Journal of Advanced Research in Service Management

Service Quality Dimensions in Banking: A Comprehensive Literature Review

2024-02-06T08:55:50+00:00

This research paper delves into the multifaceted dimensions of service quality in the banking sector by conducting an extensive literature review. Drawing insights from various studies conducted globally, the paper aims to provide a comprehensive overview of the diverse methodologies, findings, and trends in evaluating service quality within the banking industry. Key dimensions explored include service system quality, behavioral service quality, service transactional accuracy, machine service quality, human skills, tangibles, empathy, reliability, responsiveness, and more. The synthesis of these studies aims to contribute to a deeper understanding of the nuanced aspects influencing customer perceptions of service quality in banks. It examines various perspectives, methodologies, and regions, encompassing both traditional and e-banking services. Key contributions include the development of new measurement scales, comparative analyses, and explorations of service quality’s impact on customer satisfaction and loyalty.

Online Social Comparison and Its Effect on Self Esteem and Mental Health.

2024-04-18T05:15:22+00:00

This research shows the effects of the social comparison orientation on social networking sites on individuals well-being (Vidani, 2015). It also shows the mediation effect of understanding social support and self-esteem in the relationship between social comparison and individual well-being (Vidani and Solanki, 2015). And it shows a negative result of social comparison orientation on individual well-being (Vidani, 2015). In their relation between social comparison orientation and individual well-being, social support has no moderator effect and also ensures social support and self-esteem have a negative serial mediator effect (Vidani, 2015). Social comparison orientation on social networking sites shows negative emotion and declines social support, self-esteem, and individual well-being. Theoretical suggestions for research must be discussed in further detail (Solanki and Vidani, 2016).

Consumer Preferences and Ordering Behaviour: A Comparative Analysis of Zomato and Swiggy Users

2024-04-18T05:19:13+00:00

This study conducts a comparative analysis of consumer preferences and ordering behavior between users of Zomato and Swiggy, two prominent online food delivery platforms. Understanding consumer behavior in the context of these platforms is crucial due to their growing influence in the food service industry. The research employs a mixed-methods approach, combining quantitative surveys and qualitative interviews to gather comprehensive insights.

The quantitative phase involves surveying a diverse sample of Zomato and Swiggy users to identify patterns in their preferences, including factors influencing platform selection, food choices, delivery time expectations, and satisfaction levels. The qualitative phase delves deeper into the motivations behind these preferences, exploring user experiences, perceptions of service quality, and factors affecting loyalty and repeat usage.

The findings reveal nuanced differences between Zomato and Swiggy users in terms of their ordering behaviors, platform preferences, and satisfaction levels. Factors such as user interface, variety of restaurant choices, delivery speed, and promotional offers emerge as significant influencers in platform selection and user satisfaction.

This comparative analysis contributes to a better understanding of the distinct characteristics and preferences of Zomato and Swiggy users, providing valuable insights for both platforms to enhance their services and tailor offerings to meet consumer needs effectively. The study concludes by discussing implications for marketing strategies, service improvements, and opportunities for innovation within the online food delivery industry.

A Study on Customer Satisfaction Towards Allen Solly Apparels in Ahmedabad City.

2024-04-18T05:22:19+00:00

This research investigates the level of customer satisfaction with Allen Solly apparels in Ahmedabad with the aim of providing some valuable insights into the components influencing customer preferences in the competitive apparel market. The researchers used a quantitative research method to gather the data from a diverse sample of Allen Solly customers in Ahmedabad. The research objectives include evaluating customer satisfaction, identifying factors influencing purchasing decisions, assessing how customer service is effective, and understanding the viewpoints of product quality and brand name. The survey instruments include Likert-scale questions to collect quantitative aspects of customer satisfaction. The findings of this research conclude the strengths and weaknesses of Allen Solly in Ahmedabad City. Moreover, the research aims to provide implementable suggestions for the company to improve customer satisfaction, loyalty, and competitiveness in the evolving retail environment. This study serves as an essential reference for researchers, professionals, and shareholders interested in understanding consumer behavior and improving strategies.

Artificial Intelligence (AI): A Boon for Marketing

2024-04-18T05:28:54+00:00

Artificial intelligence in marketing is a rapidly developing concept that is changing the way firms approach marketing tactics. It includes the use of advanced technologies such as artificial intelligence (AI), machine learning (ML), and others to automate and optimize various marketing operations. Businesses must use these tools to stay competitive in the face of an explosion of data and rising complexity in customer behavior. This article delves into the concept of artificial intelligence in marketing, its role in modern marketing, its advantages and disadvantages, best practices for deployment, and ethical implications. It will also investigate the future of AI in marketing and its potential impact on the marketing landscape. (Asi, 2023) (AI) is the knowledge that the machine receives about the linguistic structure. Based on a learning algorithm that repeats patterns in fresh data, AI should produce a more rapid and intuitive answer. The cognitive process can be effectively mimicked by using numerous layers of intricately interconnected biological subsystems that remain unaffected by various input transformations. The universal language algorithm is provided by the universal structure of language, which contains the invariant that artificial intelligence and cognitive computing are chasing. In order to avoid the “curse of dimensionality,” the representation property to enhance machine learning (ML) generalizes the execution of a set of underlying variation factors that need to be stated in the form of other smaller underlying variation factors. The universal model outlines a more comprehensive approach. (Dioneia Motta Monte-Serrat, 2022)