In this study, we developed a modified version of the CRiteria Importance Through Inter-criteria Correlation (CRITIC) method, namely the Distance Correlation-based CRITIC (D-CRITIC) method. The usage of the method was illustrated by evaluating the weights of five smartphone criteria. The same evaluation was repeated using four other objective weighting methods, including the original CRITIC method. The results from all the methods were further analyzed based on three different tests (i.e., the distance correlation test, the Spearman rank-order correlation test, and the symmetric mean absolute percentage error test) to validate D-CRITIC. The tests revealed that D-CRITIC could produce more valid criteria weights and ranks than the original CRITIC method since D-CRITIC yielded a higher average distance correlation, a higher average Spearman rank-order correlation, and a lower symmetric mean absolute percentage error. Besides, additional sensitivity analysis indicated that D-CRITIC has the tendency to deliver more stable criteria weights and ranks with a larger decision matrix. The research has contributed an alternative objective weighting method to the area of multi-criteria decision-making through a unique extension of distance correlation. This study is also the first to propose the idea of a distance correlation test to compare the performance of different criteria weighting methods.

${\lambda }^0$-measure is a special type of fuzzy measure. In the context of multi-attribute decision making (MADM), the measure can be used together with Choquet integral to model the interdependencies that usually present between the decision attributes. Unfortunately, the range of techniques available to estimate ${\lambda }^0$-measure values is too limited i.e., only four techniques are available to this date. Besides, the review on literature shows that each of these existing techniques either requires some initial data from the decision-makers or misrepresents the actual interdependencies held by the attributes. Thus, an alternate unsupervised technique is needed for the estimation of ${\lambda }^0$-measure values. This study has developed such a technique by integrating the idea of distance correlation and Shannon entropy. In this technique, the two inputs required to estimate ${\lambda }^0$-measure values, namely, the interdependence degrees and fuzzy densities are determined by utilizing the distance correlation measures and entropy weights, respectively. An evaluation to rank the websites owned by five different hospitals located in Sabah, Malaysia, was conducted to illustrate the usage of the technique. A similar evaluation was also performed with a few selected MADM techniques for comparison purposes, where the proposed technique is found to have produced the most consistent ranking. From the literature perspective, this study has contributed an alternate unsupervised technique that can estimate ${\lambda }^0$-measure values without necessitating any additional data from the decision-makers, and at the same time can better capture the interdependencies held by the attributes.

Restaurant image refers to an immediate perception that pops up in a customer’s mind when the name of a restaurant is mentioned. Therefore, it is crucial for restaurants, including fast-food restaurants (FFRs), to evaluate and sustain a positive restaurant image. However, evaluating and improving a restaurant’s image is challenging, since it counts in multiple service attributes associated with various degrees of unknown priority. Even so, the existing literature is yet to outspread the usage of an appropriate multi-attribute decision-making (MADM)-based approach to specifically evaluate the image of FFRs. Therefore, this research aimed at employing such an approach to evaluate the image of four FFRs on an island in East Malaysia, using various people, processes, and physical evidence attributes. Firstly, an initial list of FFR image attributes was elicited from the available literature. This initial list was then further validated through a two-round Delphi survey involving a panel of ten experts. A questionnaire was then designed based on the finalized attributes, and data collected from a sample of 251 respondents were analyzed using the compromised-analytical hierarchy process (C-AHP) method. The C-AHP results suggest that the strategies to improve an FFR’s image should primarily incorporate the following six attributes: hospitality, employees’ problem-solving skills, employees’ knowledge, food taste, physical cleanliness, and service response time. The FFR at the top of the ranking has the highest performance scores over these same six attributes. Surprisingly, employees’ appearance and restaurant exterior were reported as the two least important image attributes. This research is the first to demonstrate the application of a hybrid MADM-based approach to uncover the weights of FFR image attributes and rank those FFRs by computing their aggregated image scores.

The Internet has significantly improved the quality of consumers’ decision when making online bookings for homestays which is an emerging segment of hospitality in Malaysia. Drawing from DeLone and McLean’s Theory of Information System Success, this study examines the effects of information quality, system quality, and electronic service quality on user’s satisfaction of e-homestay portals. Data were collected from homestay lodgers (N=187) in Malaysia. The study reports that the antecedents of information quality, system quality and electronic service quality positively correlates with user’s satisfaction. These results provide better understanding for the service providers to better plan their homestay portals to meet the expectation of potential users. The present study also highlights the significance of homestays lodging for the hospitality and tourism industry in Malaysia. Limitations of the study and future works are noted and discussed

Gamification is widely applied across various industries to enhance brand awareness and consumer purchase intent, particularly in marketing. This study uses Vroom's Expectancy Theory of Motivation to explore the connection between consumer behavior, brand awareness, and purchase intention, examining the moderating effect of privacy concerns. While previous studies have investigated the connection between motivation and gamification, a significant gap exists in understanding this area. This quantitative research employs SPSS and Smart-PLS software to analyze data collected from a 7-section survey questionnaire with 26 items rated on a 7-point Likert scale. Results indicate that engagement, user experience, rewards giving, and brand awareness significantly influence purchase intention, with the most substantial impact observed between brand awareness and purchase intention. This study provides insight into the increasingly widespread use of gamification in online platforms and offers valuable guidance for designing compelling gamified experiences. Keywords: Consumer Behavior, E-commerce, Gamification, Vroom Expectancy Theory of Motivation, Privacy Concerns.