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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.

Social exclusion is a complicated psychological phenomenon with behavioral ramifications that influences consumers' lifestyles and behaviors. In contrast, anthropomorphism is a phenomenon that marketing strategists employ and that occurs in customers' lives as a result of social isolation. The literature discusses these two complicated structures as ones that require investigation based on consumer judgments. The purpose of the current study is to understand the fundamental motivations that underlie the propensity for anthropomorphizing in people who suffer social isolation through their pets.

To look into the motivations driving these themes, a study technique with three distinct components was created. Cyberball was employed as a technique to manipulate social exclusion in the initial stage of this research methodology. Two scenarios, one of which had an anthropomorphizing tendency and the other of which did not, were presented to participants who had suffered social exclusion and advanced to the second phase in order to determine the anthropomorphizing tendency. The Attachment to Pets Scale (LAPS), which Johnson et al. (1992) created based on the social support provided by pets, was utilized while creating the scenarios. The Zaltman method was applied as an interviewing technique in the third stage of the research design, with the interviewees being guided by visuals that reflected their emotions and thoughts.

The results of the data analysis were evaluated in light of social psychology. A more thorough expression of the complex relationship between anthropomorphism and those who experience social exclusion has been made. The findings showed that when people anthropomorphize their pets in response to feelings of social exclusion, the motivations that emerge include pure love, loyalty, animals' need for a human, living creature and embracing. The study emphasizes that these ideas will be helpful in customers' interactions with anthropomorphic objects.

As a contribution to the literature, the study findings offer the five major motivations underpinning these beliefs. These findings may help marketing scientists comprehend social exclusion and anthropomorphism, thereby benefiting the individual and society.

The majority of research in the literature (Chen et al., 2017; Epley et al., 2008; Eyssel and Reich, 2013; Waytz et al., 2019) verified that people who were socially excluded would use anthropomorphism, but no studies were discovered about the motivations outlined in the current study. The results of this investigation should add to the body of knowledge in this area. The pet was employed as an anthropomorphism tool in the current study because it is the object that a person chooses to anthropomorphize deliberately and independently. It adds to the study's originality by explaining in the individual's own terminology how he will feel as a result of his social isolation, how he will make up for it and potential responses he may have. In addition to all of these contributions, the study's primary goal of analyzing the motivations behind anthropomorphism yields significant findings that are relevant to both industry and academic research.

This research aims to examine the educational philosophy of teachers in classrooms. Teachers' educational philosophy influences the power balance, course content function, student and teacher roles, responsibility for learning and assessment purposes and processes. The research also analyzes whether gender, qualification, specialization and experience significantly influence classroom educational philosophies.

The study utilized a quantitative research design, utilizing data from 193 teachers working in a public higher education institution in the Sultanate of Oman. The study utilized a survey method to solicit data from the respondents. Besides utilizing descriptive statistics such as mean and standard deviation, the study used analysis of variance (ANOVA) and t-test to test the hypotheses.

Analysis revealed an instructional strategy's preference, including elements of both teacher-centered and student-centered educational philosophies. Elements of progressivism, constructivism, reconstructivism and perennialism are more relevant in the teacher's instructional design. The results show no significant differences in teachers' pedagogical philosophy that exist based on gender, specialization and experience. However, teachers' age significantly influences their educational philosophy preferences.

This research centers on a public higher education institution in the Sultanate of Oman, with a particular focus on the Department of Business Studies. This resarch delimits its discussion on teachers' chosen educational philosophy. Other possible factors may also impact student retention and effective teaching and learning.

This research offers valuable insights to academicians, higher education administrators, and policymakers. Specifically, this research emphasizes the significance of employing a blended approach, which incorporates both student-centered and teacher-centered educational philosophies, to enhance student engagement, retention, and effective teaching and learning.

This research emphasizes the importance of educators' adoption of a blended educational philosophy in promoting student retention and engagement within higher education institutions. To achieve desirable outcomes, policymakers in higher education must ascertain which educational philosophy is most effective in the classroom. Additionally, ensuring congruence between preferred educational philosophy and teachers’  instructional practices is vital in facilitating effective teaching and learning.

To the best of the authors’ knowledge, this study is the first of its kind among teachers in higher education in the Sultanate of Oman. The outcome of this study helps detail the specific strategies teachers deploy and categorize into various educational philosophies.

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.

With the advancement of technology, microlearning has emerged as a promising method to improve the efficacy of teaching and learning. This study aims to investigate the document types, volume, growth trajectory, geographic contribution, coauthor relationships, prominent authors, research groups, influential documents and publication outlets in the microlearning literature.

We adapt the PRISMA guidelines to assess the eligibility of 297 Scopus-indexed documents from 2002 to 2021. Each was manually labeled by educational level. Descriptive statistics and science mapping were conducted to highlight relevant objects and their patterns in the knowledge base.

This study confirms the increasing trend of microlearning publications over the last two decades, with conference papers dominating the microlearning literature (178 documents, 59.86%). Despite global contributions, a concentrated effort from scholars in 15 countries (22.39%) yielded 68.8% of all documents, while the remaining papers were dispersed across 52 other nations (77.61%). Another significant finding is that most documents pertain to three educational level categories: lifelong learning, higher education and all educational levels. In addition, this research highlights six key themes in the microlearning domain, encompassing (1) Design and evaluation of mobile learning, (2) Microlearning adaptation in MOOCs, (3) Language teaching and learning, (4) Workflow of a microlearning system, (5) Microlearning content design, (6) Health competence and health behaviors. Other aspects analyzed in this study include the most prominent authors, research groups, documents and references.

The finding represents all topics at various educational levels to offer a comprehensive view of the knowledge base.

In recent years, there has been growing interest in the use of stainless steel (SS) in reinforced concrete (RC) structures due to its distinctive corrosion resistance and excellent mechanical properties. To ensure effective synergy between SS and concrete, it is necessary to develop a time-saving approach to accurately determine the ultimate bond strength τ_u between the two materials in RC structures.

Three robust machine learning (ML) models, including support vector regression (SVR), random forest (RF) and extreme gradient boosting (XGBoost), are employed to predict τ_u between ribbed SS and concrete. Model hyperparameters are fine-tuned using Bayesian optimization (BO) with 10-fold cross-validation. The interpretable techniques including partial dependence plots (PDPs) and Shapley additive explanation (SHAP) are also utilized to figure out the relationship between input features and output for the best model.

Among the three ML models, BO-XGBoost exhibits the strongest generalization and highest accuracy in estimating τ_u. According to SHAP value-based feature importance, compressive strength of concrete f_c emerges as the most prominent feature, followed by concrete cover thickness c, while the embedment length to diameter ratio l/d, and the diameter d for SS are deemed less important features. Properly increasing c and f_c can enhance τ_u between ribbed SS and concrete.

An online graphical user interface (GUI) has been developed based on BO-XGBoost to estimate τ_u. This tool can be utilized in structural design of RC structures with ribbed SS as reinforcement.

Drawing on the social presence theory, this study aims to explore how supplier–customer social media interactions influence supplier observers’ trust in the customers and what mechanisms contribute to variation in trust experience.

A total of 36 semi-structured interviews were conducted with Chinese suppliers using WeChat for business-to-business interactions. Data were analyzed in three steps: open coding, axial coding and selective coding.

Findings reveal that varied trust is based not only on the categories of social presence of interaction – whether social presence is embedded in informative interactions – but also on the perceived selectivity in social presence. Observer suppliers who experience selectivity during social and affective interactions create a perception of hidden information and an unhealthy relationship atmosphere, and report a sense of emotional vulnerability, thus eroding cognitive and affective trust.

The findings contribute new understandings to social presence theory by exploring the social presence of interactions in a supplier–supplier–customer triad and offer valuable insights into business-to-business social media literature by adopting a suppliers’ viewpoint to unpack the mechanisms of how social presence of interaction positively and negatively influences suppliers’ trust and behavioral responses.

The purpose of this study is to investigate consumers' emotional responses to artificial intelligence (AI) defeating people. Meanwhile, the authors investigate the negative spillover effect of AI defeating people on consumers' attitudes toward AI companies. The authors also try to alleviate this spillover effect.

Using four studies to test the hypotheses. In Study 1, the authors use the fine-tuned Bidirectional Encoder Representations from the Transformers algorithm to run a sentiment analysis to investigate how AI defeating people influences consumers' emotions. In Studies 2 to 4, the authors test the effect of AI defeating people on consumers' attitudes, the mediating effect of negative emotions and the moderating effect of different intentions.

The authors find that AI defeating people increases consumers' negative emotions. In terms of downstream consequences, AI defeating people induces a spillover effect on consumers' unfavorable attitudes toward AI companies. Emphasizing the intention of helping people can effectively mitigate this negative spillover effect.

The authors' findings remind governments, policymakers and AI companies to pay attention to the negative effect of AI defeating people and take reasonable steps to alleviate this negative effect. The authors help consumers rationally understand this phenomenon and correctly control and reduce unnecessary negative emotions in the AI era.

This paper is the first study to examine the adverse effects of AI defeating humans. The authors contribute to research on the dark side of AI, the outcomes of competition matches and the method to analyze emotions in user-generated content (UGC).

Traditional purchasing best practices primarily follow a commercial logic and may not necessarily be applicable for social enterprises (SEs) supplier selection. This study examines how SEs focused on poverty alleviation select suppliers amidst competing institutional logics to achieve both social impact and economic performance.

A grounded theory methodology is applied to guide semi-structured interviews with 18 fair trade verified SEs. Constant comparison methods aided in determining the point of data saturation was reached.

The results of this study indicate that SEs select marginalized suppliers based on implicit criteria that is initially based on social-welfare logic and then through a blend of commercial and social-welfare logic based on company structure.

This study is the first to reveal that SEs addressing social issues do not follow the traditional criteria for supplier selection but have their own unique selection criteria when selecting suppliers.

The study explored how information institutions can support the resilience of parents of adolescents affected by the Russia–Ukraine war. Ukrainian parents are facing major challenges of supporting their teenagers through a difficult developmental phase while also “buffering” their war-related hardships. By supporting parents, information institutions can also support children.

Fifteen parents were interviewed about mental health challenges and resources that are helpful and/or missing from their support systems. Recordings of participant narratives were analyzed using the qualitative thematic analysis technique.

The findings indicate that both teens and parents rely on internal resilience skills, family, friends and community resources to support themselves. However, a number of additional resources could be offered by information institutions, including content for (1) teens on developing skills in communication, interpersonal relationships, problem solving and academic pursuits; (2) parents on child development and opportunities in their host countries; both groups on (3) both groups on mental health first aid and safe spaces to meet peers. The study recommendations will be of interest to information professionals working with families, especially families affected by disasters.

The study relied on a small convenience sample of participants.

Study recommendations would be of interest to information professionals who develop and provide services to families affected by natural and manmade disasters.

Study recommendations improve understanding of the (potential) role of information institutions and libraries in strengthening family and community resilience.

The study offers a rare insight into experiences of war-affect families and provides evidence-driven recommendations for information institutions to support family and community resilience.