Brand Crisis and Customer Relationship Management on Social Media: Evidence from a Natural Experiment from the Airline Industry

2023 | Information Systems Research | Citations: 0

Authors: Al Balawi, Ramah; Hu, Yuheng; Qiu, Liangfei

Abstract: In this study, we investigate the effect of a brand crisis on the customer relat ... Expand

Abstract: In this study, we investigate the effect of a brand crisis on the customer relationship management (CRM) efforts of brands on social media. Despite the opportunities social media offers to brands to connect and engage with customers, it is still unclear how a brand crisis can change a brand’s social CRM efforts. Social media platforms can amplify the negative consequences of a brand crisis. However, the unique and public nature of social media platforms can offer new means for brands to handle a brand crisis and publicly manage their customer relationships. Compared with traditional CRM, social media enables brands to communicate with their customers publicly, which can strengthen their relationships with customers and maintain higher levels of customer engagement. Leveraging a natural experiment setting, we investigate the impact of the United Airlines crisis on three dimensions of social CRM efforts: informativeness, timeliness, and attentiveness. Contrary to traditional CRM efforts and recommendations, we find that the brand crisis increases informativeness efforts but reduces timeliness and attentiveness efforts.History: Yong Tan, Senior Editor; Beibei Li, Associate Editor.Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1159. Collapse

Topics: social media Twitter customer service customer satisfaction customer relationship management

Methods: mediation analysis longitudinal research robustness check natural experiment experimental group

Get a Word in Edgewise: Post Character Limit and Social Media-Based Customer Service

2023 | HICSS | Citations: 0

Authors: Al Balawi, Ramah; Hu, Yuheng; Qiu, Liangfei

Abstract: In this paper, we study the role of extending character limits on firm responses ... Expand

Abstract: In this paper, we study the role of extending character limits on firm responses on social media. By leveraging a natural experiment setting: the unexpected increase in post character limit on Twitter, we empirically investigate the impact on the linguistic styles of social media-based customer service responses. Using a Regression Discontinuity in Time Design and leveraging a panel dataset, our results suggest that extending character limits influences firm to change the linguistic styles in their responses which could influence consumers' perceptions. Our results show that extending post-character limits significantly reduces the readability ease of firm responses, on average, while increasing the concreteness and personal closeness scores of these responses, on average. We show that these changes were effective in influencing customer satisfaction. Collapse

Topics: social media Twitter customer service customer satisfaction user experience

Methods: instrumental variables estimation regression discontinuity design descriptive statistic regression analysis method robustness check

Theories: theory of affordance

Designing a conversational AI agent: Framework combining customer experience management, personalization, and AI in service techniques

2023 | HICSS | Citations: 0

Authors: Blümel, J.; Jha, G.

Abstract: Conversational AI agents are fundamentally changing how firms are delivering se ... Expand

Abstract: Conversational AI agents are fundamentally changing how firms are delivering service to their customers. The rapid advancement of technology and ready tools means that deploying a conversational AI agent has become far simpler than ever imagined. However, customers remain unsatisfied with their experience and firms are unable to demonstrate value of conversational AI agents. Drawing on the theoretical notions of customer experience (CX) management, personalization, and AI in service, we develop a framework to design conversational AI agents. We propose a six-stage iterative design for conversational AI agents that begins with sensing customer intent, adapting to journey context, assigning tone to the conversation, delegating to humans, orchestrating processes to service requests and training of AI agents to adaptively improve and loopback into prior design stages. Additionally, we recognize that firms need to holistically qualify and allocate service requests to such conversational AI agents based on the firm's purpose and CX strategy. Collapse

Topics: artificial intelligence usability personalization conversational agent Amazon Alexa

Methods: chatbot natural language processing literature study

Team Performance: Nature and Antecedents of Nonnormal Distributions

2023 | Organization Science | Citations: 0

Authors: Bradley, Kyle J.; Aguinis, Herman

Abstract: Team research typically assumes that team performance is normally distributed: t ... Expand

Abstract: Team research typically assumes that team performance is normally distributed: teams cluster around average performance, performance variability is not substantial, and few teams inhabit the upper range of the distribution. Ironically, although most team research and methodological practices rely on the normality assumption, many theories actually imply nonnormality (e.g., performance spirals, team composition, team learning, punctuated equilibrium). Accordingly, we investigated the nature and antecedents of team performance distributions by relying on 274 performance distributions including 200,825 teams (e.g., sports, politics, firefighters, information technology, customer service) and more than 500,000 workers. First, regarding their overall nature, only 11% of the distributions were normal, star teams are much more prevalent than predicted by normality, the power law with an exponential cutoff is the most dominant distribution among nonnormal distributions (i.e., 73%), and incremental differentiation (i.e., differential performance trajectories across teams) is the best explanation for the emergence of these distributions. Second, this conclusion remained unchanged after examining theory-based boundary conditions (i.e., tournament versus nontournament contexts, performance as aggregation of individual-level performance versus performance as a team-level construct, performance assessed with versus without a hard left-tail zero, and more versus less sample homogeneity). Third, we used the team learning curve literature as a conceptual framework to test hypotheses and found that authority differentiation and lower temporal stability are associated with distributions with larger performance variability (i.e., a greater proportion of star teams). We discuss implications for existing theory, future research directions, and methodological practices (e.g., need to check for nonnormality, Bayesian analysis, outlier management). Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2022.1619. Collapse

Topics: team productivity customer service anonymity systems development virtual teamwork

Methods: meta analysis statistical hypothesis test Bayesian analysis descriptive statistic chi squared test

Artificial Intelligence as a Service: Trade-Offs Impacting Service Design and Selection

2023 | International Conference on Information Systems | Citations: 0

Authors: Brecker, Kathrin; Lins, Sebastian; Trenz, Manuel; Sunyaev, Ali

Abstract: AI as a Service (AIaaS) is a promising path to leverage AI capabilities from th ... Expand

Abstract: AI as a Service (AIaaS) is a promising path to leverage AI capabilities from the cloud. However, there is no one-size-fits-all service, but heterogenous service options since interdependent AIaaS characteristics require trade-offs. We lack knowledge on these tradeoffs and how they result from conflicting characteristics. Therefore, we interviewed 39 AIaaS providers, customers, and consultants to provide rich descriptions of interdependent characteristics and uncover resulting trade-offs and their consequences. This study contributes to a better understanding of the inner functioning and interplay of AIaaS characteristics and discusses how this complex nature of service offerings impacts providers' design and customers' selection decisions. Collapse

Topics: artificial intelligence cloud computing ease of use data protection economies of scale

Methods: qualitative interview design artifact constant comparative analysis qualitative coding triangulation

Theories: SERVQUAL core competency theory

A Theory-Driven Deep Learning Method for Voice Chat–Based Customer Response Prediction

2023 | Information Systems Research | Citations: 0

Authors: Chen, Gang; Xiao, Shuaiyong; Zhang, Chenghong; Zhao, Huimin

Abstract: As artificial intelligence and digitalization technologies are flourishing real- ... Expand

Abstract: As artificial intelligence and digitalization technologies are flourishing real-time, online interaction–based commercial modes, exploiting customers’ purchase intention implied in online interaction processes may foster huge business opportunities. In this study, we target the task of voice chat–based customer response prediction in an emerging online interaction–based commercial mode, the invite-online-and-experience-in-store mode. Prior research shows that satisfaction, which can be revealed by the discrepancy between prior expectation and actual experience, is a key factor to disentangle customers’ purchase intention, whereas black-box deep learning methods empirically promise us advantageous capabilities in dealing with complex voice data, for example, text and audio information incorporated in voice chat. To this end, we propose a theory-driven deep learning method that enables us to (1) learn customers’ personalized product preferences and dynamic satisfaction in the absence of their profile information, (2) model customers’ actual experiences based on multiview voice chat information in an interlaced way, and (3) enhance the customer response prediction performance of a black-box deep learning model with theory-driven dynamic satisfaction. Empirical evaluation results demonstrate the advantageous prediction performance of our proposed method over state-of-the-art deep learning alternatives. Investigation of cumulative satisfaction reveals the collaborative predictive roles of theory-driven dynamic satisfaction and deep representation features for customer response prediction. Explanatory analysis further renders insights into customers’ personalized preferences and dynamic satisfaction for key product attributes. History: Yong Tan, Senior Editor; Jingjing Zhang, Associate Editor. Funding: This work was supported in part by the National Natural Science Foundation of China [Grants 71971067 and 72271059] and the China Postdoctoral Science Foundation [Grant 2022M722394]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1196. Collapse

Topics: customer satisfaction willingness to pay decision making marketing information system customer service

Methods: deep learning experiment machine learning BERT theory development

Introduction to the Minitrack on Crowd-based Platforms

2023 | HICSS | Citations: 0

Authors: Chen, Pei-Yu; Huang, Nina; Gu, Bin

Abstract: ... Expand

Abstract: Collapse

Topics: crowdsourcing crowdfunding platform worker social media online labor market

Methods: difference in differences natural language processing longitudinal research natural experiment machine learning

Theories: signaling theory theory of affordance lemon market theory

The Value of Social Media Customer Support During the Pandemic

2023 | HICSS | Citations: 0

Authors: Cheng, Huai-Tzu; Pan, Yang; Hirschheim, Rudy

Abstract: Many companies are utilizing social media as the primary avenue for customer ser ... Expand

Abstract: Many companies are utilizing social media as the primary avenue for customer service during the pandemic. However, how customers’ behaviors and interactions with customer service agents on social media are impacted by the lockdowns has not been well understood. In this study, we examine the impact of lockdowns and physical distancing on changes in customers’ behaviors, such as emotional expressions in tweets and customers’ satisfaction with social media customer service. Using a difference-in-differences research design, we find that with the lockdowns and physical distancing, customers expressed more negative emotions when tweeting the company they were having issues with. Surprisingly, compared to before the pandemic period, customers’ emotional expressions became more positive and they were more likely to express their satisfaction after interacting with customer service agents. Interestingly, our findings reveal that gender differences exist in these scenarios. We also discuss the theoretical and practical implications of these findings. Collapse

Topics: Twitter customer service social media pandemic customer satisfaction

Methods: experimental group two sample t-test with equal variance propensity score matching parametric test robustness check

Social Media Adoption, Usage And Impact In Business-To-Business (B2B) Context: A State-Of-The-Art Literature Review

2023 | Information Systems Frontiers | Citations: 0

Authors: Dwivedi, Yogesh K.; Ismagilova, Elvira; Rana, Nripendra P.; Raman, Ramakrishnan

Abstract: Social media plays an important part in the digital transformation of businesses ... Expand

Abstract: Social media plays an important part in the digital transformation of businesses. This research provides a comprehensive analysis of the use of social media by business-to-business (B2B) companies. The current study focuses on the number of aspects of social media such as the effect of social media, social media tools, social media use, adoption of social media use and its barriers, social media strategies, and measuring the effectiveness of use of social media. This research provides a valuable synthesis of the relevant literature on social media in B2B context by analysing, performing weight analysis and discussing the key findings from existing research on social media. The findings of this study can be used as an informative framework on social media for both, academic and practitioners. Collapse

Topics: social media business-to-business business-to-consumer marketing management Facebook

Methods: survey qualitative content analysis literature study regression analysis method personal interview

Theories: technology acceptance model motivation theory theory of task technology fit resource based view of the firm

The Power of Identity Cues in Text-Based Customer Service: Evidence from Twitter

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Gao, Yang; Rui, Huaxia; Sun, Shujing

Abstract: Text-based customer service is emerging as an important channel through which c ... Expand

Abstract: Text-based customer service is emerging as an important channel through which companies can assist customers. However, the use of few identity cues may cause customers to feel limited social presence and even suspect the human identity of agents, especially in the current age of advanced algorithms. Does such a lack of social presence affect service interactions? We studied this timely question by evaluating the impact of customers' perceived social presence on service outcomes and customers' attitudes toward agents. Our identification strategy hinged on Southwest Airlines' sudden requirement to include a first name in response to service requests on Twitter, which enhanced customers' perceived level of social presence. This change led customers to become more willing to engage and more likely to reach a resolution upon engagement. We further conducted a randomized experiment to understand the underlying mechanisms. We found that the effects were mainly driven by customers who were ex ante uncertain or suspicious about the human identity of agents, and the presence of identity cues improved service outcomes by enhancing customers' perceived levels of trust and empathy. Additionally, we found no evidence of elevated verbal aggression from customers toward agents with identity cues, although a mechanism test revealed the moderating role of customers' emotional states. Our study highlights the importance of social presence in text-based customer service and suggests a readily available and almost costless strategy for firms: signal humanization through identity cues. Collapse

Topics: customer service Twitter social presence social media customer satisfaction

Methods: experiment computational algorithm synthetic control method survey support vector machine

Theories: social presence theory big five model