Gamified monetary reward designs: Offering certain versus chance-based rewards

2023 | Information Systems Journal | Citations: 0

Authors: Adam, Martin; Reinelt, Annika; Roethke, Konstantin

Abstract: To motivate visitors to engage with websites, e-tailers widely employ monetary r ... Expand

Abstract: To motivate visitors to engage with websites, e-tailers widely employ monetary rewards (e.g., vouchers, discounts) in their website designs. With advances in user interface technologies, many e-tailers have started to offer gamified monetary reward designs (MRDs), which require visitors to earn the monetary reward by playing a game, rather than simply claiming the reward. However, little is known about whether and why gamified MRDs engage visitors compared to their non-gamified counterpart. Even less is known about the effectiveness of gamified MRDs when providing certain or chance-based rewards, in that visitors do or do not know what reward they will gain for successfully performing in the game. Drawing on cognitive evaluation theory, we investigate gamified MRDs with certain or chance-based rewards and contrast them to non-gamified MRDs with certain rewards in user registration systems. Our results from a multi-method approach encompassing the complementary features of a randomised field experiment (N = 651) and a randomised online experiment (N = 330) demonstrate differential effects of the three investigated MRDs on user registration. Visitors encountering either type of gamified MRD are more likely to register than those encountering a non-gamified MRD. Moreover, gamified MRDs with chance-based rewards have the highest likelihood of user registrations. We also show that MRDs have distinct indirect effects on user registration via anticipated experiences of competence and sensation. Overall, the paper offers theoretical insights and practical guidance on how and why gamified MRDs are effective for e-tailers. Collapse

Topics: website electronic mail electronic commerce gamification website design

Methods: experiment randomized field experiment survey experimental task mixed method

Theories: cognitive evaluation theory self determination theory

Too Tired and in Too Good of a Mood to Worry About Privacy: Explaining the Privacy Paradox Through the Lens of Effort Level in Information Processing

2023 | Information Systems Research | Citations: 0

Authors: Alashoor, Tawfiq; Keil, Mark; Smith, H. Jeff; McConnell, Allen R.

Abstract: The confluence of digital transactions, growing cybersecurity threats, and the i ... Expand

Abstract: The confluence of digital transactions, growing cybersecurity threats, and the internet of the future (e.g., web 3.0 and the metaverse) have made information privacy increasingly important to consumers and companies that rely on consumers willingly sharing their personal information. Although information privacy has been of interest to researchers for decades and much has been learned, one thing that perplexes scholars is the privacy paradox, which we define as a mismatch between stated privacy concerns and actual disclosure behaviors. In this paper, we shed light on this phenomenon and show that low-effort information processing triggered by cognitive depletion (Experiment 1), positive mood (Experiment 2), or both (Experiment 3) significantly attenuates the association between stated privacy concerns and disclosure behaviors. These findings do not indicate that individuals do not care about privacy because we find consistent evidence in the three experiments for a significant negative association between stated privacy concerns and disclosure behaviors when individuals have sufficient cognitive capacity (Experiment 1), experience a negative (or neutral) mood (Experiment 2), or have sufficient cognitive capacity coupled with a negative mood state (Experiment 3). Our findings reveal that the paradox is neither an absolute phenomenon nor a myth, but its existence is conditional on contextual factors, including psychological factors related to information processing. We discuss our contribution to privacy theory and provide implications for consumers, companies, and policymakers. History: Alessandro Acquisti, Senior Editor; Idris Adjerid, Associate Editor. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1182. Collapse

Topics: information privacy concern privacy decision making mobile application task productivity

Methods: experiment descriptive statistic statistical hypothesis test theory development Student's t-test

Theories: elaboration likelihood model

D&L: A Natural Language Processing Based Approach for Protecting Sensitive Information from Shoulder Surfing Attacks

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Aldossari, Marran; Zhang, Dongsong

Abstract: Despite the increasing attention and research effort, how to protect sensitive ... Expand

Abstract: Despite the increasing attention and research effort, how to protect sensitive information from shoulder surfing attacks is still under studied. Existing methods for protecting sensitive textual content on users' screens from shoulder surfing attacks have various limitations, including ineffectiveness, insufficient protection of sensitive information, low usability, and high cognitive workload. To address those limitations, this paper proposes, develops, and evaluates a new solution called "detection and labeling" (D&L), which uses NLP techniques to automatically detect and label sensitive information in the textual content. The labeled and hidden sensitive information is then read to users through their headphones upon their clicking a label. Evaluation results demonstrate that D&L improves protection, enhances usability, reduces users' cognitive workload, and allows faster browsing speed compared to the baseline methods. Collapse

Topics: electronic mail laptop computer usability privacy perceived ease of use

Methods: survey parametric test experiment Tukey HSD for post poc analysis ANOVA

Impact of Anxiety on Information Processing Among Young Adults: An Exploratory Eye-tracking Study

2023 | HICSS | Citations: 0

Authors: Alrefaei, Doaa; Sankar, Gaayathri; Norouzi Nia, Javad; Djamasbi, Soussan; Liu, Shichao; Strauss, Sarah; Gbetonmasse, Somasse

Abstract: Anxiety, one of the most common mental illnesses among American adults, is often ... Expand

Abstract: Anxiety, one of the most common mental illnesses among American adults, is often assessed via self-reported measures. While self-reported measures provide an efficient step in capturing health symptoms from patients’ point of view, by their mere nature self-reported measures provide only a narrow interpretation of health symptoms. Capturing eye movements unobtrusively during self-reports, when patients summarize their experience of anxiety by choosing a single option among a set of alternatives, can provide invaluable insight about patients’ information processing and decision behavior. Because anxiety impacts how we attended to information that we use to select options, the objective moment-to-moment eye-movement data can substantially enrich the information that is typically provided by patients as single scores representing their anxiety levels. Supporting this point of view, our results indicate that eye movements may serve as valuable objective information complementing the subjective self-reported anxiety measures to enable more effective assessment and treatment of anxiety. Collapse

Topics: decision making innovation management

Methods: oculometry self reported survey regression analysis method survey experiment

When Reflection Hurts: The Effect of Cognitive Processing Types on Organizational Adaptation to Discontinuous Change

2023 | Organization Science | Citations: 1

Authors: Alves, Marlon Fernandes Rodrigues; Vastola, Vincenzo; Vasconcelos Ribeiro Galina, Simone; Zollo, Maurizio

Abstract: Technological breakthroughs, institutional disruptions, and natural disasters of ... Expand

Abstract: Technological breakthroughs, institutional disruptions, and natural disasters often alter the course of organizations and entire industries. Such discontinuous changes threaten organizations’ survival by affecting the value of the knowledge accumulated in routines and capabilities. Although it is widely acknowledged that managerial cognition is a critical antecedent of organizational responses to discontinuous change, the role of type 1 (intuitive) and type 2 (reflective) processing in the adaptation of shared patterns of behavior, that is, routines, remains understudied. Drawing on dual-process theory, we propose that particular features of type 1 processing render this approach superior to type 2 processing, especially in highly ambiguous environments in which information is limited and difficult to verify. We tested our hypotheses in a longitudinal experiment linking individual-level factors with organizational-level practices of routine adaptation. Experienced managers, paired in 80 groups, developed routines in a first round of a simulation game; in a second round, we then introduced a discontinuous change making previous routines obsolete in order to observe how they adapted. The data show that priming type 1 processing facilitates organizational adaptation more than type 2 processing by providing faster, more routinized, efficiently coordinated, and optimal responses. In addition, type 1 appears to be more functional in highly ambiguous environments, whereas type 1 and type 2 processes yield similar levels of performance under low levels of ambiguity. Overall, our study advances the understanding of the nondeliberative dimension of organizational adaptation to discontinuous change.History: This paper has been accepted for the Organization Science Special Issue on Experiments in Organizational Theory.Funding: This work was supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Finance Code 001).Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2021.1517. Collapse

Topics: decision making strategic management innovation management problem solving task productivity

Methods: experiment longitudinal research parametric test ANOVA statistical hypothesis test

Theories: dual-system theory dynamic capabilities theory

How Does AI-Work Interaction Affect Individuals Behavior? The Role of Appraisals

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Ansari, Kimia; Abouei, Mahdi; Ghasemaghaei, Maryam

Abstract: Artificial intelligence (AI) has become a trend in capturing valuable insights ... Expand

Abstract: Artificial intelligence (AI) has become a trend in capturing valuable insights in organizations that were not achievable previously. Despite the anticipated benefits of implementing AI, the outcomes reveal contradictions. This study elaborates on the role of new AI-work interaction (in terms of augmentation and automation) in such seemingly incongruent results using the transactional theory of stress. Specifically, we propose that depending on individuals' perceived expertise, they may perceive such a new interaction either as inspirational or impeding. Therefore, if individuals appraise new AI-work interactions as possibly harmful and find that they do not have enough expertise to cope efficiently, they will likely withdraw and resist AI use. However, if they appraise the situation as potentially positive and find themselves highly capable of efficiently dealing with it, they will try to increase the effort to meet environmental demands and not resist the use of AI. Collapse

Topics: artificial intelligence organizational change

Methods: survey statistical power analysis survey design experimental task experiment

The Threatening Effect of Invoked Help from Highly Competent Intelligent Agents

2023 | International Conference on Information Systems | Citations: 0

Authors: Adam, Martin; Diebel, Christopher; Goutier, Marc

Abstract: Empowered with artificial intelligence, intelligent agents (IAs) increasingly o ... Expand

Abstract: Empowered with artificial intelligence, intelligent agents (IAs) increasingly offer help not only in response to user prompts (i.e., user-invoked help) but also without user prompts (i.e., IA-invoked help). Additionally, IAs are becoming more competent and even surpassing users in performing many computational and repetitive tasks. Drawing on self-affirmation theory, we investigate users' acceptance of IA-versus user-invoked help for identity-defining tasks from IAs with different levels of relative competence. We conducted an experiment with 199 software developers and found that IA-invoked (vs. user-invoked) help increases self-threat and thus reduces users' willingness to accept help from IAs. Moreover, relative competence moderates this effect, in that only IAs having relatively higher (vs. lower or equal) competence cause self-threat. Our study contributes to a better understanding of the self-threatening effects of IA-invoked (vs. user-invoked) help from IAs and the related role of relative competence that crucially shapes effective user-IA collaborations. Collapse

Topics: software developer user acceptance accounting artificial intelligence systems development

Methods: experiment business process modeling experimental task survey linear regression analysis

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

Decision support tool to define the optimal pool testing strategy for SARS-CoV-2

2023 | Decision Support Systems | Citations: 0

Authors: Barracosa, Bruno; Felício, João; Carvalho, Ana; Moreira, Leonilde M.; Mendes, Filipa; Verde, Sandra Cabo; Pinto-Varela, Tânia

Abstract: This work proposes a holistic decision support tool to be used by diverse stakeh ... Expand

Abstract: This work proposes a holistic decision support tool to be used by diverse stakeholders and decision-makers, integrating experimental data. Considering the characteristics of each region, the tool aids in defining the most suitable testing strategies per country or region: number of tests, time, cost, type of pool strategy based on prevalence rate interval, and the use of centralization vs. decentralization of testing strategies. The tool con­ tributes to the field of decision support systems theoretically by introducing a novel model for testing decisions in extreme situations within a specific region, and practically, by demonstrating how laboratory data can be applied to real-life decision-making in that same region. This tool has been validated with two real case studies (Portugal and France) and has applied, as input data, real experimental results. The results suggested tree- and matrixbased strategies for low and high prevalence rates. A decentralized testing strategy improves the financial and time gains introduced by pool testing; however, centralizing the decisions shows a more straightforward implementation and resource management of the pool testing system with a low-performance loss. Therefore, there might be a solution to consider overcoming operational restrictions. Collapse

Topics: decision support system pandemic

Methods: case study optimization model experiment simulation machine learning

Expl(AI)ned: The Impact of Explainable Artificial Intelligence on Users’ Information Processing

2023 | Information Systems Research | Citations: 0

Authors: Bauer, Kevin; von Zahn, Moritz; Hinz, Oliver

Abstract: Because of a growing number of initiatives and regulations, predictions of moder ... Expand

Abstract: Because of a growing number of initiatives and regulations, predictions of modern artificial intelligence (AI) systems increasingly come with explanations about why they behave the way they do. In this paper, we explore the impact of feature-based explanations on users’ information processing. We designed two complementary empirical studies where participants either made incentivized decisions on their own, with the aid of opaque predictions, or with explained predictions. In Study 1, laypeople engaged in the deliberately abstract investment game task. In Study 2, experts from the real estate industry estimated listing prices for real German apartments. Our results indicate that the provision of feature-based explanations paves the way for AI systems to reshape users’ sense making of information and understanding of the world around them. Specifically, explanations change users’ situational weighting of available information and evoke mental model adjustments. Crucially, mental model adjustments are subject to the confirmation bias so that misconceptions can persist and even accumulate, possibly leading to suboptimal or biased decisions. Additionally, mental model adjustments create spillover effects that alter user behavior in related yet disparate domains. Overall, this paper provides important insights into potential downstream consequences of the broad employment of modern explainable AI methods. In particular, side effects of mental model adjustments present a potential risk of manipulating user behavior, promoting discriminatory inclinations, and increasing noise in decision making. Our findings may inform the refinement of current efforts of companies building AI systems and regulators that aim to mitigate problems associated with the black-box nature of many modern AI systems. History: Alessandro Acquisti, senior editor; Jason Chan, associate editor. Funding: This work was supported by the Deutsche Forschungsgemeinschaft (DFG) (Projek 449023539), Volkswagen Foundation (ML2MT), and LeibnizInstitute for Financial Research SAFE. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2023.1199. Collapse

Topics: explainable artificial intelligence artificial intelligence shapley additive explanation local interpretable model agnostic explanation decision making

Methods: F-test regression analysis method experiment field study robustness check