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The objective of this paper is to further the understanding of netnography as a research method for supply chain academics. Netnography is a method for gathering and gaining insight from industry-specific online communities. We prescribe that viewing netnography through the lens of the supply chain will permit researchers to explore, discover, understand, describe or report concepts or phenomena that have previously been studied via survey research or quantitative modeling.

To introduce netnography to supply chain research, we propose a framework to guide how netnography can be adopted and used. Definitions and directions are provided, highlighting some of the practices within netnographic research.

Netnography provides the researcher with another avenue to pursue answers to research questions, either alone or in conjunction with the dominant methods of survey research and quantitative modeling. It provides another tool in the researchers’ toolbox to engage practitioners in the field.

The development of netnography as a research method is associated with Robert Kozinets. He developed the method to study online communities in consumer behavior. We justify why this method can be applied to supply chain research, how to collect data and provide research examples of its use. This technique has room to grow as a supply chain research method.

This collaborative paper presents three case studies on four scholars' experiences with remote data collection. The authors highlight the challenges and strengths of online qualitative research across three disparate projects: an interdisciplinary exploration of matrilineal heritage, an examination of Irish women's sexual identity and an investigation of dress practices among Tz'utujil-Maya.

Qualitative researchers traditionally go into the field to explore and understand social phenomena. At the onset of the COVID-19 pandemic, while people faced the daily realities of a worldwide crisis from within their homes, remote data collection became a necessary strategy to pursue knowledge. As a result, researchers adapted to unknowns regarding recruiting, scheduling, technology, interviewing and analysis.

Participant and researcher experiences during the adaptation to remote interviewing yielded important lessons on research strategies.

Outcomes from these studies highlight the potential value of online data collection alongside the necessity for flexibility in designing and conducting qualitative research.

Circular supply chain management (CSCM) is considered a promising solution to attain sustainability in the current industrial system. Despite the exigency of this approach, its application in the food industry is a challenge because of the nature of the industry and CSCM being a novel approach. The purpose of this study is to develop an industry-based systematic analysis of CSCM by examining the challenges for its application, exploring the effects of recognised challenges on various food supply chain (FSC) stages and investigating the business processes as drivers.

Stakeholder theory guided the need to consider stakeholders’ views in this research and key stakeholders directly from the food circular supply chain were identified and interviewed (n = 36) following qualitative methods.

Overall, the study reveals that knowledge, perception towards environmental initiatives and economic viability are the major barriers to circular supply chain transition in the UK FSC.

This research provides a holistic perspective analysing the loopholes in different stages of the supply chain and investigating the way a particular circular supply chain stage is affected by recognised challenges through stakeholder theory, which will be a contribution to designing management-level strategies. Reconceptualising this practice would be beneficial in bringing three-tier (economic, environmental and social) benefits and will be supportive to engage stakeholders in the sustainability agenda.

The study aims to drive conceptual clarity around resistance to information technology projects, integrating multiple facets of the phenomenon from earlier studies.

The study conducts a meta-synthesis of qualitative studies on resistance to technology projects; it analyzes those studies at a case-specific level, compares and contrasts emergent concepts against each other, and “translates” those to the rest of the studies. The study uses the seven-step meta-ethnography method by Noblit and Hare to reciprocally translate emergent concepts to construct the conceptual model.

Through meta-synthesis, the study derives a new conceptual model for resistance to information technology projects, exemplifying how the identified antecedents create user resistance and how the phenomenon progresses within organizations.

This study enriches the observations and conclusions of past individual studies while explicating various facets of the mechanisms that generate and progress technology resistance within organizations. It offers fresh insights into the equivocal nature of the phenomenon and the distinctive ways it progresses from individual to group level.

Many ambitious and costly digital transformation efforts do not succeed due to user resistance. Understanding the mechanisms that create user resistance can help organizations manage technology projects better, thereby reducing the technology assimilation gap and protecting returns on related investments.

There have been extensive studies on technology acceptance (enablers) within organizations, while those relating to technology inhibitors are somewhat limited. However, the symmetry of understanding between enablers and inhibitors is vital for organizations to assimilate promising technologies and transform their business models. This model uses a new lens of sensemaking theory to explain how the antecedents trigger perceived threats and resistance behavior; it highlights the nuances around the development of resistance within individuals and its progression to groups. The resultant model offers better generalizability in organizational contexts.

Developing a reliable cost estimate at the early stage of construction projects is challenging due to inadequate project information. Most of the information during this stage is qualitative, posing additional challenges to achieving accurate cost estimates. Additionally, there is a lack of tools that use qualitative project information and forecast the budgets required for project completion. This research, therefore, aims to develop a model for setting project budgets (excluding land) during the pre-conceptual stage of residential buildings, where project information is mainly qualitative.

Due to the qualitative nature of project information at the pre-conception stage, a natural language processing model, DistilBERT (Distilled Bidirectional Encoder Representations from Transformers), was trained to predict the cost range of residential buildings at the pre-conception stage. The training and evaluation data included 63,899 building permit activity records (2021–2022) from the Victorian State Building Authority, Australia. The input data comprised the project description of each record, which included project location and basic material types (floor, frame, roofing, and external wall).

This research designed a novel tool for predicting the project budget based on preliminary project information. The model achieved 79% accuracy in classifying residential buildings into three cost_classes ($100,000-$300,000, $300,000-$500,000, $500,000-$1,200,000) and F1-scores of 0.85, 0.73, and 0.74, respectively. Additionally, the results show that the model learnt the contextual relationship between qualitative data like project location and cost.

The current model was developed using data from Victoria state in Australia; hence, it would not return relevant outcomes for other contexts. However, future studies can adopt the methods to develop similar models for their context.

This research is the first to leverage a deep learning model, DistilBERT, for cost estimation at the pre-conception stage using basic project information like location and material types. Therefore, the model would contribute to overcoming data limitations for cost estimation at the pre-conception stage. Residential building stakeholders, like clients, designers, and estimators, can use the model to forecast the project budget at the pre-conception stage to facilitate decision-making.

This study aims to test the complaints of tourists who visit five-star hotels in Antalya, Turkey, on those same hotels’ websites.

In the study, the data were collected with qualitative methods but analyzed with the mixed analysis method. In this context, the authors collected 1,012 comments on the website between 2016 and 2019.

According to the results of the study, the most intense complaints were found to be concentrated in categories such as ambience, food and staff.

First of all, it is thought that it will make an important contribution to the literature, since different methodologies are adopted in the study. In addition, online shares, evaluations and comments produce positive or negative results for the destination or business in question. It is necessary to closely monitor such activities in electronic environments, as they may have negative consequences, thus revealing the need to take corrective or preventive measures. For this reason, the research is important in terms of not having such a large-scale study in the literature and contributing to the hospitality industry.

This study aims to investigate the factors affecting technological innovation intentions at banks listed in Vietnam, a developing country, to develop business activities and accounting benefits according to the trend of the 4th Industrial Revolution.

To collect and analyze the data for this study, qualitative and quantitative methods were used. Specifically, 20 finance and banking experts and 45 managers in the field of information technology were interviewed in qualitative research over a period of three months. Then, 1,000 questionnaires were sent to banks within six months, with the final sample for quantitative research being 324 respondents. Finally, the structural equation modeling (SEM) was used to check the hypotheses. Regarding the tools used, the qualitative study used a semistructured questionnaire to collect information. Meanwhile, SPSS software was used to analyze quantitative research information, including checking common method bias, nonresponse bias, evaluating scale quality and checking SEM.

The findings show that the usefulness, ease of application, credibility, innovation and efficiency of technology have certain impacts on technological innovation intentions at banks listed in Vietnam. Using the SEM analysis, the results showed that the five factors had a favorable influence on the technological innovation intentions. More specifically, this study proposed adding an efficiency factor, and the results showed that it has the greatest impact on technological innovation intentions.

This study would be considered a continuation of prior studies because it provides empirical evidence for business models at banks listed in developing countries (for example, Vietnam) and so provides useful advice for bank management not only in Vietnam but across Asia. In fact, bank managers should consider introducing new technology as appropriate to make their reports more clear and up-to-date, therefore improving their performance. Banking managers, in particular, should focus on enhancing the bank’s application technology indicators to obtain a competitive edge.

This is a pioneering study that uses a combination of the reasoned action theory, planned behavior theory, transaction cost theory and unified theory of acceptance and use of technology to expand knowledge about technological innovation intentions at listed banks in the context of a developing country. The study also discovered and added the efficiency factor as a key factor affecting the intention to innovate technology at listed banks. These contribute to improving the literature of technological innovation intentions.

The study aims to qualitatively analyze how faculty can mobilize the intellectual capital of higher education institutions (HEIs), comprising human, structural and relational capital to enable the education and learning of individuals with intellectual and developmental disabilities.

Drawing upon the extant literature, the researcher conducted a qualitative study through written, in-depth interviews with a sample of 40 academic staff/faculty members having prior experience in teaching individuals with intellectual and developmental disabilities. The data was collected through a set of questions formulated as key questions, to be asked to all participants for their responses.

Results of the analysis demonstrated that intellectual capital’s contribution to higher education of individuals with intellectual and developmental disabilities can be best understood in terms of its three components/dimensions. Accordingly, three main themes, with each comprising two sub-themes were uncovered. The first theme, leveraging human capital comprised: faculty acumen and faculty training as sub-themes; the second theme, resourcing structural capital comprised: tangible and intangible structural capital as sub-themes; and the third theme, nurturing relational capital comprised: in-class engagement and the second is ex-class connection as sub-themes.

The paper collects data from 40 faculty having prior experience in teaching individuals with intellectual and developmental disabilities to explore and reveal a completely new perspective of looking at intellectual capital as a means of providing accessible and inclusive higher education to differently-abled students, making them a part of the mainstream.

Since COVID-19, many services have burgeoned within the UK, but what about sexual minorities? Since the last review, there are appropriate therapies, but there is often inadequate research. The purpose of this mixed-method review synthesis looking into the efficacy of psychological therapies for sexual minorities. Seven studies were found in total.

A mixed-method review synthesis, three studies looking into the efficacy of psychological therapies for sexual minorities and four studies addressing the experiences of sexual minorities partaking in psychological therapies were identified.

These included three quantitative and four qualitative studies. The minority stress hypothesis is used to formulate problems, but challenges remain to confidentiality and privacy in this context. Therapists still operate within the heteronormative framework, discounting intersectionality in therapy conversations.

Most studies have had low retention rates since 2021. It shows that minority stress needs to be accounted for at the ethics committee and research delivery levels.

Applying a heteronormative framework to sexual minorities is not working. An alternative progress world view is needed.

Health-care clinicians strive for equitable care. Unfortunately, using an equitable health service scale adapted from Levesque et al. (2013), the rating is 3 out of 6. More work is needed to improve services.

Some services are reporting much improvement post-pandemic. Sadly, this is not the case for sexual minorities. Individual and systemic barriers remain.

This study aims to investigate the influence of ChatGPT, an AI-based chatbot, on the digital learning experience of students at Mzumbe University.

This study adopted a qualitative research design to gather in-depth insights from participants. Semi-structured interviews and an analysis of previous chat content were used as primary sources of data. Thematic analysis was used to analyze the qualitative data, allowing for the exploration of participants’ perspectives, experiences and opinions regarding the integration of ChatGPT into the learning process.

The results of the study demonstrated that ChatGPT is widely used in educational contexts and has a positive influence on students’ study habits, academic performance, and understanding of course material. Students appreciated the system’s simplicity, tailored instructions, and the promptness and accuracy of the responses. Despite the possibility of isolated mistakes.

It is important to recognize the limitations of this study. First, the sample size was small, limiting the broad application of the results. Second, this study’s narrow emphasis on students at Mzumbe University limits its applicability in other situations. Furthermore, depending on self-reported experiences, biases, such as individual interpretation or recollection bias, can occur.

Educators can maximize ChatGPT in the classroom by using study insights. Its advantages, such as effectiveness and enhanced performance, highlight the possibility for student-centered learning. Practitioners are guided by their awareness of problems, such as probable errors. Constant updates guarantee ChatGPT’s applicability and provide educators with useful advice.

Peer impact is highlighted in this study concerning social factors on the adoption of AI in education. Resolving issues preserves public confidence. Views influence public opinion and direct policymakers in discussions about safe AI use. It influences public attitudes while navigating the ethical integration of AI.

This study offers insightful information about the impact of ChatGPT on digital learning in Tanzania’s higher education. It makes innovative research contributions that enhance educational practices and emphasizes the advantages, difficulties and demands of responsible usage in the context of AI-based chatbots.