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The research about the concept of Halal tourism has emerged recently. It becomes the vital factor in determining the tourists's satisfaction or their loyalty. Thus, this concept needs to be developed in certain areas because it still not acknowledged. The purpose of this study is to align the theoretical foundations of Halal tourism to conventional tourism paradigms. It investigates the value of Muslim tourists' perception in the context of Malaysian tourist destination. The six variables of Muslim tourist perceived value (MTPV) are examined such as quality, price, emotional, social, physical and non-physical attributes. The respondents of the research are a total of 205 Muslim tourists in Langkawi, Malaysia during October 2021 (Langkawi Tourism Bubble). The results indicated four (4) variables have impacts on tourist satisfaction. Practical implications will impact towards cognitive, affective and Islamic (i.e. Halal) values on tourist satisfaction. It was examined as one of the priotitize destination experience on tourist satisfaction. The findings provide Malaysian tourism with significant managerial implications. It also impacted the Halal tourism as a new approach specifically in the post COVID-19 era. Hence, in improving Muslim tourist satisfaction, destination marketers should consider the Halal tourism. Tour agencies also should scrutinize the product and services value including destination attributes that they offer. The destination's competitiveness will be strengthened with the right destination attraction, facilities, accommodation and activities that suit Muslim tourists.

This paper aims to define the efficiency achieved by urban transport companies in Spain concerning the resources they use, considering the type of management used for implementation, public-private, and size.

This study consisted of an analysis of the efficiency of 229 public-private urban transport operators during the period 2012–2021 using Data Envelopment Analysis, the Malmquist Index and inference estimators to determine productivity, efficiency change into Pure Technical Efficiency Change (PTECH), and scale efficiency change.

Based on the efficiency analysis, the authors concluded that of the 229 companies studied, more than 35 were inefficient in all analysed periods. Considering the sample used, direct management is considered significantly more efficient. It cannot be concluded that the size of these companies influences their efficiency, as the data show unequal development behaviours in the studied years.

This study provides arguments on whether there is a significant difference between the two types of management in the urban transport sector. It also includes firm size as a study variable, which has not been previously considered in other studies related to urban transport efficiency. Efficiency should be a crucial factor in determining funding allocation in this sector, as it encourages operators to optimize and improve their services.

The purpose of this study is to investigate the influence of external enablers (EEs) – factors that are exogenous and independent of the actors – on the rate of new venture creation (NVC) in digital entrepreneurship.

The authors established a unique data panel comprising all formal digital entrepreneurial firms and startups licensed to operate in the digital space from Iran E-Commerce Development Center, from 2012 to 2022, totaling 124,751 businesses classified in 16 sectors. The authors used Poisson regression with robust standard errors to analyze the relationship between external factors and NVC. Additionally, the authors segmented the data set along demographic, spatial and sectoral subsets to compare them using the average treatment effect model.

This analysis confirms that technology readiness and technology intensity, as EEs, significantly boost digital entrepreneurship in a developing country. The authors also explored the impact of the COVID-19 pandemic as a natural environmental enabler and explored its facilitating effects and the enduring impacts post-pandemic. Moreover, the authors assessed the heterogeneous impact of the EEs across different groups and derived implications for each.

While this study is comprehensive, it focuses exclusively on formal digital entrepreneurship within a single developing country, namely, Iran. Although the authors explored various EEs, they did not address all possible types, highlighting the potential for further research in other entrepreneurial sectors for a comparative analysis of EEs.

The initial findings affirm that most NVC in Iran stems from external factors. These findings have been widely used by policymakers to define the goals and objectives of EEs concerning their nature and impact. This research reveals that EEs have heterogeneous effects on NVC, varying across different characteristics such as gender, geography and industry sectors. The results of this study suggest that governments should reassess the role of “macro” policies to support vulnerable groups. EEs disproportionately benefit male founders, which exacerbates existing gender gaps and necessitates ongoing attention and intervention. Additionally, EEs have diverse impacts across industries, potentially favoring some while disadvantaging others because of varying social, cultural and economic factors.

NVC, central to entrepreneurship in developing nations, encounters numerous challenges, yet the role of EEs remains underexplored in this context. EEs are opportunities that are not limited to a subset of entrepreneurial actors; they act as levers influencing a wide range of entrepreneurial activities. This research distinctively investigates the empirical impact of EEs in a Middle Eastern developing country context, contributing to the growing literature on the external enablers framework.

Arab Americans experience higher rates of mental illness and lower rates of treatment compared to the general population. While some factors leading to this disparity have been proposed, data in the literature remains lacking. This study aims to explore the mental health perspectives of Arab Americans living in Southeastern Michigan regarding barriers to mental health treatment, treatment resource preferences and telemental health preferences.

Responses were collected through an anonymous bilingual Qualtrics survey from both Arab and non-Arab participants (n = 294, ages 18+) in Southeastern Michigan. Participants’ perceptions regarding mental health barriers, resource preferences and telemental health were assessed. Comparisons were performed between Arabs and non-Arabs, and among Arabs.

Compared to non-Arabs, Arabs were less likely to seek mental health treatment from a therapist or a primary care provider and were more likely to not seek treatment. Arabs were more likely than non-Arabs to list fear of being called crazy by others and cultural barriers. With regards to preferences on telemental health utilization, no significant differences between Arab and non-Arab participants were found.

The results provide insights into Arab-American mental health perspectives, especially pertaining to mental health barriers, resources and telemental health preferences, adding a novel perspective on the mental health preferences of Arab-American subgroups, especially regarding gender and age differences. This study highlights the Arab-American population as a nuanced and diverse group, emphasizing the importance of future studies to show a more complete picture of Arab-American mental health.

Manufacturing capability is a crucial component of every nation’s economy and pharmaceuticals are frequently a significant part of the manufacturing sector. Pharmaceutical supply chains are essential to health-care systems, contributing to living quality and shorter hospital stays. This study aims to examine the role of multiple integrations on business performance (BP) through supply chain flexibility (SCF) and supply chain agility (SCA).

Data was collected from 198 supply chain professionals in the pharmaceutical sector of the developing economy of Pakistan. The sample was collected based on a nonprobability purposive sampling approach. A five-point Likert-scale survey was used and analyzed with the PLS-SEM technique using SmartPLS 4.

This study found that process integration (PI) does not affect SCA, whereas relationship integration and measurement integration positively affect SCA. SCA positively impacts BP. In contrast, all integrations significantly influenced supply flexibility and BP except for PI. Finally, SCF significantly mediates the relationship between all integrations and BP.

This study examined the relationships of multiple integrations on BP, directly and indirectly, through SCF and agility. The theory of dynamic capabilities has been applied and extended to increase the comprehensiveness of the findings. A developing economy’s pharmaceutical industry supply chain was examined, producing empirical evidence of the results.

Drawing on the personalization–privacy paradox and guided by means–end analysis, this study explores how consumers balance their concerns for privacy and the benefits of smart home device personalization and the role that trust plays in the process. More specifically, this study aims to investigate how perceptions of smart device personalization and privacy concerns are shaped by consumers’ experiences and the role of trust in the deliberation process.

In-depth interviews were conducted across diverse demographic groups of smart device users to shed light on the balancing act between personalization and privacy.

The study found that product experience, ownership type, perceived value of convenience and control and quality of life via “smart things” are key motivators for product usage. The benefits of tailored recommendations and high relevance are balanced against the risks of echo chamber effects and loss of control. The results also show the role of active involvement in the privacy calculus and trust level. The study points to the significance of an ecosystem-based service/business model in gaining consumer confidence when they balance between personalization and privacy.

Although many studies have explored trust, privacy concerns and personalization in an artificial intelligence (AI)-related context, few have addressed trust in the context of both smart devices and the personalization–privacy paradox. As such, this study adds to the existing literature by incorporating the concept of trust and addressing both privacy concerns and personalization in the AI context.

This paper aims to investigate the extent of personal privacy concerns expressed by university students in Vietnam while using TikTok, the influence of peer interactions and social norms on privacy attitudes and behaviors and the strategies used by university students in Vietnam to mitigate privacy risks on TikTok.

A qualitative approach using semi-structured interviews was used to gather data on the following: the degree to which Vietnamese university students express concerns about their personal privacy while using TikTok; how peer interactions and social norms influence privacy attitudes and behaviors; and the strategies these students use to mitigate privacy risks on the platform.

Findings indicate that although university students exhibit a relatively low level of concern regarding their personal privacy on TikTok, peer influences and societal norms significantly shape their attitudes and behaviors on the platform. Moreover, students use several strategies to mitigate privacy risks, such as selective content sharing and refraining from engaging with unknown links.

These insights provide valuable implications for the development of targeted interventions and educational initiatives aimed at fostering a more privacy-conscious TikTok user community among university students in Vietnam.

This research fills a critical gap in the existing literature by focusing on the influence of social norms and peer influences on privacy attitudes and behaviors on social media platforms. While prior studies have explored various factors impacting privacy concerns on social media, the role of social dynamics has been understudied. Moreover, the research specifically addresses the lack of investigation into privacy concerns on TikTok, a platform rapidly gaining popularity among younger demographics.

The proliferation of lean principles in the construction industry is offset by the enduring uncertainty among industry stakeholders regarding their respective roles in lean implementation. This uncertainty is further compounded by the scarcity of empirical investigations in this area. Consequently, this study undertakes the task of bridging this knowledge gap by identifying the critical roles of lean learners and their indispensable contributions to achieving successful lean implementation.

A qualitative exploratory approach informed by an interpretivism perspective was adopted. The case study strategy was employed to gather data from three contracting organisations that had implemented lean practices. Empirical data was collected through in-depth semi-structured interviews with fifteen industry experts and complemented by document reviews. To analyse the data, a code-based content analysis approach was employed using NVivo software, while Power BI software was utilised to develop a comprehensive force-directed graph visualisation.

The research findings substantiated nine lean learners and unveiled a set of seventy-three roles associated with them. The force-directed graph facilitated the identification of lean learners and their connections to the emerged roles. Notably, the graph highlighted the pivotal role played by project managers and internal lean trainers in ensuring the success of lean implementation, surpassing the contributions of other lean learners.

The implications of findings extend to industry professionals seeking to establish a robust lean learning framework to expedite lean implementation within the construction sector. This study not only provides a comprehensive definition of lean learners’ roles but also transcends specific construction types, making it a significant catalyst for global impact.

This study aims to tackle control challenges in soft robots by proposing a visually-guided reinforcement learning approach. Precise tip trajectory tracking is achieved for a soft arm manipulator.

A closed-loop control strategy uses deep learning-powered perception and model-free reinforcement learning. Visual feedback detects the arm’s tip while efficient policy search is conducted via interactive sample collection.

Physical experiments demonstrate a soft arm successfully transporting objects by learning coordinated actuation policies guided by visual observations, without analytical models.

Constraints potentially include simulator gaps and dynamical variations. Future work will focus on enhancing adaptation capabilities.

By eliminating assumptions on precise analytical models or instrumentation requirements, the proposed data-driven framework offers a practical solution for real-world control challenges in soft systems.

This research provides an effective methodology integrating robust machine perception and learning for intelligent autonomous control of soft robots with complex morphologies.

The purpose of this study is to forecast the mechanical properties of ternary blended concrete (TBC) modified with oyster shell powder (OSP) and shea nutshell ash (SNA) using deep neural network (DNN) models.

DNN models with three hidden layers, each layer containing 5–30 nodes, were used to predict the target variables (compressive strength [CS], flexural strength [FS] and split tensile strength [STS]) for the eight input variables of concrete classes 25 and 30 MPa. The concrete samples were cured for 3–120 days. Levenberg−Marquardt's backpropagation learning technique trained the networks, and the model's precision was confirmed using the experimental data set.

The DNN model with a 25-node structure yielded a strong relation for training, validating and testing the input and output variables with the lowest mean squared error (MSE) and the highest correlation coefficient (R) values of 0.0099 and 99.91% for CS and 0.010 and 98.42% for FS compared to other architectures. However, the DNN model with a 20-node architecture yielded a strong correlation for STS, with the lowest MSE and the highest R values of 0.013 and 97.26%. Strong relationships were found between the developed models and raw experimental data sets, with R² values of 99.58%, 97.85% and 97.58% for CS, FS and STS, respectively.

To the best of the authors’ knowledge, this novel research establishes the prospects of replacing SNA and OSP with Portland limestone cement (PLC) to produce TBC. In addition, predicting the CS, FS and STS of TBC modified with OSP and SNA using DNN models is original, optimizing the time, cost and quality of concrete.