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Understanding the multifaceted images of tourism destinations is critical for effective destination marketing and management strategies. Traditional approaches, including conceptualization of destination images or analysis of their antecedents and consequences, are commonly used. This study aims to advocate the inclusion of visitors’ latent profiles based on cognitive images to enrich the evaluation and formulation of destination marketing and management strategies.

The analysis focuses on Serbia, an emerging destination, that attracts an increasing number of first-time, repeat and prospective visitors. Exploratory factor analysis and confirmatory factor analysis were used to test the potential dimensions (tangible and intangible cultural destination; infrastructural and accessible destination; active, nature and family destination; sensory and hospitable destination; and welcoming, value for money (VFM) and safe destination) of the cognitive destination image factors scale while subtypes (profiles) were obtained using latent profile analysis (LPA).

The cognitive image component encompasses the perceived attributes of a destination, whether derived from direct experience or acquired through other means. The study identified the following profiles: conventional destination; sensory and hospitable destination; welcoming, VFM and safe destination; secure and active family destination and accessible cultural destination, which are presented individually with their sociodemographic assets.

The main contribution of the paper is the application of a novel method (LPA) for profiling visitor segments based on cognitive destination image. From a theoretical perspective, this research contributes to the extant body of literature pertaining to the destination image, thereby facilitating the identification of discrete latent visitor segments and elucidating noteworthy differences among them concerning a cognitive image.

Home-based commercial hospitality (HBCH) is the focus of this study. This community-based tourism (CBT), which has received little research attention, is examined to reveal the meaning of commercially hosting visitors in private homes for experiential meetings on a variety of topics such as food, art, culture, folklore and various workshops.

A qualitative research method was adopted, using semi-structured, in-depth interviews with HBCH providers in the desert town of Arad, located in southern Israel.

The study reveals the impact of this unusual occupation on the host's quality of life, the factors that encourage and suppress involvement in this entrepreneurship, as well as the positive and negative consequences of HBCH on the local environment.

The findings could offer important guidelines to municipalities and local governments seeking to encourage CBT and sustainable micro-enterprises.

HBCH is a recent phenomenon and, as such, has been little researched. This study of one community raises issues that may be shared by HBCH enterprises. The findings could contribute to developing such initiatives elsewhere, avoiding the obstacles faced in this pioneering effort.

The purpose of this research is to develop empirically grounded propositions for further research into UK manufacturing productivity.

Interviews were conducted with managers from strategic, tactical and operational levels from four manufacturing sectors to produce case studies. A modified strategic alignment theory framework was used to code, compare and contrast narratives on perceived productivity antecedents, definitions, compatibility with the definition from the UK Office for National Statistics, and vertical alignment issues within and across cases.

It was found that different key antecedents can facilitate and/or prevent strategic vertical alignment. Discussion reveals complex nuances in perceptions of manufacturing productivity and using the modified strategic alignment theory/productivity antecedent framework.

In revealing the alignment or otherwise of productivity definitions at different levels within the firm, the paper reveals nine propositions for future research including definitions, skills, metrics, performance measurement systems, people and system-centric perspectives, the value-added perspective of productivity and the role of innovation.

Gig workers form the backbone of any crowdsourcing platform where they showcase their talent and choose a job of their choice and freedom. The study explores the role of information quality (IQ) and social-mediated dialogue (SMD) in evaluating gig worker engagement and productivity on crowdsourcing platforms. The authors also propose to understand how gig worker productivity could be improved under the moderating effect of game elements.

A conceptual model was developed and empirically tested by integrating media richness theory and dialogic public relation theory. Data were collected from gig workers that are involved in crowdsourcing activities for the past three years. An overall sample of 346 gig workers contributing to at least one of the crowdsourcing platforms was collected. The authors tested the hypotheses using Warp PLS 7.0. Warp PLS 7.0 uses partial least square (PLS) structured equation modeling (SEM) and has been used widely to test path analytical models.

Results reveal that the information quality plays an essential role in the SMD, thereby fostering gig workers' productivity and engagement, which could be improved in the presence of game elements due to their nature of supporting rewards. However, engagement in the platform leading to improved productivity was not supported.

The study lays practical foundations for crowdsourcing platforms as it sets the importance of both IQ and dialogic communication channels. The two-way communication between gig workers and the platforms via accurate, timely, valuable and reliable information forms the key to the task's success. The introduction of the right game element will help to achieve better engagement and productivity.

This study also offers a new dimension to media richness theory and dialogic public relation theory in crowdsourcing platforms. The results would help platform designers and gig employers understand gig workers' quality and performance in a platform economy. The study uniquely positions itself in the area of crowdsourcing platforms by using game elements.

Evaluating project success within the construction industry presents challenges due to the unique characteristics of the sector, the complexity of projects, and the involvement of diverse stakeholders. Conducting a bibliometric analysis, this paper aims to unravel the major research themes and methodologies utilised by researchers in studying the critical success criteria for construction projects, as well as extracting these success criteria.

The researchers systematically searched and screened 95 papers from Scopus and Web of Science (WoS) databases. This study conducted research focus parallelship network (RFPN) analysis and keywords co-occurrence network (KCON) analysis using BibExcel and Gephi to cluster the papers, illuminate the relationships among keywords within each cluster, and identify the primary research directions.

Using the RFPN analysis, this study classified the papers into three distinct clusters: infrastructure and public projects success, risk and knowledge management, and contractors and procurement management. Statistical techniques such as structural equation modelling (SEM) and multi-criteria decision-making methods such as analytic hierarchy process (AHP) have been used to analyse project success in the construction industry.

Considering the intensified demand for streamlined digital interactions and the increasing emphasis on sustainability and safety performance, construction companies are recommended to allocate greater investments toward the automation and digitisation of their products and processes. Prioritising modular construction and embracing transformative technologies alongside data science is crucial for enabling well-informed decision-making, and enhancing project success.

This study contributes to the existing body of knowledge by conducting a quantitative and systematic evaluation of the literature on project success criteria in the construction industry and uncovering key research areas. It addresses the pressing need to understand the complexities of construction projects amidst evolving industry dynamics and emerging disruptions. Moreover, by highlighting the implications of digital innovations and modular construction, this study urges deeper exploration into their impact on project performance and stakeholder satisfaction. This research sets a comprehensive framework for investigating the interplay between project complexity, technological advancements, and sustainable practices in the construction sector, paving the way for strategic advancements in the field.

This study aims to investigate patterns of information use among undergraduate engineers as they progress through their academic programs. The primary objective was to discern how second and fourth-year students differ in their use of different types of information while performing specific tasks, namely, conducting labs, composing reports and undertaking projects.

The research used an online questionnaire to collect data, focusing on the comparative analysis of second and fourth-year engineering students’ information use. The analytical framework comprised a chi-square test, residual analysis and exploratory data analysis, for evaluating statistical significance and identifying trends over time.

The results demonstrated a statistically significant difference in information use between second and fourth year undergraduates. Notably, fourth year students exhibited a preference for disciplinary genres, such as journal articles, patents and technical reports. This coincided with a decline in fourth year students’ use of educational genres, including textbooks and instructors’ handouts, notes and slides. These shifts in information use were observed consistently across all three tasks.

The uniqueness of the study resides in its innovative approach to exploring information use by investigating the relationship between genres and tasks over the course of students’ academic programs. The research introduces a novel approach for visualizing changes in information use. By describing the evolving preferences of undergraduate students from novice to emerging professional, this study contributes valuable insights into the nuanced ways in which information is used throughout the levels of engineering education.

Capable service employees are increasingly scarce and costly. Many organizations opt to partially replace, support or augment human employees with AI systems. This study builds a framework to help managers map and understand the challenges of crafting a service climate that fosters synergies between AI and human employees, where customers require value-added, personalized and excellent service.

This conceptual article identifies barriers and facilitators of building a service climate for organizations using both human and AI-based employees through an eclectic review of relevant literature.

A conceptual framework is built, and a future research agenda is brought forth.

By identifying barriers and facilitators for AI–human synergies in service settings, this article clarifies how AI can be made to complement human employees, especially in delivering personalized, value-added services, while also highlighting knowledge gaps.

This study provides a practical framework for integrating AI into the workforce. It offers insights into addressing challenges in creating a service climate that combines human and AI capabilities to maintain service excellence. Identifying key barriers and facilitators, the framework guides managers to improve efficiency and customer satisfaction in a rapidly changing service landscape.

This research offers insights on incorporating AI to address labor shortages while maintaining high-quality, personalized service. It provides a pathway to improving service experiences, especially in sectors facing staffing challenges from an aging population.

This research builds on Bowen and Schneider’s (2014) seminal service climate framework to account for a mix of human and AI-based employees.

This study aims to examine the influence of digital financial literacy (DFL) on the financial well-being (FWB) of students in Andhra Pradesh, specifically exploring the factors of impulsivity and self-control. Both DFL and FWB are treated as multi-dimensional constructs in the study. The research delves into the impact of DFL dimensions, viz. digital financial knowledge, digital financial experience and digital financial skills, on both impulsivity and self-control. Subsequently, the study assesses the effects of impulsivity and self-control on financial well-being.

To gather data, a questionnaire-based survey method was employed, reaching 475 university students through purposive sampling. The study utilizes confirmatory factor analysis for scale validation and structural equation modeling for hypothesis testing.

The results reveal a significantly negative influence of digital financial knowledge (DFK), digital financial experience (DFE) and digital financial skills (DFS) on impulsivity, while demonstrating a significantly positive impact on self-control. Additionally, the study finds that impulsivity negatively affects financial well-being, whereas self-control has a positive impact. Focusing on higher education institutions in Andhra Pradesh, the research highlights students’ limited concern for long-term financial planning.

This study underscores the relevance of understanding the crucial role of digital financial literacy in enhancing their financial well-being. The implications of these research findings are substantial and can be utilized to shape educational programs for students in higher education institutions. Such programs can guide institutions in imparting knowledge and skills related to personal finance management, particularly in the context of the increasing digitalization of financial transactions.

The purpose of this study is to propose an analytical framework for generating main path analysis (MPA) and demonstrate the process involved in identifying, analyzing the MPA on a citation network and empirically testing in the research field chromosome anomalies (CA).

The proposed methodological structure involves five phases of the process. Search path method is used to measure the weights of each citation link from a source vertex to a sink vertex. The key route local main path and global main path are generated to identify the knowledge diffusion trajectories and validated by cross-referencing with existing literature, co-citation analysis and centrality measures of social network analysis.

The empirical validation of this framework within CA research demonstrates its potential for tracing knowledge diffusion and technological development trajectories over three decades. This approach elucidates two major intellectual knowledge flows. The first key-route main path identified the primary diagnostic protocols. The second key-route main path revealed that cancer or carcinogenesis is identified as one of the mainstream of CA.

The limitations of the data and coverage period restrict the scope of this study. MPA was applied exclusively to the most influential sub network and disregarded other sub networks. MPA identified the seminal papers that provided a historical development in diagnostic protocol and their interconnectedness of disorders and diseases. This helps the researchers to develop targeted therapies and interventions, especially in cancer treatment.

Exploiting MPA on CA research provides valuable insights to stakeholders in developing evidence-based public health policies. This is crucial for preventing the birth of children with birth defects or genetic diseases, promoting public health and reducing the socioeconomic burden on a country through enhanced surveillance and prevention efforts.

The study suggests that in addition to traditional scientometrics measures, MPA can be used to trace the evolution of knowledge and technological advancements. It also highlights the role of social network analysis measures in extracting main paths.

Biodiesel in engines can reduce net carbon dioxide emissions and boost renewable energy. Despite the benefits of biodiesel engines, little is known about their effects on fuel filters. Filterability hinders the broad use of sustainable biodiesel, as filter clogging and deterioration can lead to engine damage and further hinder the widespread adoption of biodiesel. This study aims to investigate algae biodiesel (Chlorophyta) and diesel fuel filtration and filter deterioration to fill this gap.

This study investigates the effects of different biodiesel blends on diesel fuel filter parameters, namely, filter blocking tendency (FBT), tensile strength of filter medium upon immersion and other physiochemical properties. In total, 20% biodiesel and 80% diesel (B20) was chosen for its common use as a commercial blend, 40% biodiesel and 60% diesel (B40) for mid-level biodiesel content and 100% biodiesel (B100) for pure biodiesel testing. Testing these concentrations allowed us to determine the effect of increasing biodiesel content. B20 biodiesel emerges as the most suitable blend, providing the best balance of performance and durability with a low FBT (1.0) and a 6.9% increase in tensile strength over diesel. B40 and B100 had higher FBTs of 1.53 and 7.57, respectively, and lower tensile strength, resulting in increased filter clogging and material deterioration. SEM results demonstrated that B20-immersed filters had little structural changes as compared to B40 and B100; the colour darkened noticeably suggesting deposits, including sterol glucosides, indicating material deterioration and clogging.

The results from current study concluded that when compared to B40 and B100, the B20 biodiesel blend provides the best balance of performance and longevity, with less filter blockage, improved tensile strength and lower maintenance requirements. However, its performance in harsh settings, such as colder climes, high-pressure systems and engines requiring more power output, may require augmentation and more study. While higher blends may be more appropriate in some applications, B20 remains the most adaptable solution for a wide range of general operational situations.

The study concludes that the B20 biodiesel blend provides optimal performance, longevity and maintenance for compression ignition engines, exceeding other blends. While B40 and B100 may be appropriate in certain situations, B20 remains the most practical and versatile option, combining environmental benefits with engine compatibility, making it a superior alternative to standard diesel fuel.