The bubble milk tea industry in Malaysia which was thought to have slowed down in the recent years since its first appearance in 2010 has made a comeback. At the point of conducting this research, there are almost 100 brands of bubble milk tea in Malaysia and it is not surprising that some of these shops are selling more than a thousand cups a day. However, there has been limited research conducted on factors influencing brand equity on bubble milk tea brands in Johor Bahru. This study is to investigate whether brand loyalty, perceived quality, brand awareness and brand association influence brand equity on bubble milk tea brands in Johor Bahru through distribution of online questionnaires. This study novelty is at the examining the factors influencing brand equity in the context of bubble milk tea in Johor Bahru, Malaysia. Data derived from responses of 400 respondents through sampling were analysed using SPSS v29. Hypotheses testing performed through simple linear regression revealed that brand loyalty, perceived quality, brand awareness and brand association have significant effect on brand equity of bubble milk tea brands in Johor Bahru, Malaysia. It was also demonstrated that perceived quality has the most significance influence on brand equity. Organizations in the bubble milk tea industries are able to benefit from these findings by prioritizing their marketing strategies to gain competitive edge over their competitors. With findings that perceived quality having the most significance influence, marketers with limited resources can narrow down their options and focus on this specific dimension to increase their brand value.

This research aims to test the effect that the implementation of green practices at a major sport tourism event, the Badminton World Championships in Huelva (Spain), has on the future intention of spectators to return to similar sport events. A total of 523 spectators who attended the event were randomly selected and self-administered in the presence of the interviewer. A confirmatory factor analysis of the model and a multi-group analysis were carried out. Sporting events have a great impact on the environment in which they are organised, mainly when they are linked to tourism, whether at an economic, social or environmental level. The results indicated that green practices indirectly influence spectators' future intentions through emotions and satisfaction, direct antecedents. In addition, green practices directly affect both image and trust, and indirectly affect satisfaction. In conclusion, green practices are a variable to be taken into account when planning the organisation of a sporting event that aims to consolidate itself in the tourism and sports services market.

Ticket revenues are crucial for the financial success of sports teams. To maximize these revenues, teams continuously explore effective ticket promotional strategies. One such strategy includes partial season plans, which mirror bundle offers common across various industries. Another widespread promotional strategy across industries is offering discounted credit (i.e., store credit purchased at a lower price than its face value). However, its application in sports (e.g., providing a $500 credit for tickets at $450) remains limited. Therefore, this study explores critical questions such as: “How effective is offering discounted credit compared to partial season plans?” and “What factors influence ticket promotion preferences?” Consequently, the study employed a 2 × 2 × 2 experimental designs, considering three independent variables: promotion type (discounted credit vs. partial season plans), promotion flexibility (predefined vs. customizable), and the consumer’s distance to the venue (near vs. distant). Results indicated that partial season plans generated significantly higher perceived value and purchase intentions  while presenting lower perceived risks than discounted credit . Promotion flexibility did not significantly influence the three dependent variables , but the distance to the venue did . Both practical and theoretical implications of these findings are discussed.

In this research, we explore the psychological factors that SMB owners who are micro-entrepreneurs and use SNS for entrepreneurial purposes rely on to make their self-employment decisions. Research-based on a merger of the Technology Acceptance Model (TAM) and the Theory of Planned Behaviour (TPB) deals with how perceived ease of use (PEU), perceived usefulness (PU), attitude, subjective norms (SN), perceived behavioral control (PBC), openness to experience (OTE), and dominance contribute to people’s behavioural intention (BI) to use SNS for Data was collected from 342 SMB micro-entrepreneurs in the Delhi/NCR region of India by the means of a standardized questionnaire. Employing PLS-SEM, a partial least squares structural equation modeling was used to analyze the data. The results point out an impact of PU, attitude, and behavioral intention, and unappealing presentations, unacceptance of an explanation, unclear mechanisms, and domination do not make any difference. The research emphasizes how technophobe’s attitude, and the perception of effectiveness would impact micro-entrepreneurs desire to avail SNS for entrepreneurship efforts. Moreover, research shows the psychological understanding based on the SNS adoption by the small business owners, micro-entrepreneurs as well as for the practitioners and policymakers who are working to enhance the capability of the SMB. More investigations should be conducted on the other personality traits and cover more nations as demographic dividends in comparison to acquire more inclusive data.

Cultural tourism, an important component of the wider tourism industry, has received significant attention due to the complex interplay between cultural heritage and tourism experiences. This form of tourism invites tourists to discover the arts, traditions, and lifestyles of diverse communities, thereby enriching intercultural encounters. Examining the rapidly evolving field of cultural tourism research, this article looks at its many facets, highlighting its growth, thematic focus, and global importance. In order to better understand the wealth and highlight the body of work, this study undertakes a bibliometric analysis of the concept of cultural tourism. This exploration employs bibliometric searching of journals indexed in the web of science database from 1996 to 2023, using the biblioshiny software in rstudio. This approach provides a global perspective, revealing a prolific and multidisciplinary production of the concept of cultural tourism. The study identifies a total of 369 articles published between 1996 and 2023, involving 781 authors and 244 journals. The results underline the widespread engagement with the subject across diverse scientific communities and geographical regions.

Creating a crop type map is a dominant yet complicated model to produce. This study aims to determine the best model to identify the wheat crop in the Haridwar district, Uttarakhand, India, by presenting a novel approach using machine learning techniques for time series data derived from the Sentinel-2 satellite spanned from mid-November to April. The proposed methodology combines the Normalized Difference Vegetation Index (NDVI), satellite bands like red, green, blue, and NIR, feature extraction, and classification algorithms to capture crop growth's temporal dynamics effectively. Three models, Random Forest, Convolutional Neural Networks, and Support Vector Machine, were compared to obtain the start of season (SOS). It is validated and evaluated using the performance metrics. Further, Random Forest stood out as the best model statistically and spatially for phenology parameter extraction with the least RMSE value at 19 days. CNN and Random Forest models were used to classify wheat crops by combining SOS, blue, green, red, NIR bands, and NDVI. Random Forest produces a more accurate wheat map with an accuracy of 69% and 0.5 MeanIoU. It was observed that CNN is not able to distinguish between wheat and other crops. The result revealed that incorporating the Sentinel-2 satellite data bearing a high spatial and temporal resolution with supervised machine-learning models and crop phenology metrics can empower the crop type classification process.