Results

Found 148 articles

Clear filters

Does Pre-login Search Matter? Evidence from a Mobile Commerce Platform

2023 | HICSS | Citations: 0
Authors: He, Qiaodan; Zhang, Luna; Zhang, Dawei; Yao, Yuliang

Abstract: An increasing number of consumers enjoy shopping through mobile devices. When co ... Expand

Abstract: An increasing number of consumers enjoy shopping through mobile devices. When consumers use a mobile app, they can choose whether to log in with their accounts. We argue that pre-login search plays a critical role in affecting consumers’ purchase decisions, although it has largely been overlooked in the literature. Using clickstream data, we adopt different econometric models to examine whether and how pre-login search affects the likelihood of purchase. Our results show that pre-login search behaviors are as important as post-login search to consumers’ purchase decisions. We also demonstrate that consumers’ purchase propensity increases at a diminishing rate with an increasing search effort during both pre- and post-login periods. Based on recommender systems (RSs) and paradox of choice theory, our results contribute to the burgeoning literature on consumer behavior in mobile commerce and provide novel insights to the strategic usage of RSs. Finally, we discuss theoretical and managerial implications. Collapse

Semantic filters: decision theory

Topics: mobile business mobile application recommender system consumer behavior mobile system

Methods: econometric modeling Student's t-test theory development

Theories: decision theory general systems theory
Reputation Spillover from Agencies on Online Platforms: Evidence from the Entertainment Industry

2023 | Management Information Systems Quarterly | Citations: 0
Authors: Kokkodis, Marios; Adamopoulos, Panagiotis; Ransbotham, Sam

Abstract: Digital markets have proliferated in recent years, overcoming many market ineff ... Expand

Abstract: Digital markets have proliferated in recent years, overcoming many market inefficiencies by facilitating direct interactions between consumers and creators. Thanks to this disintermediation, consumers now have access to a vast number of alternatives, while creators can efficiently reach huge markets. However, the success of digital markets has created a concomitant challenge for creators: differentiation. In crowded markets, agencies (e.g., publishing companies in books, freelance agencies in online labor markets, independent labels in music) can differentiate creators by signaling product quality. But how do agencies' reputations affect product success for creators? Can some agencies do more harm than good? To investigate these research questions, we theorize how variation in creator and agency reputation leads to asymmetric and heterogeneous effects, namely that (1) more reputable agencies have a stronger positive effect on less reputable creators than they have on more reputable creators, and (2) less reputable agencies hurt more reputable creators more than they hurt less reputable ones. Analyses of more than one million observations from two digital markets provide empirical support for these theory-driven arguments. The findings have design implications for markets and contribute to our understanding of how agencies, depending on creator reputation, can either benefit or hurt product success.Marios Kokkodis is a machine learning scientist working on personalization, recommender systems, and markets. His work appears in major information systems, data mining, and multidisciplinary outlets. He has received multiple awards and nominations, including the INFORMS ISS Gordon B. Davis Young Scholar Award, the INFORMS ISS Nunamaker-Chen dissertation award, the NYU Stern Harold W. MacDowell award, and the LinkedIn economic graph challenge award. Collapse

Semantic filters: decision theory

Topics: electronic market social media Google+ digital platform marketing management

Methods: theory development econometric modeling descriptive statistic gradient boosting machine learning

Theories: cognitive theory expectation disconfirmation theory decision theory contrast theory assimilation theory of learning
Will they take this offer? A machine learning price elasticity model for predicting upselling acceptance of premium airline seating
PDF
2023 | Information & Management | Citations: 0
Authors: Thirumuruganathan, Saravanan; Emadi, Noora Al; Jung, Soon-gyo; Salminen, Joni; Robillos, Dianne Ramirez; Jansen, Bernard J.

Abstract: Employing customer information from one of the world’s largest airline companies ... Expand

Abstract: Employing customer information from one of the world’s largest airline companies, we develop a price elasticity model (PREM) using machine learning to identify customers likely to purchase an upgrade offer from economy to premium class and predict a customer’s acceptable price range. A simulation of 64.3 million flight bookings and 14.1 million email offers over three years mirroring actual data indicates that PREM implementation results in approximately 1.12 million (7.94%) fewer non-relevant customer email messages, a predicted increase of 72,200 (37.2%) offers accepted, and an estimated $72.2 million (37.2%) of increased revenue. Our results illustrate the potential of automated pricing information and targeting marketing messages for upselling acceptance. We also identified three customer segments: (1) Never Upgrades are those who never take the upgrade offer, (2) Upgrade Lovers are those who generally upgrade, and (3) Upgrade Lover Lookalikes have no historical record but fit the profile of those that tend to upgrade. We discuss the implications for airline companies and related travel and tourism industries. Collapse

Semantic filters: decision theory

Topics: electronic mail marketing management price management customer satisfaction application programming interface

Methods: autoencoder data transformation machine learning experiment principal component analysis

Theories: optimization theory decision theory
Survey and Systematization of Prescriptive Analytics Systems: Towards Archetypes from a Human-Machine-Collaboration Perspective

2023 | European Conference On Information Systems | Citations: 0
Authors: Wissuchek, Christopher; Zschech, Patrick

Abstract: Prescriptive analytics systems (PAS) represent the most mature form of business ... Expand

Abstract: Prescriptive analytics systems (PAS) represent the most mature form of business analytics (BA), offering advanced decision support. However, current research predominantly focuses on technical facets while neglecting social-technical design aspects. A pluralist research methodology was employed to address this gap, starting with a systematic literature review of over 200 papers. We used these papers to derive a concept matrix of fundamental elements that guided the development of a taxonomy conceptualizing the interplay and collaboration between humans and machines in PAS-based decision-making processes. Based on this taxonomy, we identified four recurring PAS archetypes with salient design characteristics: Informative, executive, adaptive, and autonomous PAS. Our findings have important implications for the BA community, including the need to investigate design options for executive, adaptive, and autonomous PAS; the underrepresentation of the human perspective; the missing links to the broader organizational landscape; and the potential for interpretable machine learning and reinforcement learning in PAS. Collapse

Semantic filters: decision theory

Topics: decision making mobile system internet of things organizational value explainable artificial intelligence

Methods: literature study structured literature research literature sample survey design principle

Theories: decision theory
New theorizing for organizational decision-making with AI as an agent

2023 | Americas Conference on Information Systems | Citations: 0
Authors: Kim, Eunyoung; Marakas, George

Abstract: ... Expand

Abstract: Collapse

Semantic filters: decision theory

Topics: artificial intelligence decision making task characteristic

Methods: theory development theoretical contribution

Theories: decision theory theory of bounded rationality
BLOCKCHAIN-BASED DATA SHARING SYSTEM: AN EXPERIMENTAL ANALYSIS OF BEHAVIOURAL FEATURES AFFECTING INTER-ORGANISATIONAL COOPERATION

2022 | European Conference On Information Systems | Citations: 0
Authors: Ballatore, Marta; Toumi, Dr Mira; Arena, Lise

Abstract: This paper investigates the role of managers' behavioural features on Blockchai ... Expand

Abstract: This paper investigates the role of managers' behavioural features on Blockchain Technology (BCT) appropriation within an online scenario-based behavioural experiment. At the intersection betweenManagement of Information Systems and Experimental Economics, the scenario describes a BCT setup as a new governance mechanism facilitating data sharing among organisations. The experiment involves BCT experts who performed two different Public Good Games (PGG): the first, reflecting a traditional data sharing system while the second was concerned with an exogenous minimum contribution level representing a BCT-based system. Results reveal that experts' beliefs play a positive role in BCT early appropriation at an ecosystem level. When BCT-based data-sharing is established, and information about others' cooperation is available, early appropriation level still affects the managers' contribution and relative appropriation, moderating the role of behavioural features, such as beliefs and generalised trust.1 Xceed project is an inter-organisational collaborative project around connected vehicles involving Renault Group and its major automotive industry partners like Continental, Faurecia, Plastic Omnium, Saint-Gobain. The project aims to adopt a distributed ledger as a unique "source of truth" from vehicle design to production -XCEED project launched in 2019. A blockchain-based record-keeping would provide reliable information on each subcomponent that boosts regulatory conformity in the automotive industry. The distributed ledger is seen as the basis for a trusted network for sharing data and information. This project significantly inspired the design of our experiment. In fact, it exemplifies a BCT consortium which, contrarily to private blockchains, operates under the leadership of a group of entities allowing collaborative business transformation among organizations (Dib et a., 2018). In our context, the consortium structure helps newly arrived organizations in a BCT ecosystem to directly share information and to help all participants to find solutions together and save development costs. Collapse

Semantic filters: decision theory

Topics: blockchain public good decision making privacy mobile system

Methods: experiment qualitative interview survey Mann Whitney U test nonparametric test

Theories: behavioral economics health belief model technology acceptance model decision theory theory of bounded rationality
A Probabilistic Perspective of Human-Machine Interaction
PDF
2022 | HICSS | Citations: 0
Authors: Canan, Mustafa; Demir, Mustafa; Kovacic, Samual

Abstract: Human-machine interaction (HMI) has become an essential part of the daily routin ... Expand

Abstract: Human-machine interaction (HMI) has become an essential part of the daily routine in organizations. Although the machines are designed with state-of-theart Artificial Intelligence applications, they are limited in their ability to mimic human behavior. The humanhuman interaction occurs between two or more humans; when a machine replaces a human, the interaction dynamics are not the same. The results indicate that a machine that interacts with a human can increase the mental uncertainty that a human experiences. Developments in decision sciences indicate that using quantum probability theory (QPT) improves the understanding of human decisionmaking than merely using classical probability theory (CPT). In this paper, we examine the HMI from a QPT perspective. Applying QPT to studying HMI for decision-making shows improvement in understanding the decision process when interacting with machines because it provides insights into the mental uncertainty of a human that is not apparent in CPT. Collapse

Semantic filters: decision theory

Topics: web page decision making search engine human computer interaction usability

Methods: case study experiment

Theories: probability theory decision theory
The Empirical Reality of IT Project Cost Overruns: Discovering A Power-Law Distribution
PDF
2022 | Journal of Management Information Systems | Citations: 0
Authors: Flyvbjerg, Bent; Budzier, Alexander; Lee, Jong Seok; Keil, Mark; Lunn, Daniel; Bester, Dirk W.

Abstract: If managers assume a normal or near-normal distribution of Information Technolog ... Expand

Abstract: If managers assume a normal or near-normal distribution of Information Technology (IT) project cost overruns, as is common, and cost overruns can be shown to follow a power-law distribution, managers may be unwittingly exposing their organizations to extreme risk by severely underestimating the probability of large cost overruns. In this research, we collect and analyze a large sample comprised of 5,392 IT projects to empirically examine the probability distribution of IT project cost overruns. Further, we propose and examine a mechanism that can explain such a distribution. Our results reveal that IT projects are far riskier in terms of cost than normally assumed by decision makers and scholars. Specifically, we found that IT project cost overruns follow a power-law distribution in which there are a large number of projects with relatively small overruns and a fat tail that includes a smaller number of projects with extreme overruns. A possible generative mechanism for the identified power-law distribution is found in interdependencies among technological components in IT systems. We propose and demonstrate, through computer simulation, that a problem in a single technological component can lead to chain reactions in which other interdependent components are affected, causing substantial overruns. What the power law tells us is that extreme IT project cost overruns will occur and that the prevalence of these will be grossly underestimated if managers assume that overruns follow a normal or near-normal distribution. This underscores the importance of realistically assessing and mitigating the cost risk of new IT projects up front. Collapse

Semantic filters: decision theory

Topics: IT project project management project productivity IT project management systems development

Methods: simulation descriptive statistic theory development longitudinal survey longitudinal research

Theories: decision theory
Let’s Team Up with AI! Toward a Hybrid Intelligence System for Online Customer Service
PDF
2022 | DESRIST | Citations: 0
Authors: Poser, Mathis; Wiethof, Christina; Banerjee, Debayan; Shankar Subramanian, Varun; Paucar, Richard; Bittner, Eva A. C.

Abstract: Customers desire convenient, fast, and personalized service encounters. Hence, s ... Expand

Abstract: Customers desire convenient, fast, and personalized service encounters. Hence, service companies deploy self-service technology for online customer service. However, as solutions based on Artificial Intelligence cannot reliably answer the full range of requests and the demands on service employees (SEs) in live chat interaction are high, Hybrid Intelligence Systems (HIS) provide great potential to overcome current pitfalls by combining the complementary strengths of artificial and human intelligence. To ensure optimal performance of this socio-technical ensemble, human-centered design approaches are needed to realize real-time augmentation of decision-making in chat-based service encounters. Following a Design Science Research approach, we generate theory-based design principles (DPs) and implement them in a web-based HIS prototype. We contribute to Hybrid Intelligence research with results showing that the DPs enable task mastery and decision efficiency and provide avenues for future research. Collapse

Semantic filters: decision theory

Topics: decision making usability user interface information system use artificial intelligence

Methods: design artifact qualitative interview design science usage data qualitative content analysis

Theories: self determination theory decision theory cognitive load theory social response theory
Dynamic Reference Point-Oriented Consensus Mechanism in Linguistic Distribution Group Decision Making Restricted by Quantum Integration of Information
PDF
2022 | Group Decision and Negotiation | Citations: 0
Authors: Tan, Xiao; Zhu, Jianjun; Wu, Tong

Abstract: We present a consensus improvement mechanism based on prospect theory and quantu ... Expand

Abstract: We present a consensus improvement mechanism based on prospect theory and quantum probability theory (QPT) that enables the manifestation of irrational and uncertain behaviors of decision makers (DMs) in linguistic distribution group decision making. In this framework, the DMs pursue the possibility of working with different partial agreements on prospect values. Considering that the reference information should be comprehensive and accurate as it guides information modification and affects consensus efficiency, objective and subjective information is integrated to obtain the information. Several studies have verified that the interference effect will occur when the brain beliefs flow towards the different decision classification paths. To address this problem, QPT is introduced into the information integration and the optimized value of the interference term can be acquired by the designed multi-objective programming model based on the maximum individual utility. Finally, as the reference point changes during the preference adjustment process, a dynamic reference point-oriented consensus model is constructed to obtain the optimized modification. A case study is performed on the emergency plan for the selection of designated hospitals, and comparative analyses are performed to demonstrate the feasibility and advantages of the proposed model. Several important insights are offered to simulate the most likely possibility of consciousness flowing into different decision classifications for DMs and moderators. Collapse

Semantic filters: decision theory

Topics: decision making user attitude

Methods: multi objective optimization trade-off analysis method optimization model cross sectional research computational algorithm

Theories: decision theory behavioral theory prospect theory probability theory

Page 1 of 15 Next

AIS11

Journals

Conferences

Semantic Filter

Found 148 articles

Does Pre-login Search Matter? Evidence from a Mobile Commerce Platform

Reputation Spillover from Agencies on Online Platforms: Evidence from the Entertainment Industry

Will they take this offer? A machine learning price elasticity model for predicting upselling acceptance of premium airline seating

Survey and Systematization of Prescriptive Analytics Systems: Towards Archetypes from a Human-Machine-Collaboration Perspective

New theorizing for organizational decision-making with AI as an agent

BLOCKCHAIN-BASED DATA SHARING SYSTEM: AN EXPERIMENTAL ANALYSIS OF BEHAVIOURAL FEATURES AFFECTING INTER-ORGANISATIONAL COOPERATION

A Probabilistic Perspective of Human-Machine Interaction

The Empirical Reality of IT Project Cost Overruns: Discovering A Power-Law Distribution

Let’s Team Up with AI! Toward a Hybrid Intelligence System for Online Customer Service

Dynamic Reference Point-Oriented Consensus Mechanism in Linguistic Distribution Group Decision Making Restricted by Quantum Integration of Information