which exposes the learners’ interests and challenges.

The authors discussed the findings to provide education and training experts with a thorough understanding of novice learners, allowing them to engage them in the cybersecurity 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.

Limited geometric accuracy is one of the major challenges that hinder the wider application of additive manufacturing (AM). This paper aims to predict in-plane shape deviation for online inspection and compensation to prevent error accumulation and improve shape fidelity in AM.

A sequence-to-sequence model with an attention mechanism (Seq2Seq+Attention) is proposed and implemented to predict subsequent layers or the occluded toolpath deviations after the multiresolution alignment. A shape compensation plan can be performed for the large deviation predicted.

The proposed Seq2Seq+Attention model is able to provide consistent prediction accuracy. The compensation plan proposed based on the predicted deviation can significantly improve the printing fidelity for those layers detected with large deviations.

Based on the experiments conducted on the knee joint samples, the proposed method outperforms the other three machine learning methods for both subsequent layer and occluded toolpath deviation prediction.

This work fills a research gap for predicting in-plane deviation not only for subsequent layers but also for occluded paths due to the missing scanning measurements. It is also combined with the multiresolution alignment and change point detection to determine the necessity of a compensation plan with updated G-code.