Search results

This study aims to fill the lack of quantitative studies of user preferences in quasi-public spaces to observe the use of quasi-public spaces by questioning the contemporary needs of urban communities and to develop design strategies accordingly.

Within the scope of this study, public space design elements affecting users' preferences in the quasi-public spaces of the Podium Park shopping center in Bursa, Turkey were evaluated. By considering the spatial characteristics of the study area, 4 main and 15 subcriteria were determined and utilized by analytic hierarchy process (AHP). These criteria were evaluated by experts and locals with a participatory approach.

According to the obtained results, “events” (S2), “sun/shade” (C2), “safety” (P3) and “planting” (U4) subcriteria were determined as the vital elements for quasi-public spaces.

Although the concept of quasi-public space has been discussed for nearly 30 years, it has been observed that there are no quantitative studies to determine the criteria of user preferences in these open spaces in the literature. This study is the first quantitative research for user preferences in quasi-public spaces and there is no previous study on this subject and study area in Turkey.

The purpose of this paper is to propose a new method that allows to compare the magnetic pressures of different pulse width modulation (PWM) strategies in a fast and efficient way.

The voltage harmonics are determined using the double Fourier integral. As for current harmonics and waveforms, a new generic model based on the Park transformation and a dq model of the machine was established taking saturation into consideration. The obtained analytical waveforms are then injected into a finite element software to compute magnetic pressures using nodal forces.

The overall proposed method allows to accelerate the calculations and the comparison of different PWM strategies and operating points as an analytical model is used to generate current waveforms.

While the analytical expressions of voltage harmonics are already provided in the literature for the space vector pulse width modulation, they had to be calculated for the discontinuous pulse width modulation. In this paper, the obtained expressions are provided. For current harmonics, different models based on a linear and a nonlinear model of the machine are presented in the referenced papers; however, these models are not generic and are limited to the second range of harmonics (two times the switching frequency). A new generic model is then established and used in this paper after being validated experimentally. And finally, the direct injection of analytical current waveforms in a finite element software to perform any magnetic computation is very efficient.

Warranty-based big data analysis has attracted a great deal of attention because of its key capabilities and role in improving product quality while minimizing costs. Information and details about particular parts (components) repair and replacement during the warranty term, usually stored in the after-sales service database, can be used to solve problems in a variety of sectors. Due to the small number of studies related to the complete analysis of parts failure patterns in the automotive industry in the literature, this paper focuses on discovering and assessing the impact of lesser-studied factors on the failure of auto parts in the warranty period from the after-sales data of an automotive manufacturer.

The interconnected method used in this study for analyzing failure patterns is formed by combining association rules (AR) mining and Bayesian networks (BNs).

This research utilized AR analysis to extract valuable information from warranty data, exploring the relationship between component failure, time and location. Additionally, BNs were employed to investigate other potential factors influencing component failure, which could not be identified using Association Rules alone. This approach provided a more comprehensive evaluation of the data and valuable insights for decision-making in relevant industries.

This study's findings are believed to be practical in achieving a better dissection and providing a comprehensive package that can be utilized to increase component quality and overcome cross-sectional solutions. The integration of these methods allowed for a wider exploration of potential factors influencing component failure, enhancing the validity and depth of the research findings.

The current study aims to propose and empirically investigate a conceptual model of the most relevant antecedents and consequences of Corporate Social Responsibility (CSR) attribution, thus providing a practical and concise model as well as examining brand attachment as a mediator explaining the relationship between CSR attribution and its consequences.

A between-subjects experimental design was employed. The study included two experimental conditions; intrinsic and extrinsic CSR attribution and a control condition. An online self-administered survey was utilised for data collection. The sample was a convenience sample of 336 university students. Both one-way between-groups ANOVA and Partial Least Squares-Structural Equation Modelling (PLS-SEM) were utilised for hypotheses testing.

The most significant antecedents of CSR attribution in order of importance are the firm's approach to CSR communication, past corporate social performance, CSR type and the firm's call for customers' participation in its CSR. CSR attribution exerted a significant direct positive impact on brand attachment and trust. Three significant indirect consequences of CSR attribution were PWOM intention, purchase intention and brand loyalty intention. Whereas trust played a significant mediating role between CSR attribution and its three indirect consequences, brand attachment exerted significant mediation only between CSR attribution and brand loyalty intention. Brand attachment might mediate the relationship between CSR attribution and purchase intention. However, brand attachment failed to play a mediating role between CSR attribution and PWOM intention.

Several studies marginally investigated CSR attribution. Despite the vital role of CSR attribution in how consumers receive firms' CSR engagement, the availability of CSR attribution-centric studies is limited. By introducing a model of the most relevant antecedents and consequences of CSR attribution, this study aids in understanding the psychological mechanism underlying consumers' CSR attribution and provides valuable implications.

The study aims to identify the evoked “sense of place” that the campus community attributes to a watershed area in a Malaysian higher institution, aiming to enhance their participation in watershed conservation. Central to this objective is the incorporation of the concept of a watershed as a place, serving as the conceptual framework for analysis.

This case study explores an urban lake at Universiti Malaya, Malaysia’s oldest higher institution. It uses diverse qualitative data, including document analysis, semi-structured interviews, vox-pop interviews and a co-production workshop, to generate place-based narratives reflecting the meanings and values that staff and students associate with the watershed. Thematic analysis is then applied for further examination.

The data patterns reveal shared sense of place responses on: campus as a historic place, student, staff and campus identity, in-place learning experiences and interweaving of community well-being and watershed health. Recommendations advocate translating these narratives into campus sustainability communication through empirical findings and continuous co-production of knowledge and strategies with the campus community.

The research findings play a critical role in influencing sustainable campus planning and community inclusion by integrating place-based frameworks into sustainable development and watershed management. The study recommends the process of identifying place-based narratives with implications for the development of sustainability communication in a campus environment.

This paper contributes both conceptually and empirically to the sustainable management of a campus watershed area through place-based thinking. It outlines a process for enhancing campus sustainability communication strategies.

The focus of this research is to investigate if user-generated content (UGC) generated in the Booking platform can support quality management improvement within the hospitality industry by increasing customer satisfaction and eliminating defects more efficiently. Hence, it contributes to understanding how data-driven companies can rely on customer data to focus on innovation and performance improvement to meet customer requirements, eliminate defects and increase customer satisfaction.

Following the literature review, information was collected from the digital platform Booking, encompassing 15 hotel industry companies in Portugal Porto and Braga regions, selected due to their high number of customer reviews. This data was organized and categorized, eliminating all unnecessary information for the research and building an Excel database. The database was subsequently analysed with SPSS and Voyant software, performing statistical analysis, hypothesis testing and text-mining techniques to analyse the comments. After these analyses, applying quality tools allowed for more in-depth conclusions.

The research results highlight that customers' most relevant requirements in the Portuguese hospitality industry are breakfast, parking and a swimming pool. It was also possible to realize that the location is an attractive requirement, the bathroom is a must-be requirement and breakfast is a performance requirement. The results also allowed us to answer the most critical research question: “Is user-generated content a valuable aid to quality?” the answer is yes since it was possible to use the data to find improvements and faults/failures in the services.

The results of this study represent an essential step towards a complete understanding of how to take advantage of UGC within the hospitality industry by establishing a solid base of techniques, methods and quality tools for UGC analysis that can be applied in future research on different industry sectors.

This paper aims to identify the relevant contributing constructs of readiness for the implementation of intelligent contracts (iContracts) in the construction industry. This study investigates the relationship between the personality dimensions of technology readiness index (TRI) and the system specific factors of technology acceptance model (TAM) within the context of iContracts.

Drawing insights from the extant literature and the author's previous qualitative investigations into iContract readiness constructs, a quantitative approach is used to operationalise the constructs by offering relevant statements to be measured and validated through a multiple-item scale against the users intent to accept the future iContract technology.

This study confirms and validates the relationship of the proposed iContract readiness index (iCRI) statements against the established TAM factors by offering 18 new constructs influencing technology readiness of the iContract technology. This study proves 9 of the 12 hypotheses highlighting key factors to be addressed for the successful development of the iContract technology.

This paper contributes to the body of knowledge by proposing a novel iCRI that informs an iContract technology readiness acceptance model (iCTRAM) for a trending technology. The iCTRAM can guide developers in producing an appropriate iContract solution and assess the readiness of users and organisations for the successful adoption of the iContract concept.

This study offers a unique theoretical framework, in an embryonic field, for predicting the success of iContract implementation within construction organisations. This study combines the established studies of TRI and TAM in producing a predictive iContract readiness assessment tool.

In recent years, negative spokesperson incidents have raised significant concerns in academia and industry. While several studies have addressed celebrity endorser scandals, comprehensive analyses of current knowledge are lacking. Therefore, this study systematically reviewed the related literature to better understand trends and suggest future research directions for advancing this field.

This study employs the theory–context–characteristics–methodology (TCCM) framework to examine 76 articles on celebrity endorser scandals.

Utilizing the TCCM framework, this study presents a comprehensive research framework, revealing that (1) the celebrity endorser scandal effect primarily includes associative learning, attribution of responsibility, and moral reasoning; (2) entertainment celebrities and athletes have received significant research attention; (3) both individual- and relationship-level characteristics serve as crucial moderators, with focal brand and related brand being the primary outcome variables. Additionally, this study outlines enterprise response strategies, encompassing the reformation of existing spokesperson relationships and the establishment of future spokesperson connections; and (4) quantitative approaches dominate the field.

This study integrates and expands existing research on celebrity endorser scandals while proposing future research opportunities to advance the field.

This paper aims to deepen the current knowledge of seasonality by investigating visitors’ intentional and behavioural patterns during peak and off-peak seasons. It compares the variation in several key behavioural factors, namely, duration of stay, party size, revisit intention, spending and breakdown of spending in different sectors in hospitality and tourism including entertainment, restaurant, accommodation and transportation. Moreover, this research expands the understanding by examining the effectiveness of two innovative strategies of offering a digital app and organising a unique event to tackle seasonal imbalances through stimulating visitors’ intention to change their timing of visit from peak to off-peak periods.

The author initially used a Delphi approach to gather experts’ opinion on the two scenario settings: event organisation and a trip planner app. The scenarios aimed to potentially encourage visitors to change their visit time to off-peak seasons. Then, using a quantitative survey, the travel habits and spending behaviours of 310 participants were captured. Furthermore, the survey assessed their intention to travel during off-peak seasons in response to the implementation of the two innovative strategies.

The results revealed that although the number of visitors who travel in off-peak seasons may be lower, their daily spending is higher than peak season visitors. In addition to total spending per day, the duration of stay, part size, quality of accommodation and re-visit intention of visitors indicated significant variation between peak and off-peak seasons. According to the statistical analysis’ results, organising events (including festivals) proves more effective in encouraging visitors to travel during off-peak seasons compared to digital innovation (i.e. a trip planner app). This finding is in line with the tenets of the Jobs-to-be-Done Theory of innovation.

This study contributes by conceptualising the mechanism of seasonality and its impacts on subsectors of tourism and hospitality. To the best of the author’s knowledge, this is one of the few empirical research that compares the behavioural patterns of visitors including their average spending per day between peak and off-peak seasons. Previous studies focused on specific regions or sectors, whereas this research investigates visitors’ behaviour on a broader scale to provide more comprehensive view. Furthermore, this study is novel due to practising an outside-in approach through investigating the effectiveness of the two innovative strategies aimed at addressing seasonality in the hospitality and tourism industry from visitors’ point of view.