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This study aims to investigate the interplay between environmental concerns, consumer attitudes and their influence on sustainable practices within the craft beer industry, exploring how consumers’ perception of environmental attributes impact their purchase intentions regarding craft beer.

The study used a quantitative approach, using a self-administered survey instrument comprised 28 Likert-scale items. Data was gathered from a convenience sample of 319 craft beer consumers. Confirmatory factor analysis was conducted to assess the measurement model’s validity and reliability. Subsequently, partial least squares structural equation modeling was used to test the hypothesized relationships.

This study’s findings challenge established notions within the field, demonstrating that water conservation, carbon footprint reduction, organic farming and local sourcing practices hold significant influence on consumers’ purchase intentions regarding sustainable craft beer. These findings depart from previous research that suggested a less pronounced impact of specific sustainability practices on consumer behavior.

This study contributes to the literature by revealing consumer insights into the environmental implications of craft beer choices, empowering them to make informed purchasing decisions aligned with their sustainability values. However, acknowledging potential limitations, sample size and demographic composition (young adults in Brazil) potentially impact the generalizability of the findings.

This study addresses the understanding of consumer attitudes and priorities regarding environmental sustainability within the craft beer industry. By focusing on the tangibility of specific sustainable attributes the research shows how these practices influence consumer perceptions and purchasing intentions.

Misinformation surrounding the Sustainable Development Goals (SDGs) has contributed to the formation of misbeliefs among the public. The purpose of this paper is to investigate public sentiment and misbeliefs about the SDGs on the YouTube platform.

The authors extracted 8,016 comments from YouTube videos associated with SDGs. The authors used a pre-trained Python library NRC lexicon for sentiment and emotion analysis, and to extract latent topics, the authors used BERTopic for topic modeling.

The authors found eight emotions, with negativity outweighing positivity, in the comment section. In addition, the authors identified the top 20 topics discussing various SDGs and SDG-related misbeliefs.

The authors reported topics related to public misbeliefs about SDGs and associated keywords. These keywords can be used to formulate social media content moderation strategies to screen out content that creates these misbeliefs. The result of hierarchical clustering can be used to devise and optimize response strategies by governments and policymakers to counter public misbeliefs.

This study represents an initial endeavor to gain a deeper understanding of the public’s misbeliefs regarding SDGs. The authors identified novel misbeliefs about SDGs that previous literature has not studied. Furthermore, the authors introduce an algorithm BERTopic for topic modeling that leverages transformer architecture for context-aware topic modeling.

The outbreak of COVID-19 significantly disrupted educational activities and forced universities to rapidly transition from the traditional face-to-face (F2F) environment to online learning formats. The purpose of this paper is to examine the effects of self-directed learning (SDL) on three instructional modalities (F2F, online and HyFlex) among emerging adults. The authors propose that class interaction enjoyment serves as a channel to understand how SDL relates to students’ satisfaction and stress reduction.

An online survey was distributed to the emerging adults, aged 18–25, at six universities across five different US states. Construct validity and reliability were tested by using confirmatory factor analysis. The moderated mediation relationship was examined by calculating the indirect effects of each course delivery format.

The results show that the positive indirect effect of SDL on stress reduction via interaction enjoyment was stronger for F2F classes. In addition, the positive indirect effect of SDL on class satisfaction via interaction enjoyment was stronger for HyFlex classes.

This literature has shown contradictory results: the effects of SDL on student satisfaction and stress reduction prove to be sometimes positive, sometimes non-significant. To better understand this relationship, the authors aim at a mediating variable – enjoyment of class interaction – as a mechanism, and a moderating variable – the instructional modality – as a boundary condition. This research contributes to emerging adults learning literature by involving the interplay among SDL, enjoyment of class interaction and the instructional modality.

Attracting resources (financial and non-financial), supporters and the community’s attention to sports and physical activity has become essential for local sports associations (LSAs). Corporate social responsibility (CSR) initiatives are innovative and less imitable tools for strengthening relationships with customers (Lim et al., 2018), and there needs to be relevant research on CSR and the variables discussed in this study at the level of small sports organizations. Moreover, past research has focused on professional sports organizations in developed and non-Islamic countries. So, the following research questions are: What is the influence of CSR initiatives on the organizational reputation, brand equity and customer satisfaction of LSAs operating in the context of recreational sports, particularly in Islamic developing countries, and how does this relationship evolve within the framework of small sports organizations?

Participants (n = 290) consisted of all customers who used the services of LSAs in the Islamic Republic of Iran). This research seeks to measure the relationship between variables within a causal model based on structural equation modeling.

This study critically examines the connection between CSR, customer satisfaction, organizational reputation and brand equity in LSAs. This study presents a model that explores how CSR influences customer satisfaction, reputation and brand equity in LSAs in developing countries.

Consequently, customers are likely to feel more satisfied with LSAs that demonstrate a commitment to CSR, and this leads to evaluations of the organization’s reputation and brand equity, ultimately resulting in outcomes for them.

This research presents a comprehensive theoretical model that examines the relationship between CSR, customer satisfaction, reputation and brand equity of LSAs in developing Islamic countries. LSAs must understand and recognize customer interests in social issues and their response to various CSR programs.

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.

This paper aims to examine the antecedents of adoption intention of profit-sharing investment deposits (PSID) among Moroccan customers.

Applying an extended version of diffusion of innovation (DOI) theory and using a non-probability sampling technique with convenience approach, a quantitative survey was developed and administered to 171 Islamic banking users. Structural equation modeling was then used to evaluate the significance of relationships between the various variables under study using SPSS 21.0 and AMOS 26.0 statistical packages.

Empirical findings of the structural analysis indicated a significant direct relationship between adoption intention and six out of seven variables: perceived relative advantage, perceived compatibility, perceived complexity, perceived risk, religiosity and social influence, all of which had a significant effect on Moroccan customers’ intention to invest their funds in profit-sharing based deposit instruments, whereas customer awareness exerted an insignificant positive effect.

The absence of a longitudinal study tracking the actual adoption behavior is the main limitation of this study. Furthermore, data were collected solely from Islamic banking users. Finally, despite being insightful, the empirical findings should be generalized with caution since the sample was purposely selected by the banks’ management.

This study implied that participatory banks should pay substantial attention to risk perceptions, as PSID adoption intention is typically inhibited by high perceived risks associated with these products. Moreover, this study provides great indications to Moroccan regulators and policymakers on a number of issues related to this emerging business.

To the best of the authors’ knowledge, this paper represents the first attempt to confirm the effectiveness of the Rogers’ DOI in examining the intention to adopt a financial innovation in the Moroccan context. It is also the first of its kind to address customers’ apprehensions regarding profit-sharing investment products.

The research paper examines the complex relationship between gamification, student engagement and academic performance in educational environments. The study employed a structural equation model that highlights important connections among key constructs within the educational setting.

This research aims to explore the connection between gamification, student engagement and academic performance in educational settings. The study employs various statistical techniques such as factor analysis, Kaiser–Meyer–Olkin (KMO), Bartlett’s test, component transformation matrix, correlation and regression analysis, descriptive statistics, ANOVA, coefficients and coefficient correlations, residual statistics and confirmatory factor analysis (CFA) to analyze the data.

It was found that active participation by the instructor and good time management skills have a positive impact on student engagement levels (β = 0.380, p < 0.001; β = 0.433 and p < 0.001). However, peer interaction does not significantly predict student engagement (β = −0.068 and p = 0.352). Additionally, there is a positive correlation between student engagement and performance (β = 0.280 and p < 0.001).

The study highlights the importance of innovative design to fully utilize gamification. Future research should consider design, user characteristics and educational context. The findings can guide informed decisions about gamification in education, fostering motivation and learning objectives.

The study presents a reliable tool for assessing student engagement and performance in educational settings, demonstrating high Cronbach’s alpha and robust reliability. It identifies student engagement and time management as significant predictors of Global Learning Outcome. The findings can inform decisions on implementing gamification in educational settings, promoting intrinsic motivation and aligning with learning objectives.

The research highlights the transformative impact of gamification on educational practices, highlighting its potential to enhance student experiences, motivate, promote diversity and improve long-term academic performance, highlighting the trend of integrating technology into education.

In today’s ever-changing education landscape, it is essential to incorporate innovative techniques to keep students engaged and enthusiastic about learning. Gamification is one such approach that has become increasingly popular. It is a concept that takes inspiration from the immersive world of games to enhance the overall learning experience.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

Quality management practices (QMP) have stood as one of the critical strategic differentiators for enhancing firm performance. The production and manufacturing industry is the main driving force of economic growth and social development for any developed or developing country. This study aims to focus on two primary dimensions of QMP: soft quality management practices (SQMP) and hard quality management practices (HQMP) from the socio-technical system perspectives. Based on institutional theory perspectives, the study explores the impact of SQMP and HQMP on quality performance (QP), innovation performance (IVP) and financial performance (FP) in Indian oil processing organizations.

A proposed research model is validated using 289 cross-sectional survey data collected from the senior officials of oil processing firms in India. Covariance-based structural equation modeling is used to verify the proposed theoretical model.

SQMP, directly and indirectly, influenced QP and IVP while only indirectly to FP mediated through QP. HQMP directly impacted only QP while indirectly to IVP and FP mediated through QP.

Impact of organizational legitimacy in proper utilization or application of QMP in achieving the firm sustainable growth. The future study may address the following Research Question (RQ) also: How do QMP enhance the legitimacy of organizations operating in the oil processing industries? Are there specific mechanisms or pathways through which improved performance contributes to enhanced organizational legitimacy? How does legitimacy impact the success and sustainability of organizations, particularly, within the context of the oil processing industries? Are there regulatory requirements or industry certifications that organizations must adhere to in order to maintain legitimacy?

Similarly, manufacturing firms establish QMP of interaction and maintaining relationships with all the stakeholders, total employee empowerment and involvement, workforce commitment and workforce management, helping to control their reputations and maintain legitimacy (Li et al., 2023). Similarly, in the health industry, the health management information system (HMIS), which uses the DHIS2 platform, establishes that isomorphism legitimizes data QMP among health practitioners and, subsequently, data quality. Further, it was concluded that mimetic isomorphism led to moral and pragmatic legitimacy. In contrast, normative isomorphism led to cognitive legitimacy within the HMIS structure and helped to attain the correctness and timeliness of the data and reports, respectively (Msendema et al., 2023). Quality, flexibility and efficiency of Big Data Analytics through better storage, speed and significance can optimize the operational performance of a manufacturing firm (Verma et al., 2023).

The study provides the academician with the different dimensions of QMP. The study demonstrates how a firm develops multiple performance capabilities through proper QMP. Also, it shows how vital behavioral and managerial perspectives are to QMP and statistically solid tools and techniques. The study draws their importance to risk factors involved in the firms. Since the SQMP play a vital role, thus, emphasis on the behavioral dimension of quality requires more investigation and is in line with hard technological advancements in the quality field.

The study of the impact of HQMP and SQMP on performance is still not established. There are inconsistencies in the findings. The study of the impact of HQMP and SQMP in oil processing industries has not dealt with before. The effects of HQMP and SQMP on the firm’s FP have least been dealt. In context to the intended influence of QM implementation, QP has not been examined as a potential mediator between FP. Research carried out in the past is limited to American and European countries. However, a limited study was done in Asia, and no study has been conducted in the Indian context.

This study aims to examine the effect of application architecture and application efficiency on the intention to continue using Islamic bank with data security as a moderator. The investigation was situated within the framework of a hacker attack that compromised the security of customer data at one of Indonesia’s largest Islamic bank.

A survey questionnaire method was used, and the sample population comprised users of Islamic bank in Indonesia. The respondents were then selected purposively with the criteria of individuals who were using mobile banking services. Furthermore, data collection in this study was carried out by distributing questionnaires online. To validate the questionnaire, consultation and validation were conducted by engaging four experts and conducting a pilot study. Hypothesis testing was performed using the structural equation modeling method based on partial least squares.

The results of the partial least square structural model assessment showed that application efficiency and data security positively influenced the intention to continue using Islamic bank, while application architecture had no effect. Furthermore, data security could not moderate the relationship between application architecture and efficiency toward the intention to continue using Islamic bank.

The results of this study suggested that Islamic banking practitioners must prioritize the enhancement of digital banking services, with a specific focus on improving application efficiency and ensuring robust data security. These two dimensions were critical determinants influencing the intention to continue using Islamic bank.

This study addressed the issue of data security as a moderator, particularly in the context of hacker attacks targeting a major Islamic bank in Indonesia. Furthermore, this current report expounded on the study conducted by Mir et al. (2022) by introducing novel dimensions to the e-service quality of internet banking.