Human vs. Automated Sales Agents: How and Why Customer Responses Shift Across Sales Stages

2023 | Information Systems Research | Citations: 0

Authors: Adam, Martin; Roethke, Konstantin; Benlian, Alexander

Abstract: Customers in sales processes increasingly encounter automated sales agents (ASAs ... Expand

Abstract: Customers in sales processes increasingly encounter automated sales agents (ASAs) that complement or replace human sales agents (HSAs). Yet, little is known about whether, how, and why customers respond to ASAs in contrast to HSAs across successive decision stages of the same sales process. Even less is known about customer responses to HSA-ASA combinations, where both agents assume distinct roles and focus on complementary tasks that are traditionally performed by only one single sales agent. Against this backdrop, this paper explores the influence of increasingly common sales representative (rep) types (i.e., ASA, HSA, and HSA-ASA) on customer decisions across sales stages. Drawing on information processing theory and the literature on hedonic-utilitarian decision making, we investigate customer responses to text-based ASAs from vendor companies in two common early stages of email sales processes (i.e., sales initiation stages) when customers successively decide whether to indicate their initial interest in an offer and then, whether to provide their contact information. Specifically, we conducted two complementary multi-decision experiments, namely (1) a randomized field experiment in a high-stakes sales initiation setting (n = 325) and (2) a subsequent randomized online experiment to complement the real-world insights (n = 408). Our core findings reveal reversing effect patterns of sales rep types across stages: although customers are more likely to indicate their initial interest to HSAs (versus ASAs) because of HSAs’ higher levels of social presence, they are less likely to provide contact information to HSAs because of HSAs’ lower levels of performance expectancy and effort expectancy. We also show that HSA-ASA combinations can be reasonable options for single ASAs, yet contextual features of the sales setting may affect differential customer responses to HSA-ASA combinations (versus ASAs) in each sales stage. Taken together, we uncover shifting effect patterns in customer responses to sales rep types across successive sales stages and shed light on the consecutive underlying mechanisms that explain these shifts. These findings have significant implications for vendor companies seeking to allocate HSAs and/or ASAs effectively across various decision stages in sales processes and beyond. History: Wonseok Oh, Senior Editor and Khim Yong Goh; Associate Editor. Funding: This work was supported by the Center for Responsible Digitality (ZEVEDI) and the German Research Foundation (DFG) [Grant 471168026]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1171. Collapse

Topics: electronic mail social presence decision making recommender system information system use

Methods: experiment randomized field experiment regression analysis method experimental task field experiment

Theories: information processing theory

“Collaborating” with AI: Taking a System View to Explore the Future of Work

2023 | Organization Science | Citations: 0

Authors: Anthony, Callen; Bechky, Beth A.; Fayard, Anne-Laure

Abstract: In the wake of media hype about artificial intelligence (AI)/human collaboration ... Expand

Abstract: In the wake of media hype about artificial intelligence (AI)/human collaboration, organizations are investing considerable resources into developing and using AI. In this paper, we draw on theories of technology in organizations to frame new directions for the study of what it means to work “with” AI. Drawing on prior literature, we consider how interactions between users and AI might unfold through theoretical lenses which cast technology as a tool and as a medium. Reflecting on how AI technologies diverge from technologies studied in the past, we propose a new perspective, which considers technology as a counterpart in a system of work that includes its design, implementation, and use. This perspective encourages developing a grounded understanding of how AI intersects with work, and therefore ethnography, building on thick descriptions, is an apt approach. We argue that relational ethnographic approaches can assist organization theorists in navigating the methodological challenges of taking a counterpart perspective and propose several strategies for future research. Collapse

Topics: artificial intelligence spreadsheet human AI collaboration human computer interaction information system use

Methods: ethnography computational algorithm qualitative interview experiment theory development

Theories: theory of distributed cognition actor network theory

The Evaluation of Founder Failure and Success by Hiring Firms: A Field Experiment

2023 | Organization Science | Citations: 0

Authors: Botelho, Tristan L.; Chang, Melody

Abstract: Organizations tout the importance of innovation and entrepreneurship. Yet, when ... Expand

Abstract: Organizations tout the importance of innovation and entrepreneurship. Yet, when hiring it remains unclear how they evaluate entrepreneurial human capital—namely, job candidates with founder experience. How hiring firms evaluate this experience—and especially how this evaluation varies by entrepreneurial success and failure—reveals insights into the structures and processes within organizations. Organizations research points to two perspectives related to the evaluation of founder experience: Former founders may be advantaged, due to founder experience signaling high-quality capabilities and human capital, or disadvantaged, due to concerns related to fit and commitment. To identify the dominant class of mechanisms driving the evaluation of founder experience, it is important to consider how these evaluations differ, depending on whether the founder’s venture failed or succeeded. To isolate demand-side mechanisms and hold supply-side factors constant, we conducted a field experiment. We sent applications varying the candidate’s founder experience to 2,400 software engineering positions in the United States at random. We find that former founders received 43% fewer callbacks than nonfounders and that this difference is driven by older hiring firms. Further, this founder penalty is greatest for former successful founders, who received 33% fewer callbacks than former failed founders. Our results highlight that mechanisms related to concerns about fit and commitment, rather than information asymmetry about quality, are most influential when hiring firms evaluate former founders in our context. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2022.1592. Collapse

Topics: IT career human resource management entrepreneurship systems development innovation management

Methods: qualitative interview field experiment theory development experiment experimental design

Theories: lemon market theory

Organizing Entrepreneurial Teams: A Field Experiment on Autonomy over Choosing Teams and Ideas

2023 | Organization Science | Citations: 1

Authors: Boss, Viktoria; Dahlander, Linus; Ihl, Christoph; Jayaraman, Rajshri

Abstract: Scholars have suggested that autonomy can lead to better entrepreneurial team pe ... Expand

Abstract: Scholars have suggested that autonomy can lead to better entrepreneurial team performance. Yet, there are different types of autonomy, and they come at a cost. We shed light on whether two fundamental organizational design choices—granting teams autonomy to (1) choose project ideas to work on and (2) choose team members to work with—affect performance. We run a field experiment involving 939 students in a lean startup entrepreneurship course over 11 weeks. The aim is to disentangle the separate and joint effects of granting autonomy over choosing teams and choosing ideas compared with a baseline treatment with preassigned ideas and team members. We find that teams with autonomy over choosing either ideas or team members outperform teams in the baseline treatment as measured by pitch deck performance. The effect of choosing ideas is significantly stronger than the effect of choosing teams. However, the performance gains vanish for teams that are granted full autonomy over choosing both ideas and teams. This suggests the two forms of autonomy are substitutes. Causal mediation analysis reveals that the main effects of choosing ideas or teams can be partly explained by a better match of ideas with team members’ interests and prior network contacts among team members, respectively. Although homophily and lack of team diversity cannot explain the performance drop among teams with full autonomy, our results suggest that self-selected teams fall prey to overconfidence and complacency too early to fully exploit the potential of their chosen idea. We discuss the implications of these findings for research on organizational design, autonomy, and innovation.History: This paper has been accepted for the Organization Science Special Issue on Experiments in Organizational Theory.Funding: The authors are grateful for funding from the Innovation Growth Laboratory, the Peter Curtius Foundation, and the Deutsche Forschungsgemeinschaft [Grant CRC TRR 190].Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2021.1520. Collapse

Topics: team productivity entrepreneurship missing data intrinsic motivation evaluation criteria

Methods: experiment experimental group survey causal mediation analysis field experiment

Artificial Intelligence (AI) Student Assistants in the Classroom: Designing Chatbots to Support Student Success

2023 | Information Systems Frontiers | Citations: 1

Authors: Chen, Yu; Jensen, Scott; Albert, Leslie J.; Gupta, Sambhav; Lee, Terri

Abstract: In higher education, low teacher-student ratios can make it difficult for studen ... Expand

Abstract: In higher education, low teacher-student ratios can make it difficult for students to receive immediate and interactive help. Chatbots, increasingly used in various scenarios such as customer service, work productivity, and healthcare, might be one way of helping instructors better meet student needs. However, few empirical studies in the field of Information Systems (IS) have investigated pedagogical chatbot efficacy in higher education and fewer still discuss their potential challenges and drawbacks. In this research we address this gap in the IS literature by exploring the opportunities, challenges, efficacy, and ethical concerns of using chatbots as pedagogical tools in business education. In this two study project, we conducted a chatbot-guided interview with 215 undergraduate students to understand student attitudes regarding the potential benefits and challenges of using chatbots as intelligent student assistants. Our findings revealed the potential for chatbots to help students learn basic content in a responsive, interactive, and confidential way. Findings also provided insights into student learning needs which we then used to design and develop a new, experimental chatbot assistant to teach basic AI concepts to 195 students. Results of this second study suggest chatbots can be engaging and responsive conversational learning tools for teaching basic concepts and for providing educational resources. Herein, we provide the results of both studies and discuss possible promising opportunities and ethical implications of using chatbots to support inclusive learning. Collapse

Topics: artificial intelligence IT career anonymity user experience accounting

Methods: chatbot survey qualitative interview personal interview machine learning

A Vietnamese Handwritten Text Recognition Pipeline for Tetanus Medical Records

2023 | International Conference on Information Systems | Citations: 0

Authors: Dinh, Minh; Le, Mau Toan; Bui, Triet; Mai, Minh; Tran, Long; Nguyen, Nhan; Vo, Tan Hoang

Abstract: Machine learning techniques are successful for optical character recognition ta ... Expand

Abstract: Machine learning techniques are successful for optical character recognition tasks, especially in recognizing handwriting. However, recognizing Vietnamese handwriting is challenging with the presence of extra six distinctive tonal symbols and vowels. Such a challenge is amplified given the handwriting of health workers in an emergency care setting, where staff is under constant pressure to record the well-being of patients. In this study, we aim to digitize the handwriting of Vietnamese health workers. We develop a complete handwritten text recognition pipeline that receives scanned documents, detects, and enhances the handwriting text areas of interest, transcribes the images into computer text, and finally auto-corrects invalid words and terms to achieve high accuracy. From experiments with medical documents written by 30 doctors and nurses from the Tetanus Emergency Care unit at the Hospital for Tropical Diseases, we obtain promising results of 2% and 12% for Character Error Rate and Word Error Rate, respectively. Collapse

Topics: healthcare data digital transformation database system mobile system data security

Methods: experiment image processing deep learning computational algorithm machine learning

Moral Foundations, Himpathy, and Punishment Following Organizational Sexual Misconduct Allegations

2023 | Organization Science | Citations: 0

Authors: Dodson, Samantha J.; Goodwin, Rachael D.; Graham, Jesse; Diekmann, Kristina A.

Abstract: We build on deontic justice and moral foundations theories to shed light on resp ... Expand

Abstract: We build on deontic justice and moral foundations theories to shed light on responses to sexual misconduct at work by proposing a model that explains why some third parties punish accusing victims and support alleged perpetrators. We theorize that when third parties are given conflicting he-said, she-said information, they intuitively evaluate organizational injustice based on moral values. We further theorize that binding moral foundations (loyalty, authority, purity) give rise to sympathy toward men accused of sexual misconduct and anger toward female accusers. Across five studies (total n = 5,413) utilizing archival, field, and vignette designs, we examined third-party responses to sexual misconduct accusations ranging in severity across several industries. Third-party endorsement of binding moral foundations was linked to increased perpetrator-directed sympathy and victim-directed anger (Studies 1–4). These emotions jointly mediated the relationship between binding values and credibility perceptions of the accusing victim and the alleged perpetrator (Studies 2 and 3). Moreover, victim credibility was negatively associated with social sanctions and punishment severity levied toward the accusing victim, and perpetrator credibility was negatively associated with the same punishment outcomes for the alleged perpetrator (Studies 3 and 4). In Study 5, we found that managers framing the accusing victim as disloyal exacerbated negative judgments and emotions toward the victim and positive judgments and emotions toward the perpetrator for individuals who highly ascribe to binding moral foundations. We discuss the theoretical contributions and practical implications of moral concerns on third parties’ emotions, judgments, and motivations to punish actors involved in sexual misconduct allegations. Supplemental Material: The e-companion is available at https://doi.org/10.1287/orsc.2022.1652. Collapse

Topics: Twitter electronic mail social media IT career Facebook

Methods: theory development survey statistical hypothesis test structural equation modeling mediation analysis

Theories: justice theory big five model organizational behavior theory

Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy

2023 | International Journal of Information Management | Citations: 0

Authors: Dwivedi, Yogesh K.; Kshetri, Nir; Hughes, Laurie; Slade, Emma Louise; Jeyaraj, Anand; Kar, Arpan Kumar; Baabdullah, Abdullah M.; Koohang, Alex; Raghavan, Vishnupriya; Ahuja, Manju; Albanna, Hanaa; Albashrawi, Mousa Ahmad; Al-Busaidi, Adil S.; Balakrishnan, Janarthanan; Barlette, Yves; Basu, Sriparna; Bose, Indranil; Brooks, Laurence; Buhalis, Dimitrios; Carter, Lemuria; Chowdhury, Soumyadeb; Crick, Tom; Cunningham, Scott W.; Davies, Gareth H.; Davison, Robert M.; Dé, Rahul; Dennehy, Denis; Duan, Yanqing; Dubey, Rameshwar; Dwivedi, Rohita; Edwards, John S.; Flavián, Carlos; Gauld, Robin; Grover, Varun; Hu, Mei-Chih; Janssen, Marijn; Jones, Paul; Junglas, Iris; Khorana, Sangeeta; Kraus, Sascha; Larsen, Kai R.; Latreille, Paul; Laumer, Sven; Malik, F. Tegwen; Mardani, Abbas; Mariani, Marcello; Mithas, Sunil; Mogaji, Emmanuel; Nord, Jeretta Horn; O’Connor, Siobhan; Okumus, Fevzi; Pagani, Margherita; Pandey, Neeraj; Papagiannidis, Savvas; Pappas, Ilias O.; Pathak, Nishith; Pries-Heje, Jan; Raman, Ramakrishnan; Rana, Nripendra P.; Rehm, Sven-Volker; Ribeiro-Navarrete, Samuel; Richter, Alexander; Rowe, Frantz; Sarker, Suprateek; Stahl, Bernd Carsten; Tiwari, Manoj Kumar; Van Der Aalst, Wil; Venkatesh, Viswanath; Viglia, Giampaolo; Wade, Michael; Walton, Paul; Wirtz, Jochen; Wright, Ryan

Abstract: ☆ This editorial opinion paper provides a subjective viewpoint on the potential ... Expand

Abstract: ☆ This editorial opinion paper provides a subjective viewpoint on the potential impact of generative AI technologies such as ChatGPT in the domains of education, business, and society. Its objective is to offer initial guidance on the opportunities, challenges, and implications associated with these technologies. It is worth noting that, given its nature as an editorial opinion piece, this submission has not undergone a formal double-blind review process but has been reviewed informally by appropriate experts.Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT's capabilities to enhance productivity and suggest that it is likely to offer 1 Equal contributions Y.K. Dwivedi et al.International Journal of Information Management 71 (2023) 102642 3 significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT's use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts. Collapse

Topics: artificial intelligence generative AI Twitter privacy innovation management

Methods: chatGPT chatbot generative pre-trained transformer literature study language model

Tagging the Fakes? - The Impact of Tagging Fake Reviews on Consumer Trust and Purchase Intention

2023 | European Conference On Information Systems | Citations: 0

Authors: Erlebach, Stefanie; Kupfer, Alexander; Wrabel, Andrea; Zimmermann, Steffen

Abstract: The manipulation of online consumer reviews (OCRs) became a global phenomenon. ... Expand

Abstract: The manipulation of online consumer reviews (OCRs) became a global phenomenon. Although policymakers require platforms to delete OCRs that are identified as fake, they also urge to consider the principle of freedom of expression before deleting content. Since fake OCR detection algorithms are imperfect, platforms must either balance preventive deletion with preserving freedom of expression, or they can simply tag OCRs that are potentially fake. In this paper, we examine the consequences of the latter. With an incentive-compatible experiment, we observe that tagging fake OCRs harms relevant consumer trust dimensions. Contrary to our expectations, tags do not serve as trust-increasing signals for the platform as a signal provider. They do, however, not only directly harm consumers' trust in OCRs and reviewers, but also indirectly via the activation of consumers' persuasion knowledge. Although consumer trust decreases, we find no negative economic consequences for the platform from tagging potential fake OCRs. Collapse

Topics: electronic commerce data quality digital platform digital service marketing management

Methods: experiment mediation analysis theoretical contribution Student's t-test statistical power analysis

Theories: signaling theory

A Comparison of Methods for Treatment Assignment with an Application to Playlist Generation

2023 | Information Systems Research | Citations: 0

Authors: Fernández-Loría, Carlos; Provost, Foster; Anderton, Jesse; Carterette, Benjamin; Chandar, Praveen

Abstract: This study presents a systematic comparison of methods for individual treatment ... Expand

Abstract: This study presents a systematic comparison of methods for individual treatment assignment, a general problem that arises in many applications and that has received significant attention from economists, computer scientists, and social scientists. We group the various methods proposed in the literature into three general classes of algorithms (or metalearners): learning models to predict outcomes (the O-learner), learning models to predict causal effects (the E-learner), and learning models to predict optimal treatment assignments (the A-learner). We compare the metalearners in terms of (1) their level of generality and (2) the objective function they use to learn models from data; we then discuss the implications that these characteristics have for modeling and decision making. Notably, we demonstrate analytically and empirically that optimizing for the prediction of outcomes or causal effects is not the same as optimizing for treatment assignments, suggesting that, in general, the A-learner should lead to better treatment assignments than the other metalearners. We demonstrate the practical implications of our findings in the context of choosing, for each user, the best algorithm for playlist generation in order to optimize engagement. This is the first comparison of the three different metalearners on a real-world application at scale (based on more than half a billion individual treatment assignments). In addition to supporting our analytical findings, the results show how large A/B tests can provide substantial value for learning treatment-assignment policies, rather than simply for choosing the variant that performs best on average.History: Olivia Liu Sheng, Senior Editor; Gautam Pant, Associate Editor.Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1149. Collapse

Topics: decision making digital service music streaming service Spotify marketing management

Methods: meta learning computational algorithm machine learning experiment decision tree classification