Search results

This study aims to explore the role of social media visual posts (known as foodstagramming) on restaurant visit intention. Drawing on the heuristic–systematic model and normative focus theory, this research introduces a framework that assesses the effects of key foodstagramming attributes – vicarious expression, aesthetic appeal and post popularity – and the mediating roles of goal relevance and mimicking desire, in the process.

Structural equation modelling was performed to test the proposed model using a sample of tourists (n = 377) and residents (n = 341). Multi-group analysis was performed to compare the differences between these groups.

Results reveal that mimicking desire and goal relevance influence restaurant visit intention; however, mimicking desire has a stronger influence than goal relevance. Little difference was found between the tourist and the resident groups in the proposed relationships, except that vicarious expression positively influences mimicking desire in the tourist group but not in the resident group.

This study guides restauranteurs and social media influencers (foodstagrammers). It shows that consumers value the textual content and aesthetic appeal of photos over the popularity of a post. It also indicates that vicarious expression is more important for tourists than for residents.

This research advances social media marketing literature by proposing a new information processing framework. To the best of the authors’ knowledge, this study is one of the first studies to explore the impact of visual post attributes on individual decision-making behaviours through socially acceptable norms.

According to the market competition theory, a firm’s decision-making is influenced by the behaviors or strategies of its competitors. The repercussions of competition include market-stealing and spillover effects. Relatively few studies in the reinsurance literature discuss the effect of competitors on an insurer’s decision-making. This study aims to fill a gap in the reinsurance literature by comparing insurers' reinsurance demand to their competitors' reinsurance purchases.

This study uses unbalanced panel data for the US property-liability insurance industry from 2006 to 2017 to determine the impact of competitors' reinsurance purchases on insurers' reinsurance demand. This study employs the Mixed Effect Model and the Quantile Regression to test the proposed hypotheses.

The evidence suggests that the affiliated reinsurance purchases of competitors have a positive and substantial effect on the affiliated reinsurance demand of insurers, crediting mimicking the reinsurance strategy. Interestingly, the market-stealing effect is supported while the non-affiliated reinsurance metric is used. Remarkably, given insurers with low non-affiliated reinsurance purchases, the finding sustains the mimicking reinsurance strategy. Nevertheless, the market-stealing effect remains a concern for insurers with a high non-affiliated reinsurance purchase.

The new findings concerning competitor effects analysis fill a void in the reinsurance literature. Risk diversification, capital substitution, and real services demand may play a crucial role in determining the market-stealing effect, leading to a decrease in market share. Insurers can mitigate the market-stealing effect of competitors by accessing expertise and capital substitution through non-affiliated reinsurance purchases.

This study aims to analyze a conspicuous corpus of literature related to the field of technology-based intensive care research and to develop an architecture model of the future smart intensive care unit (ICU).

Papers related to the topics of electronic health record (EHR), big data, data flow and clinical decision support in ICUs were investigated. These concepts have been analyzed in combination with secondary use of data, prediction models, data standardization and interoperability challenges. Based on the findings, an architecture model evaluated using MIMIC III is proposed.

Research identified issues regarding implementation of systems, data sources, interoperability, management of big data and free text produced in ICUs and lack of accuracy of prediction models. ICU should be treated as part of a greater system, able to intercommunicate with other entities.

The research examines the current needs of ICUs in interoperability and data management. As environment changes dynamically, continuous assessment and evaluation of the model with other ICU databases is required.

The proposed model improves ICUs interoperability in national health system, ICU staff intercommunication, remote access and decision support. Its modular approach ensures that ICUs can have their own particularities and specialisms while ICU functions provide ongoing expertise and training to upgrade its staff.

This study aims to explore Augmented Language Models (ALMs) for synthetic data generation in services marketing and research. It evaluates ALMs' potential in mirroring human responses and behaviors in service scenarios through comparative analysis with five empirical studies.

The study uses ALM-based agents to conduct a comparative analysis, leveraging SurveyLM (Bickley et al., 2023) to generate synthetic responses to the scenario-based experiment in Söderlund and Oikarinen (2018) and four more recent studies from the Journal of Services Marketing. The main focus was to assess the alignment of ALM responses with original study manipulations and hypotheses.

Overall, our comparative analysis reveals both strengths and limitations of using synthetic agents to mimic human-based participants in services research. Specifically, the model struggled with scenarios requiring high levels of visual context, such as those involving images or physical settings, as in the Dootson et al. (2023) and Srivastava et al. (2022) studies. Conversely, studies like Tariq et al. (2023) showed better alignment, highlighting the model's effectiveness in more textually driven scenarios.

To the best of the authors’ knowledge, this research is among the first to systematically use ALMs in services marketing, providing new methods and insights for using synthetic data in service research. It underscores the challenges and potential of interpreting ALM versus human responses, marking a significant step in exploring AI capabilities in empirical research.

Energy poverty is a global phenomenon, but its prevalence is enormous in most African countries, with a potential impact on quality of life. This study aims to investigate the impact of energy poverty on the shadow economy.

The study uses panel data from 45 countries in Africa over a period of 1996–2018. Using panel cointegrating regression and panel vector auto-regression model in the generalized method of moments technique.

This study provides that energy poverty deepens the size of the shadow economy in Africa. It also documents that there is a bidirectional causality between shadow economy and energy poverty. Therefore, the two variables can predict each other.

The study suggests that lack of access to clean and modern energy services contributes to the depth of the shadow economy in Africa. African authorities are advised to strengthen rural and urban electrification initiatives by providing adequate energy infrastructure so as to reduce the level of energy poverty in the region. To ensure energy sustainability delivery, the study proposes that the creation of national and local capacities would be the most effective manner to guarantee energy accessibility and affordability. Also, priorities should be given to the local capital mobilization and energy subsidies for the energy poor. Energy literacy may also contribute to the sustainability and the usage of modern energy sources in Africa.

Previous studies reveal that income inequality contributes to the large size of shadow economy in developing economies. However, none of these studies analyzed the role of energy poverty and its implications for underground economic operations. Inadequate access to modern energy sources is likely to deepen the prevalence of informality in developing nations. Based on this, this study provides fresh evidence on the implications of energy deprivation on the shadow economy in Africa using a heterogeneous panel econometric framework. The study contributes to the literature by advocating that the provision of affordable modern energy sources for rural and urban settlements, and the creation of good energy infrastructure for the firms in the formal economy would not only improve the quality of life but also important to discourage underground economic operations in developing economies.

Despite the dramatic increase in construction toward additive manufacturing, several challenges are faced using natural materials such as Earth and salt compared to the most market-useable materials in 3D printing as concrete which consumes high carbon emission.

Characterization and mechanical tests were conducted on 19 samples for three natural binders in dry and wet tests to mimic the additive manufacturing process in order to reach an efficient extrudable and printable mixture that fits the 3D printer.

Upon testing compressive strength against grain size, compaction, cohesion, shape, heat and water content, X-Salt was shown to record high compressive strength of 9.5 MPa. This is equivalent to old Karshif and fire bricks and surpasses both rammed Earth and new Karshif. Material flow analysis for X-Salt assessing energy usage showed that only 10% recycled waste was produced by the end of the life cycle compared to salt.

Findings are expected to upscale the use of 3D salt printing in on-site and off-site architectural applications.

Findings contribute to attempts to resolve challenges related to vernacular architecture using 3D salt printing with sufficient stability.

Benefits include recyclability and minimum environmental impact. Social aspects related to technology integration remain however for further research.

This paper expands the use of Karshif, a salt-based traditional building material in Egypt's desert by using X-Salt, a salt-base and natural adhesive, and investigating its printability by testing its mechanical properties to reach a cleaner and low-cost sustainable 3D printed mixture.

This pragmatic research paper aims to unravel the smart vision-based method (SVBM), an AI program to correlate the computer vision (recorded and live videos using mobile and embedded cameras) that aids in manual lifting human pose deduction, analysis and training in the construction sector.

Using a pragmatic approach combined with the literature review, this study discusses the SVBM. The research method includes a literature review followed by a pragmatic approach and lab validation of the acquired data. Adopting the practical approach, the authors of this article developed an SVBM, an AI program to correlate computer vision (recorded and live videos using mobile and embedded cameras).

Results show that SVBM observes the relevant events without additional attachments to the human body and compares them with the standard axis to identify abnormal postures using mobile and other cameras. Angles of critical nodal points are projected through human pose detection and calculating body part movement angles using a novel software program and mobile application. The SVBM demonstrates its ability to data capture and analysis in real-time and offline using videos recorded earlier and is validated for program coding and results repeatability.

Literature review methodology limitations include not keeping in phase with the most updated field knowledge. This limitation is offset by choosing the range for literature review within the last two decades. This literature review may not have captured all published articles because the restriction of database access and search was based only on English. Also, the authors may have omitted fruitful articles hiding in a less popular journal. These limitations are acknowledged. The critical limitation is that the trust, privacy and psychological issues are not addressed in SVBM, which is recognised. However, the benefits of SVBM naturally offset this limitation to being adopted practically.

The theoretical and practical implications include customised and individualistic prediction and preventing most posture-related hazardous behaviours before a critical injury happens. The theoretical implications include mimicking the human pose and lab-based analysis without attaching sensors that naturally alter the working poses. SVBM would help researchers develop more accurate data and theoretical models close to actuals.

By using SVBM, the possibility of early deduction and prevention of musculoskeletal disorders is high; the social implications include the benefits of being a healthier society and health concerned construction sector.

Human pose detection, especially joint angle calculation in a work environment, is crucial to early deduction of muscoloskeletal disorders. Conventional digital technology-based methods to detect pose flaws focus on location information from wearables and laboratory-controlled motion sensors. For the first time, this paper presents novel computer vision (recorded and live videos using mobile and embedded cameras) and digital image-related deep learning methods without attachment to the human body for manual handling pose deduction and analysis of angles, neckline and torso line in an actual construction work environment.

Avatars, which are used as a technology and marketing tactic, can embody consumer-facing employees and mimic their real-life roles on companies' websites, thereby playing a key role in enhancing the relationships between consumers and brands in the online environment. Academics and practitioners have increasingly acknowledged the significance of the consumer-brand relationship in both traditional and online contexts. However, the impersonal nature of the online environment is considered to be a hindrance to the development of these relationships. Despite the importance of this technology, little attention has been paid to the investigation of the avatar concept from a marketing perspective. This paper explores the nature of the avatar concept, including its main characteristics, dimensions, and conditions as well as the attitudinal and behavioural consequences of avatar users.

Adopting the qualitative design, a taxonomy was developed from interviews. In total, 42 interviews were conducted with current university students. 30 participants participated in the exploratory interviews. A total of 12 interviews were conducted during the in-depth stage based on findings in the preceding research.

Based on the qualitative data analysis, a taxonomy was developed. The idea of the taxonomy is summarized in that different dimensions of the avatar are considered the main base (first phase) of the taxonomy. There are consequential three parts: the attitudinal consequences related to the website; the attitudinal consequences related to the brand; the behaviours towards the brand. These behaviours represent the final phase of the taxonomy.

By developing a taxonomy of using avatars on brands' websites, the authors advance the understanding consumer-brands relationships. Using avatars' verbal interactions helps in shaping consumers' cognitive, affective, attitudinal and behavioural responses and add vital empirical evidence to the increasing body of research and practices involving avatar usage in the interactive marketing area.

This paper aims to explore how the role of the perception of good public governance reduces tax evasion (TE). Besides, this study investigates whether the nexus of public governance and TE differs between developed and developing economies.

Apart from the ordinary least squares (OLS) model, this study uses the linear mixed modeling technique. The World Governance Indicators and the multiple causes estimation (MIMIC) method are used to measure public governance. The shadow economy is used as a proxy for TE.

The results show that people's perceptions of public governance and the quality of government institutions are core elements that influence tax-evasion behavior. Besides, the rule of law (RoL) and political stability (PS) significantly impact tax-evasion behavior in developing countries. Nevertheless, the RoL, the control of corruption and PS are the most critical tax-evasion determinants among public governance indicators for developed countries. Regulatory quality shows a substantial positive relationship with TE in developed but not developing countries.

This paper provides a guide for policymakers on reducing tax-evasion behavior by paying more attention to maintaining the RoL and PS and fighting corruption. Additionally, this study highlights the importance of people's perceptions of the government's pursuit of the above policy-related improvements, which, in turn, affect their tax behavior.

To the best of the authors’ knowledge, this study is the first to explore the role of people's perceptions of improvements in public governance and how this can reduce TE behavior in developed and developing economies. Unlike prior studies, this study used the linear mixed model method, which is more advantageous than OLS and produces robust estimators.

This study examines whether common directors are guilty of contagion of corporate frauds from the customer side and, if so, how contagion occurs. Moreover, it explores a way to mitigate it, which is the increased digital orientation of firms.

Secondary data analysis is applied in this paper. We extract supply chain relations from the China Stock Market and Account Research (CSMAR) database as well as corporate fraud data from the same database and the official website of the China Securities Regulatory Commission (CSRC). Digital orientations are estimated through text analysis. Poisson regression is conducted to examine the moderating effect of common directors and the moderated moderating effect of the firms’ digital orientations.

By analysing the 2,096 downstream relations from 2000 to 2021 in China, the study reveals that corporate frauds are contagious through supply chains, while only customers’ misconduct can contagion to upstream firms. The presence of common directors strengthens such supply chain contagion. Additionally, the digital orientation can mitigate the positive moderating effect of common directors on supply chain contagion.

This study highlights the importance of understanding supply chain contagion through corporate fraud by (1) emphasising the existence of the contagion effects of corporate frauds; (2) understanding the potential channel in the process of contagion; (3) considering how digital orientation can mitigate this contagion and (4) recognising that the effect of contagion comes only from the downstream, not from the upstream.