The rise of digital service platforms has accelerated the adoption of invisible queue systems, yet eliminating physical lines does not inherently improve consumer experience. Grounded in the Elaboration Likelihood Model, this study uncovers a “visibility paradox.” While physical queue visibility raises perceived goal achievement difficulty, it simultaneously elevates expected value and stimulates customer-to-customer interaction, which strongly predicts service trust. Structural equation modeling of data from 320 participants confirms these dual mechanisms. Generational analysis further reveals that Millennial/Gen Z consumers exhibit a stronger visibility–social interaction relationship than older cohorts, suggesting physical waiting serves as consumption authentication rather than mere operational delay. These findings reconceptualize queue management as a strategic communication tool, cautioning that the wholesale removal of physical queues eliminates socially embedded signals consumers use to validate service quality.
Companies increasingly adopt service-oriented strategies to enhance competitiveness and growth through service excellence. However, implementation remains inconsistent due to divergent definitions and limited integration with service management frameworks. Service excellence is variously equated with total quality management, employee responsiveness, cultural competence, or customer experience, creating conceptual ambiguity that hinders practical application. The service triangle, which highlights the interrelationships among organizations, employees, and customers, has received limited attention in this context. This study synthesized fragmented definitions of service excellence to identify shared elements and proposed a unifying definition. It also examined the service triangle as a strategic framework for implementation by clarifying the roles of managers, frontline staff, and customers. By linking service excellence to the service triangle, the study offered directions for future research and a cohesive model for translating excellence into coordinated, actionable practice.
This study examined innovation, creativity, engagement, and learning effectiveness in engineering education using a global sample. Longitudinal data from 287 students (>70 nationalities) were collected over two semesters in a 16-week project-based course that required creative, real-world solutions. Quality function deployment successfully aligned the curriculum to student needs and identified opportunities for innovation. In addition, creativity drove original problem-solving. Engagement included behavioral, emotional, and cognitive aspects. Learning effectiveness is measured by knowledge gain through instruction, motivation, and engagement. The results indicated that innovation had a significant positive effect on creativity (β = 0.65, p < 0.001). Creativity also mediated the relationship between innovation and engagement (β = 0.31, p < 0.01), with a significant indirect effect (indirect effect = 0.288, p < 0.01). In addition, innovation was positively correlated with engagement (r = 0.45, p < 0.05), while creativity was positively correlated with effectiveness (r = 0.50, p < 0.01). Furthermore, creativity had a significant positive effect on effectiveness through engagement (β = 0.60, p < 0.001). Finally, quality function deployment moderated the relationship between creativity and engagement (β = 0.42, p < 0.05).
The widespread use of artificial intelligence (AI) has been applied in many areas of application, including software engineering. In this practice-oriented study, AIware is adopted to build a traditional human-directed software model to eliminate manual software engineering development effort. The AI tools simplify and facilitate straightforward development processes, ranging from requirements specification, design, and testing. However, this paper incorporates human intervention to fine tune some important decisions during the development process because the AI tools may produce incomplete results. The effort spent on requirements creation by the proposed method is slightly reduced as the software size becomes larger. Future work is to enhance effective testing methods using innovative AI tools on a larger software development scale.
This study examines how corporate cash holdings affect investment efficiency in Saudi-listed firms and whether environmental, social, and governance (ESG) performance changes this relationship. Using panel data from non-financial firms listed on the Saudi Stock Exchange (Tadawul) between 2021 and 2024, the study applies random-effects regression. Investment efficiency is measured following established models, and additional tests are conducted using subsample analyses and an alternative composite measure. The results show that higher cash holdings are linked to greater investment inefficiency, suggesting that excess liquidity may encourage suboptimal investment decisions. However, strong ESG performance weakens this negative effect by improving transparency and accountability, which limits inefficient use of cash. The findings support the relevance of agency and legitimacy theories in the Saudi context and highlight the importance of ESG practices in improving investment outcomes. The study suggests that managers should integrate ESG into long-term strategies, while policymakers should enhance ESG disclosure and incentives to promote more efficient capital allocation.
While literature focuses on the domestic consequences of EPU, its international propagation through trade linkages remains underexplored, particularly for major oil-exporting regions. Using a local projection framework and monthly data from 2002 to 2025, this study estimates country-specific impulse response functions for bilateral exports and imports while controlling for global oil price dynamics. The empirical results reveal that shocks to Russian EPU generate contractions in bilateral trade, materializing within months and persisting over the short-to-medium run before dissipating. A key finding is the asymmetry in responses is that GCC exports to Russia exhibit larger and more persistent declines than imports from Russia, suggesting a dominant demand-side transmission channel. Furthermore, results highlight pronounced heterogeneity, with smaller, less diversified GCC economies experiencing more severe trade disruptions than larger counterparts.
Researchers have explored various factors that shape the ethical decision-making process. Among these, personal values (PVs) and cultural values (CVs) emerge as essential determinants, serving as critical guidelines for ethical behavior. The aim of this study was to explore how PVs and CVs influence managers’ decision-making, with a focus on the mediating role of ethical intention. In order to collect data from business managers in Syria, this study used a self-administrated questionnaire approach. This research employed a convenience sampling method to gather data, followed by an analysis using SmartPLS 4. The results showed that both CVs and PVs have a positive impact on ethical decision-making. Key PVs, such as integrity, honesty, and fairness, significantly shape how business managers make decisions that affect both their companies and stakeholders. In Syria, business ethics are deeply influenced by cultural and religious norms, which guide managers in navigating ethical dilemmas and decision-making processes.
This study investigates the moderating role of board gender diversity in the relationship between tax avoidance and corporate cash holdings in the Kingdom of Saudi Arabia (KSA), an emerging market in the Gulf Cooperation Council region. The analysis is based on a panel dataset of nonfinancial firms listed in KSA over the period 2020–2023. Fixed-effects regression models are employed to examine the proposed relationships, and robustness checks are conducted using alternative measures of cash holdings and lagged independent variables to mitigate endogeneity concerns. The results reveal a significant positive association between tax avoidance and corporate cash holdings, indicating that firms engaging in greater tax avoidance strategies tend to accumulate higher cash balances. Furthermore, board gender diversity is found to strengthen this relationship, suggesting that gender-diverse boards play an active role in influencing financial decision-making related to tax-driven cash retention.
Mobile software development turns around quickly, which induces inadequate testing to meet this short-lived pace of development and release process. The traditional testing procedures just do not fit such a rapid mobile paradigm. This study sets out to exploit two straightforward techniques, namely equivalence partitioning and control-limit techniques, based on software cost and quality. The first technique divides input domain into proper partitions that suit the software functions and selects cost representative data from each partition for testing. The second technique keeps the outcomes within the limits to ensure acceptable output. The contributions of these techniques prove to be reliable steppingstones for mobile software development without losing sight of the founding methodologies. The study envisions that future work should incorporate well-established techniques for developers to set up rapid test-and-run procedures, whereby quality mobile software can be developed from this fast paced and short-lived cycle.
Personality has a long-standing influence on fast-food impulsive buying behavior. It would not be an exaggeration to suggest that it can draw young consumers into a difficult and often unhealthy purchasing cycle. Among the internal factors influencing young consumers, personality stands out as a key determinant in driving fast-food purchases. Beyond personality traits, other consumer-related aspects also challenge the decision-making process of young individuals, highlighting the combined impact of both extrinsic and intrinsic factors on fast-food purchasing decisions. In this study, two personality variables—extroversion and openness—were examined for their role in shaping fast-food impulsive buying behavior. Additionally, the mediating effects of three factors—emotional conflict, positive and unplanned buying, and retail attributes—were analyzed to understand how they influence the relationship between personality traits and impulsive fast-food purchases. The Hayes mediation method was employed to assess the direct, indirect, and total effects. The findings of this study are largely confirmatory, although certain variables presented results that contrast with existing literature. Overall, all three mediators were found to play a significant role in determining the association between personality traits and fast-food impulsive buying behavior.
Logistics service providers (LSPs) face fierce competition despite increasing demand for third-party logistics (3PL). LSPs must tackle increasing costs and complexity, labor shortages, and scarce warehouse space while meeting individual customers' needs. Studies suggest modularity could provide LSPs a competitive edge, but few methods for modeling logistics service modules exist. Although there are numerous product design approaches, these are seldom applied to services. This study applies insights from product design literature to develop a top-down approach for modeling and modularizing warehouse services. To test the proposed approach, three case studies across seven warehouses were conducted at a world-leading LSP. The study shows the approach can identify and define warehouse service modules using the warehouse service variant master (WSVM) technique, which clarifies the variety of warehouse services in three domains: client, service, and resource. The study also suggests LSPs can reduce complexity by offering warehouse services from standardized service modules.
Bad medical debt negatively impacts individuals' physical and mental well-being, can discourage future care-seeking, and is increasingly viewed as a social determinant of health. Addressing this issue by linking the healthcare sector with humanitarian efforts presents a significant global challenge, requiring innovative solutions. This paper aims to synthesize existing literature on the topic. Using the Scopus database, a systematic review of literature was conducted from 1990 to 2024, employing the PRISMA framework. Thematic analysis was applied to organize and interpret the findings. Reviewing 958 papers, including 318 key sources, revealed a pressing need for a SMART blockchain healthcare platform. Such a platform would securely connect beneficiaries with philanthropists, facilitate data sharing, and align donor requirements with beneficiary criteria. Employing decentralized autonomous organization frameworks and smart contracts ensures transparency, efficiency, and accountability through automated and secure processes.
A new era of connectivity has begun with the introduction of the Internet of Things (IoT), which has altered how we perceive and interact with technology. Concerns regarding data security have been significantly heightened as IoT ecosystems continue to grow and generate an unprecedented volume of data from interconnected devices. It is of the utmost importance to protect sensitive data within these extensive networks to guarantee the dependability and credibility of IoT applications. This article investigates the quantification of IoT data security metrics in literature. This paper evaluates current metrics, including their merits and practicality, while also considering emerging trends and advancements that will shape the trajectory of IoT security metrics in the future.
The rapid growth of dark data in organisations presents both opportunities and challenges. While dark data contains hidden insights that could improve decision-making, it also leads to compliance, security, and storage risks. This study explores the sources of dark data, its challenges to organisations, and strategies for mitigation of its risks. The findings reveal that legacy systems, unstructured data, and governance gaps are major contributors to dark data accumulation. The study highlights artificial intelligence-driven solutions, role-based access controls, and improved data literacy as effective strategies for addressing dark data challenges. Organisations can enhance data visibility, reduce redundant storage, and improve overall data management by implementing structured governance frameworks and leveraging automation. The study offers propositions that align with organisational implications and outline a roadmap for better utilisation of dark data.
This article aims to answer the question of how artificial intelligence (AI)-based chatbots in customer service can be perceived more efficiently and effectively in terms of interaction with customers using ChatGPT. Based on a literature review and analysis of 115 relevant publications, a research model was developed to answer this question. The research study is based on the task-technology fit (TTF). The evaluation of the research model was based on an online survey with 202 study participants. Our results show that the TTF of ChatGPT has a significant influence on the efficiency and effectiveness of AI-based chatbots in customer service. In terms of efficiency, key factors include an intuitive and user-friendly interface, or accurate understanding of customer queries without the need for repetition. Regarding effectiveness, the study finds that unambiguous and clear responses, support for complex customer issues, and ensuring the security of customer data are all essential.
Traditional citation analyses often fail to capture how research reshapes intellectual landscapes. This study applies Structural Variation Analysis (SVA) to assess the interdisciplinary impact of a highly cited paper on the Cross-Entropy Method in Operations Research. Using co-citation network analysis, the authors examine structural shifts through key metrics, including modularity change (∆M = 52.57), cluster linkage (CL = 135.2), centrality divergence (CKL = 0.47), and entropy (E = 0.98). The findings reveal that this methodological paper plays a pivotal role in bridging previously unconnected research domains. Beyond accumulating citations, the Cross-Entropy Method has fundamentally altered research connectivity. SVA offers an early indicator of transformative research before conventional citation metrics can capture their full impact.
