Pub Date : 2022-11-01DOI: 10.1177/10946705221136270
Claire Cardy, Nawar N. Chaker, Johannes Habel, M. Klarmann, Olaf Plötner
Extant literature has studied how customer–salesperson price negotiations evolve in “normal” circumstances. However, recent economic recessions illustrate the need to advance theory on the question of how price negotiations evolve in “abnormal” times when customer demand significantly contracts beyond expected variation. In response to this gap in the literature, this study uses a multi-method design to investigate price negotiations during exceptional demand contractions. Our results from a theories-in-use study reveal that during such circumstances, salespeople’s perceived dependency on customers increases while customers’ perceived dependency on salespeople decreases. The inherent “power shift” should benefit customers in subsequent price negotiations. However, customers are less likely to capitalize on their power if they have a close relationship with a salesperson, implying that salespeople do not have to concede on price negotiations. This effect is likely due to increased sympathy during periods of exceptional demand contractions. The authors further validate key propositions from this qualitative study in a field study and a scenario-based experiment. Altogether, this study suggests that managers should not be too hasty in approving and encouraging salespeople to offer unnecessary price discounts during exceptional demand contractions as buyers may become more sympathetic and lenient during price negotiations. Graphical Abstract
{"title":"Customer–Salesperson Price Negotiations During Exceptional Demand Contractions","authors":"Claire Cardy, Nawar N. Chaker, Johannes Habel, M. Klarmann, Olaf Plötner","doi":"10.1177/10946705221136270","DOIUrl":"https://doi.org/10.1177/10946705221136270","url":null,"abstract":"Extant literature has studied how customer–salesperson price negotiations evolve in “normal” circumstances. However, recent economic recessions illustrate the need to advance theory on the question of how price negotiations evolve in “abnormal” times when customer demand significantly contracts beyond expected variation. In response to this gap in the literature, this study uses a multi-method design to investigate price negotiations during exceptional demand contractions. Our results from a theories-in-use study reveal that during such circumstances, salespeople’s perceived dependency on customers increases while customers’ perceived dependency on salespeople decreases. The inherent “power shift” should benefit customers in subsequent price negotiations. However, customers are less likely to capitalize on their power if they have a close relationship with a salesperson, implying that salespeople do not have to concede on price negotiations. This effect is likely due to increased sympathy during periods of exceptional demand contractions. The authors further validate key propositions from this qualitative study in a field study and a scenario-based experiment. Altogether, this study suggests that managers should not be too hasty in approving and encouraging salespeople to offer unnecessary price discounts during exceptional demand contractions as buyers may become more sympathetic and lenient during price negotiations. Graphical Abstract","PeriodicalId":48358,"journal":{"name":"Journal of Service Research","volume":"33 1","pages":"351 - 370"},"PeriodicalIF":12.4,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85368815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-01DOI: 10.1177/10946705221103538
E. Pantano, Daniele Scarpi
This research draws upon the increasing usage of AI in service. It aims at understanding the extent to which AI systems have multiple intelligence types like humans and if these types arouse different emotions in consumers. To this end, the research uses a two-study approach: Study 1 builds and evaluates a scale for measuring different AI intelligence types. Study 2 evaluates consumers’ emotional responses to the different AI intelligences. The findings provide a measurement scale for evaluating different types of artificial intelligence against human ones, thus showing that artificial intelligences are configurable, describable, and measurable (Study 1), and influence positive and negative consumers’ emotions (Study 2). The findings also demonstrate that consumers display different emotions, in terms of happiness, excitement, enthusiasm, pride, inspiration, sadness, fear, anger, shame, and anxiety, and also emotional attachment, satisfaction, and usage intention when interacting with the different types of AI intelligences. Our scale builds upon human intelligence against AI intelligence characteristics while providing a guidance for future development of AI-based systems more similar to human intelligences. Graphical Abstract
{"title":"I, Robot, You, Consumer: Measuring Artificial Intelligence Types and their Effect on Consumers Emotions in Service","authors":"E. Pantano, Daniele Scarpi","doi":"10.1177/10946705221103538","DOIUrl":"https://doi.org/10.1177/10946705221103538","url":null,"abstract":"This research draws upon the increasing usage of AI in service. It aims at understanding the extent to which AI systems have multiple intelligence types like humans and if these types arouse different emotions in consumers. To this end, the research uses a two-study approach: Study 1 builds and evaluates a scale for measuring different AI intelligence types. Study 2 evaluates consumers’ emotional responses to the different AI intelligences. The findings provide a measurement scale for evaluating different types of artificial intelligence against human ones, thus showing that artificial intelligences are configurable, describable, and measurable (Study 1), and influence positive and negative consumers’ emotions (Study 2). The findings also demonstrate that consumers display different emotions, in terms of happiness, excitement, enthusiasm, pride, inspiration, sadness, fear, anger, shame, and anxiety, and also emotional attachment, satisfaction, and usage intention when interacting with the different types of AI intelligences. Our scale builds upon human intelligence against AI intelligence characteristics while providing a guidance for future development of AI-based systems more similar to human intelligences. Graphical Abstract","PeriodicalId":48358,"journal":{"name":"Journal of Service Research","volume":"22 1","pages":"583 - 600"},"PeriodicalIF":12.4,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91023921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-05DOI: 10.1177/10946705221130467
Jochen Wirtz, W. Kunz, Nicole Hartley, James Tarbit
Digitization, artificial intelligence, and service robots carry serious ethical, privacy, and fairness risks. Using the lens of corporate digital responsibility (CDR), we examine these risks and their mitigation in service firms and make five contributions. First, we show that CDR is critical in service contexts because of the vast streams of customer data involved and digital service technology’s omnipresence, opacity, and complexity. Second, we synthesize the ethics, privacy, and fairness literature using the CDR data and technology life-cycle perspective to understand better the nature of these risks in a service context. Third, to provide insights on the origins of these risks, we examine the digital service ecosystem and the related flows of money, service, data, insights, and technologies. Fourth, we deduct that the underlying causes of CDR issues are trade-offs between good CDR practices and organizational objectives (e.g., profit opportunities versus CDR risks) and introduce the CDR calculus to capture this. We also conclude that regulation will need to step in when a firm’s CDR calculus becomes so negative that good CDR is unlikely. Finally, we advance a set of strategies, tools, and practices service firms can use to manage these trade-offs and build a strong CDR culture. Graphical Abstract
{"title":"Corporate Digital Responsibility in Service Firms and Their Ecosystems","authors":"Jochen Wirtz, W. Kunz, Nicole Hartley, James Tarbit","doi":"10.1177/10946705221130467","DOIUrl":"https://doi.org/10.1177/10946705221130467","url":null,"abstract":"Digitization, artificial intelligence, and service robots carry serious ethical, privacy, and fairness risks. Using the lens of corporate digital responsibility (CDR), we examine these risks and their mitigation in service firms and make five contributions. First, we show that CDR is critical in service contexts because of the vast streams of customer data involved and digital service technology’s omnipresence, opacity, and complexity. Second, we synthesize the ethics, privacy, and fairness literature using the CDR data and technology life-cycle perspective to understand better the nature of these risks in a service context. Third, to provide insights on the origins of these risks, we examine the digital service ecosystem and the related flows of money, service, data, insights, and technologies. Fourth, we deduct that the underlying causes of CDR issues are trade-offs between good CDR practices and organizational objectives (e.g., profit opportunities versus CDR risks) and introduce the CDR calculus to capture this. We also conclude that regulation will need to step in when a firm’s CDR calculus becomes so negative that good CDR is unlikely. Finally, we advance a set of strategies, tools, and practices service firms can use to manage these trade-offs and build a strong CDR culture. Graphical Abstract","PeriodicalId":48358,"journal":{"name":"Journal of Service Research","volume":"62 1","pages":"173 - 190"},"PeriodicalIF":12.4,"publicationDate":"2022-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80725878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-14DOI: 10.1177/10946705221126590
Markus Gahler, Jan F. Klein, Michael Paul
Managing customer experiences has become a key strategic priority for service research and management. Yet researchers and managers lack a customer experience (CX) measure that applies to the different experience partners, touchpoints, and journey stages in the omnichannel environments of today’s service industries. Without such a common measure, empirical research on CX remains fragmented, and service companies continue to struggle to improve customer interactions in customer journeys. To address this shortcoming, this article proposes an omnichannel-capable measurement of CX that applies to different customer interactions in the omnichannel environment. With seven studies, the authors develop and validate a six-dimensional, 18-item CX scale. The proposed CX scale overcomes the fragmentation of existing scales in service research and provides a valid measure that can be used consistently for various customer interactions in omnichannel environments. This article details how the proposed CX scale can monitor and compare CX for different interactions in customer journeys (i.e., pain-point analysis), as well as improve CX features and their marketing outcomes (i.e., CX profiling). By overcoming the existing fragmentation in available scales and providing a common omnichannel CX measure, this CX scale establishes an empirical foundation for developing CX knowledge and advancing related service research.
{"title":"Customer Experience: Conceptualization, Measurement, and Application in Omnichannel Environments","authors":"Markus Gahler, Jan F. Klein, Michael Paul","doi":"10.1177/10946705221126590","DOIUrl":"https://doi.org/10.1177/10946705221126590","url":null,"abstract":"Managing customer experiences has become a key strategic priority for service research and management. Yet researchers and managers lack a customer experience (CX) measure that applies to the different experience partners, touchpoints, and journey stages in the omnichannel environments of today’s service industries. Without such a common measure, empirical research on CX remains fragmented, and service companies continue to struggle to improve customer interactions in customer journeys. To address this shortcoming, this article proposes an omnichannel-capable measurement of CX that applies to different customer interactions in the omnichannel environment. With seven studies, the authors develop and validate a six-dimensional, 18-item CX scale. The proposed CX scale overcomes the fragmentation of existing scales in service research and provides a valid measure that can be used consistently for various customer interactions in omnichannel environments. This article details how the proposed CX scale can monitor and compare CX for different interactions in customer journeys (i.e., pain-point analysis), as well as improve CX features and their marketing outcomes (i.e., CX profiling). By overcoming the existing fragmentation in available scales and providing a common omnichannel CX measure, this CX scale establishes an empirical foundation for developing CX knowledge and advancing related service research.","PeriodicalId":48358,"journal":{"name":"Journal of Service Research","volume":"145 1","pages":"191 - 211"},"PeriodicalIF":12.4,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87974817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-18DOI: 10.1177/10946705221120147
Kirk L. Wakefield, Priya Raghubir, J. Inman
Traditional practice prominently presents offers (e.g., “50% Off”) followed by a quantity (“When you buy two”), duration (“Today only”), or other conditional restriction as a scarcity appeal to increase urgency. Placing a hurdle to clear before purchase eligibility presents the good news of the offer followed by the bad news of the restriction. We propose and test a sales promotion framework for admission-based experiences showing that leading with the bad news first (the restriction) followed by the good news (the discount) is consistent with consumer news order preferences and changes perceptions of the deal. Our first study confirms consumer preference for bad news before good news in general and ticket offers in particular. The next two studies examine the process by which leading with the bad news (of the restriction first, discount later) increases the salience of the deal (% off). This in turn makes the customer feel in greater control over the offer, thereby making the deal appear to be fairer and more attractive, leading to increased purchase intentions. A fourth study in the field shows presenting the restriction followed by a discount improves click-through and potential revenue compared to presenting the identical offer with the discount preceding the restriction.
{"title":"Have We Got a Deal for You: Do You Want the Good News or Bad News First?","authors":"Kirk L. Wakefield, Priya Raghubir, J. Inman","doi":"10.1177/10946705221120147","DOIUrl":"https://doi.org/10.1177/10946705221120147","url":null,"abstract":"Traditional practice prominently presents offers (e.g., “50% Off”) followed by a quantity (“When you buy two”), duration (“Today only”), or other conditional restriction as a scarcity appeal to increase urgency. Placing a hurdle to clear before purchase eligibility presents the good news of the offer followed by the bad news of the restriction. We propose and test a sales promotion framework for admission-based experiences showing that leading with the bad news first (the restriction) followed by the good news (the discount) is consistent with consumer news order preferences and changes perceptions of the deal. Our first study confirms consumer preference for bad news before good news in general and ticket offers in particular. The next two studies examine the process by which leading with the bad news (of the restriction first, discount later) increases the salience of the deal (% off). This in turn makes the customer feel in greater control over the offer, thereby making the deal appear to be fairer and more attractive, leading to increased purchase intentions. A fourth study in the field shows presenting the restriction followed by a discount improves click-through and potential revenue compared to presenting the identical offer with the discount preceding the restriction.","PeriodicalId":48358,"journal":{"name":"Journal of Service Research","volume":"26 1","pages":"251 - 269"},"PeriodicalIF":12.4,"publicationDate":"2022-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84993619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-18DOI: 10.1177/10946705221120232
Tae Woo Kim, Li Jiang, A. Duhachek, Hyejin Lee, Aaron M. Garvey
The use of Artificial Intelligence (AI) has grown rapidly in the service industry and AI’s emotional capabilities have become an important feature for interacting with customers. The current research examines personal disclosures that occur during consumer interactions with AI and humans in service settings. We found that consumers’ lay beliefs about AI (i.e., a perceived lack of social judgment capability) lead to enhanced disclosure of sensitive personal information to AI (vs. humans). We identify boundaries for this effect such that consumers prefer disclosure to humans over AI in (i) contexts where social support (rather than social judgment) is expected and (ii) contexts where sensitive information will be curated by the agent for social dissemination. In addition, we reveal underlying psychological processes such that the motivation to avoid negative social judgment favors disclosing to AI whereas seeking emotional support favors disclosing to humans. Moreover, we reveal that adding humanlike factors to AI can increase consumer fear of social judgment (reducing disclosure in contexts of social risk) while simultaneously increasing perceived AI capacity for empathy (increasing disclosure in contexts of social support). Taken together, these findings provide theoretical and practical insights into tradeoffs between utilizing AI versus human agents in service contexts.
{"title":"Do You Mind if I Ask You a Personal Question? How AI Service Agents Alter Consumer Self-Disclosure","authors":"Tae Woo Kim, Li Jiang, A. Duhachek, Hyejin Lee, Aaron M. Garvey","doi":"10.1177/10946705221120232","DOIUrl":"https://doi.org/10.1177/10946705221120232","url":null,"abstract":"The use of Artificial Intelligence (AI) has grown rapidly in the service industry and AI’s emotional capabilities have become an important feature for interacting with customers. The current research examines personal disclosures that occur during consumer interactions with AI and humans in service settings. We found that consumers’ lay beliefs about AI (i.e., a perceived lack of social judgment capability) lead to enhanced disclosure of sensitive personal information to AI (vs. humans). We identify boundaries for this effect such that consumers prefer disclosure to humans over AI in (i) contexts where social support (rather than social judgment) is expected and (ii) contexts where sensitive information will be curated by the agent for social dissemination. In addition, we reveal underlying psychological processes such that the motivation to avoid negative social judgment favors disclosing to AI whereas seeking emotional support favors disclosing to humans. Moreover, we reveal that adding humanlike factors to AI can increase consumer fear of social judgment (reducing disclosure in contexts of social risk) while simultaneously increasing perceived AI capacity for empathy (increasing disclosure in contexts of social support). Taken together, these findings provide theoretical and practical insights into tradeoffs between utilizing AI versus human agents in service contexts.","PeriodicalId":48358,"journal":{"name":"Journal of Service Research","volume":"50 1","pages":"649 - 666"},"PeriodicalIF":12.4,"publicationDate":"2022-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91210612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-15DOI: 10.1177/10946705221120218
C. Makridis, Saurabh Mishra
The share of artificial intelligence (AI) jobs in total job postings has increased from 0.20% to nearly 1% between 2010 and 2019, but there is significant heterogeneity across cities in the United States (US). Using new data on AI job postings across 343 US cities, combined with data on subjective well-being and economic activity, we uncover the central role that service-based cities play to translate the benefits of AI job growth to subjective well-being. We find that cities with higher growth in AI job postings witnessed higher economic growth. The relationship between AI job growth and economic growth is driven by cities that had a higher concentration of modern (or professional) services. AI job growth also leads to an increase in the state of well-being. The transmission channel of AI job growth to increased subjective well-being is explained by the positive relationship between AI jobs and economic growth. These results are consistent with models of structural transformation where technological change leads to improvements in well-being through improvements in economic activity. Our results suggest that AI-driven economic growth, while still in the early days, could also raise overall well-being and social welfare, especially when the pre-existing industrial structure had a higher concentration of modern (or professional) services.
{"title":"Artificial Intelligence as a Service, Economic Growth, and Well-Being","authors":"C. Makridis, Saurabh Mishra","doi":"10.1177/10946705221120218","DOIUrl":"https://doi.org/10.1177/10946705221120218","url":null,"abstract":"The share of artificial intelligence (AI) jobs in total job postings has increased from 0.20% to nearly 1% between 2010 and 2019, but there is significant heterogeneity across cities in the United States (US). Using new data on AI job postings across 343 US cities, combined with data on subjective well-being and economic activity, we uncover the central role that service-based cities play to translate the benefits of AI job growth to subjective well-being. We find that cities with higher growth in AI job postings witnessed higher economic growth. The relationship between AI job growth and economic growth is driven by cities that had a higher concentration of modern (or professional) services. AI job growth also leads to an increase in the state of well-being. The transmission channel of AI job growth to increased subjective well-being is explained by the positive relationship between AI jobs and economic growth. These results are consistent with models of structural transformation where technological change leads to improvements in well-being through improvements in economic activity. Our results suggest that AI-driven economic growth, while still in the early days, could also raise overall well-being and social welfare, especially when the pre-existing industrial structure had a higher concentration of modern (or professional) services.","PeriodicalId":48358,"journal":{"name":"Journal of Service Research","volume":"1 1","pages":"505 - 520"},"PeriodicalIF":12.4,"publicationDate":"2022-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90161829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-10DOI: 10.1177/10946705221118233
Victoria‐Sophie Osburg, Vignesh Yoganathan, W. Kunz, S. Tarba
Advances in artificial intelligence (AI) are increasingly enabling firms to develop services that utilize autonomous vehicles (AVs). Yet, there are significant psychological barriers to adoption, and insights from extant literature are insufficient to understand customer emotions regarding AV services. To allow for a holistic exploration of customer perspectives, we synthesize multidisciplinary literature to develop the Customer Responses to Unmanned Intelligent-transport Services based on Emotions (CRUISE) framework, which lays the foundation for improved strategizing, targeting, and positioning of AV services. We subsequently provide empirical support for several propositions underpinning the CRUISE framework using representative multinational panel data (N = 27,565) and an implicit association test (N = 300). We discover four distinct customer segments based on their preferred degree of service autonomy and service risk. The segments also differ in terms of the valence and intensity of emotional responses to fully autonomous vehicle services. Additionally, exposure to positive information about AV services negatively correlates with the likelihood of membership in the two most resistant segments. Our contribution to service research is chiefly twofold; we provide: 1) a formal treatise of AV services, emphasizing their uniqueness and breadth of application, and 2) empirically validated managerial directions for effective strategizing based on the CRUISE framework.
{"title":"Can (A)I Give You a Ride? Development and Validation of the CRUISE Framework for Autonomous Vehicle Services","authors":"Victoria‐Sophie Osburg, Vignesh Yoganathan, W. Kunz, S. Tarba","doi":"10.1177/10946705221118233","DOIUrl":"https://doi.org/10.1177/10946705221118233","url":null,"abstract":"Advances in artificial intelligence (AI) are increasingly enabling firms to develop services that utilize autonomous vehicles (AVs). Yet, there are significant psychological barriers to adoption, and insights from extant literature are insufficient to understand customer emotions regarding AV services. To allow for a holistic exploration of customer perspectives, we synthesize multidisciplinary literature to develop the Customer Responses to Unmanned Intelligent-transport Services based on Emotions (CRUISE) framework, which lays the foundation for improved strategizing, targeting, and positioning of AV services. We subsequently provide empirical support for several propositions underpinning the CRUISE framework using representative multinational panel data (N = 27,565) and an implicit association test (N = 300). We discover four distinct customer segments based on their preferred degree of service autonomy and service risk. The segments also differ in terms of the valence and intensity of emotional responses to fully autonomous vehicle services. Additionally, exposure to positive information about AV services negatively correlates with the likelihood of membership in the two most resistant segments. Our contribution to service research is chiefly twofold; we provide: 1) a formal treatise of AV services, emphasizing their uniqueness and breadth of application, and 2) empirically validated managerial directions for effective strategizing based on the CRUISE framework.","PeriodicalId":48358,"journal":{"name":"Journal of Service Research","volume":"41 1","pages":"630 - 648"},"PeriodicalIF":12.4,"publicationDate":"2022-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83037552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-09DOI: 10.1177/10946705221118579
R. Bagozzi, Michael K. Brady, Ming-Hui Huang
AI in service can be for routine mechanical tasks, analytical thinking tasks, or empathetic feeling tasks. We provide a conceptual framework for the customer, firm, and interactional use of AI for empathetic tasks at the micro-, meso-, and macro-levels. Emotions resulting from AI service interactions can include basic emotions (e.g., joy, sadness, and fear), self-conscious emotions (e.g., pride, guilt, embarrassment), and moral emotions (e.g., contempt, righteous anger, social disgust). These emotions are mostly likely to occur during frontline interactions in which both firms and customers use AI, a phenomenon called “AI as customer.” The analysis level of AI service and emotion can be at the macro-level in which AI is transforming the service economy into a feeling economy, at the meso-level in which firms can use “thoughtful AI” to make the employees’ and customers’ lives a little bit better by brightening their days, and at the micro-level in which customers can experience basic, self-conscious, and moral emotions from interactions with service AI.
{"title":"AI Service and Emotion","authors":"R. Bagozzi, Michael K. Brady, Ming-Hui Huang","doi":"10.1177/10946705221118579","DOIUrl":"https://doi.org/10.1177/10946705221118579","url":null,"abstract":"AI in service can be for routine mechanical tasks, analytical thinking tasks, or empathetic feeling tasks. We provide a conceptual framework for the customer, firm, and interactional use of AI for empathetic tasks at the micro-, meso-, and macro-levels. Emotions resulting from AI service interactions can include basic emotions (e.g., joy, sadness, and fear), self-conscious emotions (e.g., pride, guilt, embarrassment), and moral emotions (e.g., contempt, righteous anger, social disgust). These emotions are mostly likely to occur during frontline interactions in which both firms and customers use AI, a phenomenon called “AI as customer.” The analysis level of AI service and emotion can be at the macro-level in which AI is transforming the service economy into a feeling economy, at the meso-level in which firms can use “thoughtful AI” to make the employees’ and customers’ lives a little bit better by brightening their days, and at the micro-level in which customers can experience basic, self-conscious, and moral emotions from interactions with service AI.","PeriodicalId":48358,"journal":{"name":"Journal of Service Research","volume":"55 1","pages":"499 - 504"},"PeriodicalIF":12.4,"publicationDate":"2022-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79433503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-02DOI: 10.1177/10946705221111347
Matthew J. Hall, Jamie D. Hyodo
While consumers frequently attempt to resolve their own consumption problems (i.e., do-it-yourself (DIY)), they are often unsuccessful and subsequently turn to a professional. In the present research, we consider DIY failure as a form of service failure (SF) and demonstrate that experiencing DIY service failure (DIY SF) influences consumer evaluations of subsequent firm recovery. This occurs because consumers who experience DIY SF gain greater understanding of the task (i.e., learning) through their failed attempt. This learning promotes increased appreciation of the recovering service provider’s ability, ultimately resulting in greater satisfaction with the recovery offering. We further identify mindset as a moderator of this effect, wherein those with a growth mindset are more likely to learn from failure and appreciate the abilities of the recovering service provider. By highlighting DIY SF as a novel form of SF, we demonstrate the importance of understanding customers’ prior experiences with the focal consumption problem and its solution, and of training front-line employees to better manage these customers. We test our theory across four studies using lab and field data, and close by discussing theoretical and managerial implications.
{"title":"Service Provider to the Rescue: How Firm Recovery of Do-It-Yourself Service Failure Turns Consumers from Competitors to Satisfied Customers","authors":"Matthew J. Hall, Jamie D. Hyodo","doi":"10.1177/10946705221111347","DOIUrl":"https://doi.org/10.1177/10946705221111347","url":null,"abstract":"While consumers frequently attempt to resolve their own consumption problems (i.e., do-it-yourself (DIY)), they are often unsuccessful and subsequently turn to a professional. In the present research, we consider DIY failure as a form of service failure (SF) and demonstrate that experiencing DIY service failure (DIY SF) influences consumer evaluations of subsequent firm recovery. This occurs because consumers who experience DIY SF gain greater understanding of the task (i.e., learning) through their failed attempt. This learning promotes increased appreciation of the recovering service provider’s ability, ultimately resulting in greater satisfaction with the recovery offering. We further identify mindset as a moderator of this effect, wherein those with a growth mindset are more likely to learn from failure and appreciate the abilities of the recovering service provider. By highlighting DIY SF as a novel form of SF, we demonstrate the importance of understanding customers’ prior experiences with the focal consumption problem and its solution, and of training front-line employees to better manage these customers. We test our theory across four studies using lab and field data, and close by discussing theoretical and managerial implications.","PeriodicalId":48358,"journal":{"name":"Journal of Service Research","volume":"7 1","pages":"578 - 596"},"PeriodicalIF":12.4,"publicationDate":"2022-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87945524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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