Search results

Following a contingency approach, this paper aims to understand when service automation can enhance or destroy value for customers in the frontline by (1) providing a comprehensive overview of factors that influence the value co-creation/co-destruction potential of service automation and (2) zooming in on the combination of service contexts and service tasks to develop research propositions.

This paper uses a grounded theory approach based on qualitative data from multiple methods (i.e. a diary study with follow-up interviews, a consultation of academic experts and a storyboard study) as well as a systematic literature review to develop (1) a Framework of Automated Service Interactions (FASI) and (2) a contingency model for service tasks/contexts.

This paper presents a framework which gives an overview of factors influencing the value co-creation/co-destruction potential of service automation. The framework discerns between three types of factors: service design (i.e. controllable and manageable by the organization), static contingency (i.e. uncontrollable and fixed) and dynamic contingency (i.e. uncontrollable and flexible). Furthermore, the paper presents a contingency model based on the combination of service contexts and service tasks which results in seven research propositions.

This paper brings structure in the fragmented field of service automation. It integrates and summarizes insights regarding service automation and sheds more light on when service automation has the potential to create or destroy value in the organizational frontline.

Previous research suggests that the compensation offered to customers after a service failure has to be substantial to make customer satisfaction surpass that of an error-free service. However, with the right service recovery strategy, it might be possible to reduce compensation size while maintaining happy customers. The aim of the current study is to test whether an anchoring technique can be used to achieve this goal.

After experiencing a service failure, participants were told that there is a standard size of the compensation for service failures. The size of this standard was different depending on condition. Thereafter, participants were asked how much they would demand to be satisfied with their customer experience.

The compensation demand was relatively high on average (1,000–1,400 SEK, ≈ $120). However, telling the participants that customers typically receive 200 SEK as compensation reduced their demand to about 800 SEK (Experiment 1)—an anchoring effect. Moreover, a precise anchoring point (a typical compensation of 247 SEK) generated a lower demand than rounded anchoring points, even when the rounded anchoring point was lower (200 SEK) than the precise counterpart (Experiment 2)—a precision effect.

Setting a low compensation standard—yet allowing customers to actually receive compensations above the standard—can make customers more satisfied while also saving resources in demand-what-you-want service recovery situations, in particular when the compensation standard is a precise value.

This paper aims to highlight problems and opportunities for introducing digital automation in public administration (PA) and to propose implications for public value creation of robotic process automation (RPA) through the perspective of good bureaucracy as a guiding framework.

This conceptual paper addresses the purpose by applying three normative ideal types: Weber’s ideal type for a bureaucracy, new public management and public value management. This paper synthesizes an analytical framework in conducting case studies of the implementation of RPA systems in municipal administration.

This paper contributes to new insights into public value creation and digital automation. The following four implications are proposed: the deployment of RPA in municipal administration should emphasize that organizing administrative tasks is essentially a political issue; include considerations based on a well-grounded analysis in which policy areas that are suitable for RPA; to pay attention to issues on legal certainty, personal integrity, transparency and opportunities to influence automated decisions; and that the introduction of RPA indicates a need to develop resources concerning learning and knowledge in the municipal administration.

This paper is innovative, as it relates normative, descriptive and prescriptive issues on the developing of digital automation in PA. The conceptual approach is unusual in studies of digitalization in public activities.

This paper aims to investigate the influential aspects of industrial branding in building customer brand engagement from the buyer’s and the seller’s points of view. Collecting buyer and seller information is essential to understand business-to-business interaction better. Buyer’s and seller’s perspective integration is significant for stakeholders to develop proper strategies to achieve customer brand engagement.

This study uses a structural equation model to examine the antecedents of customer brand engagement from the buyer’s perspective; then, the result is compared with the seller’s view by conducting an analytical hierarchy process. The authors exercise 140 valid data from the buyer’s industry and 9 experts from the seller’s industry.

This study finds that in developing customer brand engagement, rational brand quality is the most influential from the buyer’s view and top priority from the seller’s view. Surprisingly, both parties have different perspectives about the second and third priorities. The buyers put emotional brand associations as a second priority; perceived value is meaningless and insignificant. On the contrary, the sellers set the perceived value as the second priority and emotional brand associations as the last.

The respondents from the buyer industry cover various industries, and the research is limited to the buyer and the seller in the chemical polymer emulsion market, a market where product quality and application quality on the buyers’ side are essential and where the buyer–seller interaction is intense. Replicating the study in other industries and cultural backgrounds is recommended for generalization.

The paper’s novelty is that there are different priorities and perspectives from the buyer’s and the seller’s views. This study contributes to industrial brand engagement research studies. Investigation of the buyer’s and the seller’s perspectives in industrial brand engagement research studies is still limited.

Retailers increasingly endeavour to implement artificial intelligence (AI) innovations, such as humanoid social robots (HSRs), to enhance customer experience. This paper investigates the interactive effect of HSR intelligence and consumers' speciesism on their perceptions of retail robots as sales assistants.

Three online experiments testing the effects of HSRs' intellectual intelligence on individuals' perceived competence and, consequently, their decision to shop at a retail store that uses HSRs as sales assistants are reported. Furthermore, the authors examine whether speciesism attenuates these effects such that a mediation effect is likely to be observed for individuals low in speciesism but not for those with high levels of speciesism. Data for all studies were collected on Prolific and analysed with SPSS to perform a logistic regression and PROCESS 4.0 (Hayes, 2022) for the mediation and moderated-mediation analysis.

The findings show that the level of speciesism moderates the relationship between HSR intellectual intelligence and perceived competence such that an effect is found for low but not for high HSR intelligence. When HSR intellectual intelligence is low, individuals with higher levels of speciesism (vs low) rate the HSR as less competent and display lower HSR acceptance (i.e. customers' decision to shop using retail robots as sales assistants).

This research responds to calls in research to adopt a human-like perspective to understand the compatibility between humans and robots and determine how personality traits, such as a person's level of speciesism, may affect the acceptance of AI technologies replicating human characteristics (Schmitt, 2019). To the best of the authors' knowledge, the present research is the first to examine the moderating role of speciesism on customer perceptions of non-human retail assistants (i.e. human-like and intelligent service robots). This study is the first to showcase that speciesism, normally considered a negative social behaviour, can positively influence individuals' decisions to engage with HSRs.

By extending the service robot acceptance model (sRAM), this study aims to explore and enhance the acceptance of chatbots. The study considered functional, relational, social, user and gratification elements in determining the acceptance of chatbots.

By using the purposive sampling technique, data of 321 service customers, gathered from millennials through a questionnaire and subsequent PLS-SEM modeling, was applied for hypotheses testing.

Findings revealed that the functional elements, perceived usefulness and perceived ease of use affect acceptance of chatbots. However, in social elements, only perceived social interactivity affects the acceptance of chatbots. Moreover, both user and gratification elements (hedonic motivation and symbolic motivation) significantly influence the acceptance of chatbots. Lastly, trust is the only contributing factor for the acceptance of chatbots in the relational elements.

The study extends the literature related to chatbots and offers several guidelines to the service industry to effectively employ chatbots.

This is one of the first studies that used newly developed sRAM in determining chatbot acceptance. Moreover, the study extended the sRAM by adding user and gratification elements and privacy concerns as originally sRAM model was limited to functional, relational and social elements.

This article aims to develop a new theory that can better explain and predict how and when humans interact with commercial robots. To this end, utility maximization theory (UMT) along with four principles and propositions that may guide how human-to-commercial robot interactions are developed.

This article conceptualizes UMT by drawing from social exchange, conservation of resources, and technology-driven theories.

This article proposes UMT, which consists of four guiding principles and propositions. First, it is proposed that the human must invest sufficient resources to initiate a human-to-commercial robot interaction. Second, the human forms an expectation of utility gain maximization once a human-to-commercial robot interaction is initiated. Third, the human severs a human-to-commercial robot interaction if the human is unable to witness maximum utility gain upon the interaction. Finally, once the human severs a human-to-commercial robot interaction, the human seeks to reinvest sufficient resources in another human-to-commercial robot interaction with the same expectation of utility maximization.

This article is one of the few studies that offers a theoretical foundation for understanding the interactions between humans and commercial robots. Additionally, this article provides several managerial implications for managing effective human-to-commercial robot interactions.

Business models are increasingly recognized as a concept to support innovation in organizations. The implementation and operation of a new or altered business model involves the (re-)design of an organization's business processes and their successful execution. This study reviews and synthesizes the existing body of literature to guide organizations in systematically moving from a business model design to the implementation and operation of the business model through their underlying business processes.

A systematic literature review of the methods that bridge business models and business processes is performed. The selected 34 studies are classified according to the method's characteristics and the support in the design, implementation and operation of business models.

The results of the systematic review provide an overview of existing methods that organizations can adopt when moving from business model design into the implementation and operation of their business model using processes.

This work provides a comprehensive overview and detailed insight into the existing methods that align business models and business processes. It increases the understanding on how these two concepts can be synthesized to support more effective digital innovation in organizations. Based on the review results, knowledge gaps are identified and an agenda for future research bridging the fields of business models and business processes is proposed.

This article revisits some theories and concepts of public administration, including those related to public value, transaction costs and social equity, to analyze the advantages and disadvantages of using artificial intelligence (AI) algorithms in public service delivery. The author seeks to mobilize theory to guide AI-era public management practitioners and researchers.

The author uses an existing task classification model to mobilize and juxtapose public management theories against artificial intelligence potential impacts in public service delivery. Theories of social equity and transaction costs as well as some concepts such as red tape, efficiency and economy are used to argue that the discipline of public administration provides a foundation to ensure algorithms are used in a way that improves service delivery.

After presenting literature on the challenges and promises of using AI in public service, the study shows that while the adoption of algorithms in public service has benefits, some serious challenges still exist when looked at under the lenses of theory. Additionally, the author mobilizes the public administration concepts of agenda setting and coproduction and finds that designing AI-enabled public services should be centered on citizens who are not mere customers. As an implication for public management practice, this study shows that bringing citizens to the forefront of designing and implementing AI-delivered services is key to reducing the reproduction of social biases.

As a fast-growing subject, artificial intelligence research in public management is yet to empirically test some of the theories that the study presented.

The paper vulgarizes some theories of public administration which practitioners can consider in the design and implementation of AI-enabled public services. Additionally, the study shows practitioners that bringing citizens to the forefront of designing and implementing AI-delivered services is key to reducing the reproduction of social biases.

The paper informs a broad audience who might not be familiar with public administration theories and how those theories can be taken into consideration when adopting AI systems in service delivery.

This research is original, as, to the best of the author’s knowledge, no prior work has combined these concepts in analyzing AI in the public sector.

This is a general review study aiming to specify the key customer-based factors and technologies that influence the value co-creation (VCC) process through artificial intelligence (AI) and automation in the hospitality and tourism industry.

The study uses a theory-based general literature review approach to explore key customer-based factors and technologies influencing VCC in the tourism industry. By reviewing the relevant literature, the authors conclude a theoretical framework postulating the determinants of VCC in the AI-driven tourism industry.

This paper identifies customers' perceptions, attitudes, trust, social influence, hedonic motivations, anthropomorphism and prior experience as customer-based factors to VCC through the use of AI. Service robots, AI-enabled self-service kiosks, chatbots, metaversal tourism and new reality, machine learning (ML) and natural language processing (NLP) are technologies that influence VCC.

The results of this research inform a theoretical framework articulating the human and AI elements for future research set to expand the models predicting VCC in the tourism industry.

Few studies have examined consumer-related factors that influence their participation in the VCC process through automation and AI.