Is more customer control of services always better?

Does artificial intelligence (AI) boost digital banking user satisfaction? Integration of expectation confirmation model and antecedents of artificial intelligence enabled digital banking

In the era disruptive technology the emergence of artificial intelligence has fundamentally improved banking operations. The execution of artificial intelligence is no longer discretionary for financial institutions and now it is considered an essential tool to meet customer expectations. Although artificial intelligence enabled digital banking is faster efficient and effective however user acceptance of digital banking driven by artificial intelligence is in its initial stages. Therefore, current study develops and integrated research framework with expectation confirmation model and examines digital banking user satisfaction and acceptance of AI enabled digital banking. Data were collected from digital banking user through structured questionnaire. Overall, 320 respondents were approached and requested to participate in digital banking survey. In return 251 valid responses were received and analyzed with structural equation modeling. Findings of the structural model indicate that satisfaction is jointly determined by expectation confirmation, perceived performance, trendiness, visual attractiveness, problem solving, customization, communication quality and revealed substantial variance R^2 51.1% in digital banking user satisfaction. Therefore, satisfaction and corporate reputation have shown considerable variance R^2 48.3 in user acceptance of AI enabled digital banking. Moreover, the research framework has revealed substantial predictive power Q^2 0.449 to predict digital banking user satisfaction and Q^2 0.493 user acceptance of artificial intelligence enabled digital banking. Concerning with hypotheses relationships exogenous factors have shown positive and significant impact user satisfaction except trendiness and customization. Practically, this research has suggested that policy makers should pay attention in improving user expectation confirmation, perceived performance, visual attractiveness, communication quality and corporate reputation which in turn enhance satisfaction and boost digital banking user's confidence to accept artificial intelligence enabled digital banking. This study is original as it integrates expectation confirmation model with the antecedents of artificial intelligence and examines user behavior towards acceptance of artificial intelligence enabled digital banking.

The personal and contextual factors that affect customer experience during rail service failures and the implications for service design

This paper: identifies personal and contextual factors that influence customer experience when service failures occur in rail transport; what is being conveyed through that factor (e.g. older age being used to convey vulnerability); and the implications for future service design. The results are from a thematic analysis of free-text rail passenger complaints (n = 516) reporting service failures that impacted on customer experience. The study differs from existing research on the pertinent personal and contextual factors for public transport service provision in that it: focuses on the passenger experience resulting from specific incidents (rather than evaluative, overall assessments of satisfaction), generates the factors inductively from the data (rather than a-priori) and uses detailed qualitative cases (rather than quantitative survey data). The findings (1) identify some similar factors to those used in previous research and uncover some new factors for both person and context, (2) provide an understanding of what they mean in terms of the passenger experience and (3) indicate how the factors might need to be measured if they are to be used by the rail industry. The paper concludes by using the outcome of an industry-based validation exercise to describe how the findings could be used in future rail services, namely: predicting where the customer experience is going to be sub-optimal, prioritising responses to particular circumstances, and designing services to better meet customer's needs. This exploratory research is timely, given the need for a more passenger-centric approach to service design and future developments such as smart-ticketing, which could potentially enable greater understanding of who is using the rail network and for what journeys.

Trapped in a service encounter

Purpose

The purpose of this paper is to explore the reasons why customers often cannot or do not exit a negative service encounter (lock-in) and to discuss how this affects their well-being and coping responses. This contributes to the research on how negative service encounters emerge and evolve and how such encounters impact customer well-being and subsequent responses.

Design/methodology/approach

An inductive, exploratory approach was used. Interviews with 20 service customers yielded over 90 detailed lock-in experiences across 25 different services. A multi-step, iterative coding process was used with a mixture of coding techniques that stem from a grounded theory approach.

Findings

Four categories of factors that caused customers to endure a negative event were identified (physical lock-in, dependency on the service, social lock-in and psychological lock-in). Customers either experienced inner turmoil (if they perceived having the option to stay or leave) or felt captive; both impacted their well-being and coping strategies in different ways. Three characteristics of negative events that caused lock-in to persist over time were identified.

Research limitations/implications

This is a qualitative study that aims to identify factors behind customer lock-in, reduced well-being and coping strategies across different types of service encounters. Future research may build on these themes to investigate lock-in during specific service encounters in greater depth.

Practical implications

This research provides insights regarding how service providers can anticipate lock-in situations. In addition, the findings point to several ways in which frontline employees can assist customers with the coping process, during lock-in.

Originality/value

Customer lock-in during a service encounter is a common, yet unexplored phenomenon. This research contributes to a better understanding of why customers endure negative events and how such perceptions are reflected in their experiences and behaviors.

Smart service experience in hospitality and tourism services

Purpose

The purpose of this paper is to offer a discussion, definition and comprehensive conceptualization of the smart service experience, i.e. the way guests and customers in hospitality and tourism experience and value the use of personalized and pro-active services that the intelligent use of data and technology enable.

Design/methodology/approach

Based on prior research on service experience, smart services and the differences between regular and smart services, this paper develops a conceptual framework in which the smart service experience is the central construct.

Findings

The characteristics of smart services (the intelligent, anticipatory, and adaptable use of data and technology) permit customers to experience services that previous conceptualizations of the service experience could not capture. The smart service experience provides empowerment, a seamless experience, enjoyment, privacy and security, and accurate service delivery. The paper also discusses challenges that service firms face in employing smart services, and proposes a future research agenda.

Practical implications

Both academics and practitioners expect smart services to revolutionize many industries such as tourism and hospitality. Therefore, research is needed to help understand the way customers experience smart services, what values they derive from them and the way service firms can employ them sensibly to enhance customers’ experiences.

Originality/value

This paper synthesizes insights from the literature on customer experience, smart services and co-creation into a conceptualization of the smart service experience, and distinguishes it from previous conceptualizations of regular services.

Consumer control in service recovery: beyond decisional control

Purpose

Focusing on decisional control of the outcome provides only a partial picture of how firms may handle customer complaints and ignores many (alternative) opportunities to recover the relationship with the customer when service delivery fails. The purpose of this paper is to introduce other types of control and explore their effects.

Design/methodology/approach

This paper conducts a field study using survey instruments to collect data from real consumers, which are subsequently analyzed with structural equations modeling.

Findings

The main conclusion of this study is that there is more to control than having a choice. Different types of control have differential main effects: behavioral control affects distributive justice, cognitive control affects procedural justice and decisional control affects interactional justice (which in turn affect satisfaction and loyalty).

Research limitations/implications

Service recovery research should include behavioral, cognitive and decisional control of the service recovery as aspects of the firm’s organizational response to customer complaints. The effects of these customer control types on satisfaction and loyalty are mediated by dimensions of justice.

Practical implications

Firms should offer complaining customers information to interpret and appraise the failure (cognitive control), opportunities to personally take action and influence the recovery (behavioral control), and choices in the recovery process and outcome (decisional control).

Originality/value

This study is the first to offer a comprehensive investigation of the subtle interrelationships between types of control and dimensions of justice in a service recovery context.