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Critical examination of Bangladeshi laws related to workers’ rights in the garment industry. This paper aims to examine the impact of foreign direct investment (FDI) on the protection of garment workers’ rights in Bangladesh, analyzing how international investment practices influence labor standards and the overall well-being of workers in the garment industry.

In this study, qualitative and analytical methods has been used to analyze legal frameworks related to labor rights in Bangladesh and BITs.

The findings indicate a need to strengthen the current legal framework to better protect workers' rights in Bangladesh. The study also provides recommendations for the relevant authorities to improve the existing laws.

Novel idea critically evaluating the Bangladeshi legal framework in the context of foreign direct investment and implications for worker's rights.

This paper describes a model of “Learning to care” derived from a study exploring how care workers in care homes learn to care for people living with dementia. The “Learning to care” model is primarily informal in nature in which influences such as formalised training and organisational culture impact care outcomes indirectly rather than directly.

This study used a focused, critical ethnographic approach in two care homes in England resulting in 63 h of observation of care of people living with advanced dementia, 15 semi-structured interviews and 90 in-situ ethnographic interviews with care staff.

The findings reveal a three-level model of learning to care. At the level of day-to-day interactions is a mechanism for learning that is wholly informal and follows the maxim “What Works is What Matters”. Workers draw on resources and information within this process derived from their personal experiences, resident influences and care home cultural knowledge. Cultural knowledge is created through a worker’s interactions with colleagues and the training they receive, meaning that these organisational level influences affect care practice only indirectly via the “What Works is What Matters” mechanism.

This study makes an original contribution by explaining the nature of day-to-day informal learning processes as experienced by care workers and those living with dementia in care homes. In particular, it illuminates the specific mechanisms by which organisational culture has an effect on care practice and the limitations of formal training in influencing such practice.

With the increasing global popularity of the gig economy encompassing freelancers, online platform workers, self-employed, on-call workers and other temporary contractual workers, this study aims to review research focusing on the gig economy to explore the nuances of working in various alternative work arrangements. We aim to enhance the understanding of existing literature and highlight underexplored areas for future research.

A systematic literature review of 258 studies based on gig economy and gig work was conducted. It follows systematic search criteria and SPAR-4-SLR protocols. The analysis uses different gig worker profiles given by Watson et al. (2021) to categorize studies, followed by thematic analysis to identify patterns in the literature.

Our analysis of studies on gig economy shows that the majority of existing literature is based on gig service providers. Within this literature subset, major areas of study are work conditions, financial conditions, emergence and role of collectives, the impact of algorithmic management and the impact of rise of the gig economy on entrepreneurship. There is little research focusing on gig goods providers, gig data providers, agency gig workers and traditional gig worker profiles. More focus is also needed on emerging markets and markets transitioning to the gig economy.

Differing from reviews that have attempted to define gig work and workers, we focus on literature on the lived experiences of gig workers. By reviewing studies on different gig worker profiles, we are able to highlight future research directions for pointed sample sets and comparative underexplored areas.

The focus of this article is in documentation in substance abuse inpatient rehabilitation. Our article scrutinizes how workers give accounts of the documentation in the inpatient substance abuse rehabilitation unit and what kind of client information the workers record.

The study focuses on institutional interaction and practices. Our data consist of interviews with substance abuse rehabilitation professionals (N = 15). We analyzed the interviews using content analysis and the account concept in keeping with the ethnomethodological research tradition.

Study shows how workers account for the significance of documentation. Workers deemed documentation significant in four different ways: in gathering basic and rehabilitation information, in storing and transmitting information, as a tool for analysis and assessment and in supporting linguistic transparency in substance abuse rehabilitation. Workers justified the significance of documentation by the legal requirement to record information about clients. Documented information enables clear management of client information and supports substance abuse rehabilitation work in various ways. Documentation contains descriptions of the client’s situation and work performed. Additionally, documentation serves as a tool for communication among social care professional.

Thus the research show that documentation plays a significance part in the inpatient substance abuse rehabilitation and are connected to its institutional tasks and practices.

The purpose of this study is to investigate the usage of digital technologies (DTs) in improving the mental health of workers on construction sites.

A mixed research approach was used in the study, which comprised a questionnaire survey and two phases of semi-structured interviews. Purposive sampling was used to determine the interviewees and respondents of the questionnaire survey. Weighted mean rating (WMR) and manual content analysis were used to rank and evaluate the collected data.

The findings of this study revealed bipolar disorder, anxiety disorders, attention-deficit/hyperactivity disorder, obsessive-compulsive disorder, work-related stress and depression as the six most significant mental disorders (MDs) among the construction workforce and 30 causes for them. Moreover, 27 symptoms were related to the six most significant MDs, and sweating was the most significant symptom among them. Despite that, 16 DTs were found to be suitable in mitigating the causes for the most significant MDs.

There are numerous studies conducted on the application of DTs to construction operations. However, insufficient studies have been conducted focusing on the application of DTs in improving the mental health of workers at construction sites. This study can thus influence the use of DTs for tackling the common causes for MDs by bringing a new paradigm to the construction industry.

The motivation to participate in crowdsourcing (CS) platforms is an emerging challenge. Although researchers and practitioners have focused on crowd motivation in the past, the results obtained through such practices have not been satisfactory. Researchers have left unexplored research areas related to CS pillars, such as the evolution of the crowd’s primary motivations, seekers applying effective policies and incentives, platform design challenges and addressing task complexity using the synchronicity of the crowd. Researchers are now more inclined to address these issues by focusing on sustaining the crowd’s motivation; however, sustaining the crowd’s motivation has many challenges.

To fill this gap, this study conducted a systematic literature review (SLR) to investigate and map the challenges and factors affecting sustained motivation during CS with the overcoming implications. Studies that satisfied the inclusion criteria were published between 2010 and 2021.

Important sustainable factors are extracted using the grounded theory that has sustained participation and the factors' cohesion leads to the identification of challenges that the pillars of CS face. Crowds being the most vital part of CS contests face the challenge of engagement. The results reported the factors that affect the crowd’s primary and post-intentions, perceived value of incentives and social and communal interaction. Seekers face the challenge of knowledge and understanding; the results identify the reason behind the crowd’s demotivation and the impact of theories and factors on the crowd's psychological needs which helped in sustaining participation. Similarly, the platforms face the challenge of being successful and demanding, the results identify the latest technologies, designs and features that seekers proclaim and need the platforms designer's attention. The identified task challenges are completion and achievement; the authors have identified the impact of trait of task and solving mechanisms that have sustained participation.

The study identifies, explores and summarizes the challenges on CS pillars researchers are facing now to sustain contributions by keeping participants motivated during online campaigns. Similarly, the study highlights the implication to overcome the challenges by identifying and prioritizing the areas concerning sustainability through the adoption of innovative methods or policies that can guarantee sustained participation.

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.

Warehouses are under pressure to operate as efficiently as possible. In pursuit of attaining high efficiency in the order picking process, the warehouse manager must take several planning decisions, typically supported by a central planning system. However, highly centralised work erodes the autonomy of warehouse workers, interfering with worker well-being and productivity. This study holistically explores the impact of a work system with more decision autonomy for order pickers.

We conduct a unique field experiment in a real-world warehouse and use a within-subjects design to compare two work systems, one with worker autonomy and one without. 18 permanent employees participate in our study, in which we measure both psychosocial and physical well-being as well as productivity. Post-experimental interviews are conducted to delve deeper into the observed effects.

Our study illustrates that involving order pickers in operational decisions can benefit their job satisfaction and motivation without compromising productivity. Although we fail to find significance at the conventional level (α = 0.05), we do find marginally significant effects of our treatment on physical well-being aspects. Furthermore, our intervention invoked a highly positive user experience.

We show that slightly loosening tight process control results in organisational and individual benefits without endangering smooth operational flows. The warehouse in this paper acknowledged this and decided to permanently work according to this philosophy.

This study is the first to holistically explore the effects of a participatory work setting in a real-world warehouse.

The purpose of this paper is to embark on evaluating the role of Chat Generative-Trained Transformer (ChatGPT) in personal knowledge management (PKM) practices of individual knowledge workers across varied disciplines.

The methodology involves four steps, i.e. literature search, screening and selection of relevant data, data analysis and data synthesis related to KM, PKM and generative artificial intelligence (AI) with a focus on ChatGPT. The findings are then synthesized to develop a viewpoint on the challenges and opportunities brought by ChatGPT for individual knowledge workers in enhancing their PKM capability.

This work highlights the prevailing challenges and opportunities experienced by knowledge workers while leveraging PKM through implying ChatGPT. It also encapsulates how some management theories back the cruciality of generative AI (specifically ChatGPT) for PKM.

This study identifies the challenges and opportunities. from existing studies and does not imply empirical data/result. The authors believe that findings can be adjusted to diverse domains regarding knowledge workers’ PKM endeavors. This paper draws some conclusions and calls for further empirical research.

ChatGPT’s capability to accelerate organizational performance compelled scholars to focus in this domain. The linkage of ChatGPT to Knowledge Management is an under-explored area specifically the role of ChatGPT on PKM hasn't been given attention in the existing work. This is one of the earliest studies to explore this context.

This paper aims to explore the implications of algorithmic management on careers and employment relationships in the Nigerian gig economy. Specifically, drawing on labour process theory (LPT), this study provides an understanding of the production relations beyond the “traditional standard” to “nonstandard” forms of employment in a gig economy mediated by digital platforms or digital forms of work, especially on ride-hailing platforms (Uber and Bolt).

This study adopted the interpretive qualitative approach and a semi-structured interview of 49 participants, including 46 platform drivers and 3 platform managers from Uber and Bolt.

This study addresses the theoretical underpinnings of the LPT as it relates to algorithmic management and control in the digital platform economy. The study revealed that, despite the ultra-precarious working conditions and persistent uncertainty in employment relations under algorithmic management, the underlying key factors that motivate workers to engage in digital platform work include higher job flexibility and autonomy, as well as having a source of income. This study captured the human-digital interface and labour processes related to digital platform work in Nigeria. Findings of this study also revealed that algorithmic management enables a transactional exchange between platform providers and drivers, while relational exchanges occur between drivers and customers/passengers. Finally, this study highlighted the perceived impact of algorithmic management on the attitude and performance of workers.

The research presents an interesting case study to investigate the influence of algorithmic management and labour processes on employment relationships in the largest emerging economy in Africa.