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Need: The previous suggestion assists with administrative methodology. The contribution explores customer understandings in different industry and transaction texts. They include online education, video marketing, and entertainment analytics. The communication needs to be detailed to improve the system.

Purpose: The suggestion aims to improve the previous contribution by enhancing the user experience. The study increases the usage of video content. The recommendation brings better business to the video host.

Methodology: The work includes the machine learning algorithm to understand the user and improve the client’s experience. The recommendation uses the Apriori algorithm to map various attributes of the trainer and learners. The suggested work has three features. It focusses on video possessions, educator feelings, physical characteristics, and visible aesthetic characteristics. The study considers 1,200 different samples.

Findings: The work simulates using python. It improves efficiency by 29.5% compared to previous work.

Practical Implications: Machine learning has pitched in to understand diverse customers’ behaviour. Various features affecting the behaviour are collected and analysed by the system. The study intends to find an appropriate mapping between the attributes of the user and educator.

There is a strong prerequisite for organizations to analyze customer review behavior to evaluate the competitive business environment. The purpose of this study is to analyze and predict customer reviews of halal restaurants using machine learning (ML) approaches.

The authors collected customer review data from the Yelp website. The authors filtered the reviews of only halal restaurants from the original data set. Following cleaning, the filtered review texts were classified as positive, neutral or negative sentiments, and those sentiments were scored using the AFINN and VADER sentiment algorithms. Also, the current study applies four machine learning methods to classify each review toward halal restaurants into its sentiment class.

The experiment showed that most of the customer reviews toward halal restaurants were positive. The authors also discovered that all of the methods (decision tree, linear support vector machine, logistic regression and random forest classifier) can correctly classify the review text into sentiment class, but logistic regression outperforms the others in terms of accuracy.

The results facilitate halal restaurateurs in identifying customer review behavior.

Sentiment and emotions, according to appraisal theory, form the basis for all interactions, facilitating cognitive functions and supporting prospective customers in making sense of experiences. Emotion theory also describes human affective states that determine motives and actions. The study looks at how potential customers might react to a halal restaurant’s consensus on social media based on reviewers’ opinions of halal restaurants because emotions can be conveyed through reviews.

This study applies machine learning approaches to analyze and predict customer sentiment based on the review texts toward halal restaurants.

The purpose of this paper is to understand different aspects of structural dispersion in virtual teams (VTs). The study measures five types of dispersion, their impact on VT performance and the moderating effect of electronic communication.

The authors collected data from 44 globally distributed VTs representing 403 members. The authors used details of the members’ locations to measure five elements of dispersion for each team: spatial, time-zone, number of locations, extent of numerical balance across locations and extent of isolated members for a team. The authors used two items to assess effective electronic communication and measured team performance on four items from three sources – members, leaders and third-party stakeholders.

Using regression, the authors found that the number of sites, degree of team balance and isolation had a negative impact on team performance. Spatial and temporal dispersion did not impact performance. Effective electronic communication moderated the relationship of team performance with team balance and the number of sites.

Study presents novel findings on the role of team configuration in VTs. Limitations: the study provides pointers to the likelihood of a non-linear relationship between spatial distance and performance; however, the scope of the paper does not permit an examination of this model. Future research can study this relationship. Second, the study does not examine how team configuration impacts the team processes that discount performance. Finally, the study treats each index of dispersion as independent of the others. The analysis does not study the interplay between and among the indices.

The findings provide clear indicators for managers and researchers of VTs on the issues associated with the location and configuration of the teams. Managers, while designing and managing dispersed members are now informed of the impact of the number of sites and the sub-group dynamics. The study underscores the importance of effective electronic communication in managing dispersion.

The study presents how faultiness based on location of VT sub-groups (as represented in the configuration of a team) can hamper performance. Literature suggests that this faultiness can also extend to social identities (based on gender, culture, etc.). The indicators provided by this study in this respect provide a topical focus for research because diverse dispersed teams are becoming more prevalent.

The study is the first empirical exploration of dispersion in VTs beyond the traditionally acknowledged dimensions of spatial distance and time-zones. It is a timely response to the recent trends in literature. Additionally, the study derives data from a unique data set of global VTs, thus making findings easily generalizable.

The purpose of this paper is to examine Vietnamese international students’ experiences with the campus learning environment by analysing differences in staff and student perceptions.

Two focus groups (n=12) and ten in-depth interviews were conducted with Vietnamese students and four in-depth interviews with the university staff (totalling 26).

The findings show a greater divergence of views between students and staff on teaching and learning than English language proficiency and student support services. These key differences were influenced by students’ prior expectations of their learning environment in Vietnam.

This study is limited to one group of international students and to one Australian university.

The lecturers/administrators must have a good understanding of international students’ learning backgrounds and expectations to enhance their positive experience; appropriate teaching skills and practices are essential for teachers to meet the current needs of students. More effective training for international students to understand the multicultural nature of Australia is also essential.

This study contributes to the literature by identifying the experience of Vietnamese international students in a western university, which is, a relatively under-researched nationality compared to other Asian nationalities such as Chinese and Indian.

This study aims to develop an empathetic and user-centric customer support service design model. Though service design has been a critical research focus for several decades, few studies focus on customer support services. As customer support gains importance as a source of competitive advantage in the present era, this paper aims to contribute to industry and academia by exploring the service design model.

The study adopted a theories-in-use approach to elucidate mental models based on the industry’s best practices. In-depth interviews with 62 professionals led to critical insights into customer service design development, supported by service-dominant logic and theory of mind principles.

The ensuing insights led to a model that connects the antecedents and outcomes of empathetic and user-centric customer service design. The precursors include people, processes and technology, while the results are user experience, service trust and service advocacy. The model also emphasises the significance of the user’s journey and the user service review in the overall service design.

The model developed through this study addresses the critical gap concerning the lack of service design research in customer support services. The key insights from this study contribute to the ongoing research endeavours towards transitioning customer support services from an operational unit to a strategic value-creating function. Future scholars may investigate the applicability of the empathetic user service design across cultures and industries. The new model must be customised using real-time data and analytics across user journey stages.

The empathetic and user-centric design can elevate the customer service function as a significant contributor to the overall customer experience, loyalty and positive word of mouth. Practitioners can adopt the new model to provide superior customer service experiences. This original research was developed through crucial insights from interviews with senior industry professionals.

This research is the original work developed through the key insights from the interview with senior industry professionals.

In general, cloud computing is a model of on-demand business computing that grants a convenient access to shared configurable resources on the internet. With the increment of workload and difficulty of tasks that are submitted by cloud consumers; “how to complete these tasks effectively and rapidly with limited cloud resources?” is becoming a challenging question. The major point of a task scheduling approach is to identify a trade-off among user needs and resource utilization. However, tasks that are submitted by varied users might have diverse needs of computing time, memory space, data traffic, response time, etc. This paper aims to proposes a new way of task scheduling.

To make the workflow completion in an efficient way and to reduce the cost and flow time, this paper proposes a new way of task scheduling. Here, a self-adaptive fruit fly optimization algorithm (SA-FFOA) is used for scheduling the workflow. The proposed multiple workflow scheduling model compares its efficiency over conventional methods in terms of analysis such as performance analysis, convergence analysis and statistical analysis. From the outcome of the analysis, the betterment of the proposed approach is proven with effective workflow scheduling.

The proposed algorithm is more superior regarding flow time with the minimum value, and the proposed model is enhanced over FFOA by 0.23%, differential evolution by 2.48%, artificial bee colony (ABC) by 2.85%, particle swarm optimization (PSO) by 2.46%, genetic algorithm (GA) by 2.33% and expected time to compute (ETC) by 2.56%. While analyzing the make span case, the proposed algorithm is 0.28%, 0.15%, 0.38%, 0.20%, 0.21% and 0.29% better than the conventional methods such as FFOA, DE, ABC, PSO, GA and ETC, respectively. Moreover, the proposed model has attained less cost, which is 2.14% better than FFOA, 2.32% better than DE, 3.53% better than ABC, 2.43% better than PSO, 2.07% better than GA and 2.90% better than ETC, respectively.

This paper presents a new way of task scheduling for making the workflow completion in an efficient way and for reducing the cost and flow time. This is the first paper uses SA-FFOA for scheduling the workflow.

This study aims to investigate whether various types of risks faced by the publicly listed commercial banks of India and Bangladesh are driven by market power and provides comparative insights from both economies.

By using the adjusted Lerner index to gauge bank market power and applying the generalised methods of moments (GMM) regression approach, the research delved into the relationship between bank market power and three distinct facets of risk across a sample of 26 publicly listed commercial banks in India and 22 listed banks in Bangladesh spanning from 2011 to 2022.

The results indicate that for Bangladesh, both “competition fragility” and “competition stability” viewpoints coexist simultaneously across all risk types, supporting a nonlinear relationship between market power and risk. However, in the Indian context, a nonlinear association exists only in the case of credit risk, while the relationship with insolvency risk is linear, substantiating the “competition fragility view”. Apart from market power and bank-specific variables, GDP growth rate has emerged as a prominent driver across all risk categories in both countries.

The filtration of banks is a limitation that might have influenced the outcomes. This study recommends that the Reserve Bank of India encourages further bank consolidation. Along the same line, Bangladesh Bank should closely oversee the growing competitive landscape. Furthermore, the regulators must monitor the elevated levels of non-performing loans to reduce credit risk so as to bolster the stability of their respective banking sectors.

This comparative study is the first attempt to analyse the market power and risk relationship and includes a novel bank-specific variable, i.e. technology, apart from other established variables.

In order to prevent accidents during driving, driver drowsiness detection systems have become a hot topic for researchers. There are various types of features that can be used to detect drowsiness. Detection can be done by utilizing behavioral data, physiological measurements and vehicle-based data. The existing deep convolutional neural network (CNN) models-based ensemble approach analyzed the behavioral data comprises eye or face or head movement captured by using a camera images or videos. However, the developed model suffered from the limitation of high computational cost because of the application of approximately 140 million parameters.

The proposed model uses significant feature parameters from the feature extraction process such as ReliefF, Infinite, Correlation, Term Variance are used for feature selection. The features that are selected are undergone for classification using ensemble classifier.

The output of these models is classified into non-drowsiness or drowsiness categories.

In this research work higher end camera are required to collect videos as it is cost-effective. Therefore, researches are encouraged to use the existing datasets.

This paper overcomes the earlier approach. The developed model used complex deep learning models on small dataset which would also extract additional features, thereby provided a more satisfying result.

Drowsiness can be detected at the earliest using ensemble model which restricts the number of accidents.

The marketing of immersive and competitive online gaming products has proliferated in recent times. Consumption has also shown a substantial increase, especially among children. Such elevated levels of gaming have adversely affected children’s overall well-being. This paper aims to examine the role of parental attachment variables in enhancing children’s self-control behavior in counteracting the adverse effects of excessive gaming. The role of gender in excessive gaming is also studied.

A conceptual framework is tested that examines the direct relationship of features of parental attachment with excessive online gaming behavior and an indirect effect through the mediation of a child’s self-control construct using structural equation modeling.

The findings indicate that parental attachment through self-control can play a significant role in limiting excessive gaming behavior among vulnerable young gamers. Excessive gaming behavior was more pronounced for boys than girls. Alienation explained excessive gaming behavior among girls, while communication was significant for boys, but in a reversed direction.

All possible antecedent variables from the literature, like parental rearing style, that may further contribute to developing a comprehensive theoretical framework could not be studied.

The study suggests that the priming of children achieved through parental attachment relationships may help prevent excessive gaming behavior among vulnerable young gamers.

This study addresses the gap in the understanding of parental attachment features related to excessive gaming among different genders. It also establishes the role of the intervening mechanism of a child’s self-control in regulating behavior in relation to excessive gaming in the Indian context.