Results

Found 57399 articles

Clear filters

Getting Personal: A Deep Learning Artifact for Text-Based Measurement of Personality
PDF
2023 | Information Systems Research | Citations: 0
Authors: Yang, Kai; Lau, Raymond Y. K.; Abbasi, Ahmed

Abstract: Analysts, managers, and policymakers are interested in predictive analytics capa ... Expand

Abstract: Analysts, managers, and policymakers are interested in predictive analytics capable of offering better foresight. It is generally accepted that in forecasting scenarios involving organizational policies or consumer decision making, personal characteristics, including personality, may be an important predictor of downstream outcomes. The inclusion of personality features in forecasting models has been hindered by the fact that traditional measurement mechanisms are often infeasible. Text-based personality detection has garnered attention because of the public availability of digital textual traces. However, the text machine learning space has bifurcated into two branches: feature-based methods relying on manually crafted human intuition, or deep learning language models that leverage big data and compute, the main commonality being that neither branch generates accurate personality assessments, thereby making personality measures infeasible for downstream forecasting applications. In this study, we propose DeepPerson, a design artifact for text-based personality detection that bridges these two branches by leveraging concepts from relevant psycholinguistic theories in conjunction with advanced deep learning strategies. DeepPerson incorporates novel transfer learning and hierarchical attention network methods that use psychological concepts and data augmentation in conjunction with person-level linguistic information. We evaluate the utility of the proposed artifact using an extensive design evaluation on three personality data sets in comparison with state-of-the-art methods proposed in academia and industry. DeepPerson can improve detection of personality dimensions by 10–20 percentage points relative to the best comparison methods. Using case studies in the finance and health domains, we show that more accurate text-based personality detection can translate into significant improvements in downstream applications such as forecasting future firm performance or predicting pandemic infection rates. Our findings have important implications for research at the intersection of design and data science, and practical implications for managers focused on enabling, producing, or consuming predictive analytics.History: Olivia Sheng, Senior Editor; Huimin Zhao, Associate Editor.Funding: This work was supported by Oracle for Research (NLP for the Greater Good) and the U.S. National Science Foundation’s Division of Information and Intelligent Systems [Grants BDS-1636933 and IIS-1816504]. The work was also partly supported by the Research Grants Council of the Hong Kong Special Administrative Region [Project: CityU 11507219] and the City University of Hong Kong SRG [Project: 7005780].Supplemental Material: The online appendices are available at https://doi.org/10.1287/isre.2022.1111. Collapse

Topics: personality Twitter social media pandemic Google+

Methods: machine learning BERT design science deep learning computational algorithm

Theories: big five model
Optimal IP-based content provision model for digital content platforms
PDF
2023 | Information & Management | Citations: 0
Authors: Yang, Liyuan; Li, Zhiyong; Nan, Guofang; Li, Dahui; Li, Minqiang

Abstract: To increase the provision of digital content based on a pre-existing intellectua ... Expand

Abstract: To increase the provision of digital content based on a pre-existing intellectual property (IP-based content), digital content platforms can either procure finished IP-based content made by IP holders (the IP-holder-led provision model) or develop IP-based content themselves after obtaining adaptation licenses from IP holders (the platform-led provision model). This study develops a stylized analytical model and finds that the platform’s choice of provision model is determined by maximum demand, cost efficiency, and the difference in the capacity to generate content-derived revenue between the platform and the IP holder. We also examine the conditions under which a win-win-win outcome for the platform, the IP holder, and consumers can arise. Collapse

Topics: consumer surplus IS economics social media price management

Methods: mathematical model hierarchical linear modeling cross sectional research

Theories: lemon market theory
Responding to Online Reviews in Competitive Markets: A Controlled Diffusion Approach

2023 | Management Information Systems Quarterly | Citations: 0
Authors: Yang, Mingwen; Zheng, Zhiqiang (Eric); Mookerjee, Vijay; Chen, Hongyu

Abstract: We study how firms respond to online customer reviews in a competitive market w ... Expand

Abstract: We study how firms respond to online customer reviews in a competitive market where they jostle with one another for sales based on online ratings. The focus of this paper is on how firms can optimally manage their ratings through management response and how review ratings affect the sales and profits of competing firms. We develop a controlled diffusion process to model the coevolution of sales and ratings as a function of the response strategy chosen to maximize profit over time. Our model considers a variety of factors, such as profit margin and customer rating sensitivity, that influence a firm's effort to manage ratings and subsequently its sales and profits. More response effort needs to be exerted to manage ratings when either the profit margin of a tour is very high or customers are very sensitive to ratings. We estimate our model using data on Ctrip's tours that include each tour's sales, reviews, prices, and tour features. We find that consumers anchor their beliefs in the mean market rating and that their purchase decisions depend on the tour's rating relative to this anchor. Thus, relative, rather than absolute, ratings matter. Our study informs firms on how competition and other primitives impact their efforts to manage ratings and hence profit. Our methodology allowed us to conduct "what-if" analyses, for example, to study what would happen to the review ratings, sales, and profits of a tour if a firm adopted a different response strategy. We were also able to provide turnaround strategies for struggling tours, i.e., factors that a loss-making tour should change if it wishes to make a positive profit. Ultimately, we conducted a competitive analysis that allowed us to modify certain parameters that affect the intensity of competition and hence the sales and the profits of competing tours. Finally, we demonstrate the flexibility of the model by extending it to incorporate multiple state variables that might affect the response strategy. Collapse

Topics: application programming interface online learning system Twitter innovation management service quality

Methods: longitudinal research time series analysis generalized autoregressive conditional heteroskedasticity model natural language processing autoregressive moving average model

Theories: co-evolution theory
Recommendation content matters! Exploring the impact of the recommendation content on consumer decisions from the means-end chain perspective
PDF
2023 | International Journal of Information Management | Citations: 0
Authors: Yang, Tiantian; Yang, Feng; Men, Jinqi

Abstract: Consumers increasingly rely on social media to obtain product information. The v ... Expand

Abstract: Consumers increasingly rely on social media to obtain product information. The vlog, a new kind of social medium, has been adopted by sellers to connect with consumers. Through this platform, sellers can cooperate with vloggers to recommend products or services to consumers. Extending beyond the previous perspective that consumers’ perceptions and behaviors are influenced by vloggers’ attributes, we herein investigate the influence of recommendation content. Drawing on the means-end chain framework, this paper investigates how the at tributes of recommendation content affect consumers’ intention to adopt (i.e., follow) the recommendation. Data collected from 513 respondents provides support for the proposed influences. The results indicate that source credibility, content diagnosticity, and content serendipity drive consumers’ recommendation adoption intention. This research contributes to the literature on rec-vlog marketing by clarifying the significant role of recom mendation content. Implications are discussed regarding these findings. Collapse

Topics: problem solving marketing management social commerce decision making internet technology

Methods: survey chi squared test SEM tool statistical hypothesis test self reported survey

Theories: mean end chain theory
Corporate Trade War Uncertainty and Patent Bubble

2023 | Americas Conference on Information Systems | Citations: 0
Authors: Yang, Xu; Hu, Nan; Liang, Peng

Abstract: This paper draws upon resource dependence theory and investigates how trade pol ... Expand

Abstract: This paper draws upon resource dependence theory and investigates how trade policy uncertainty affects firm strategic innovation management in China. Adopting a machine learning approach called Word2Vec from computational linguistics, we construct and validate a measure of firm-level managers' perceived trade war uncertainty (TWU). We find that TWU has a positive effect on the number of total patent applications, but this positive effect is totally driven by low-quality patents instead of high-quality patents. Moreover, we document that firms have stronger incentives for such strategic innovation behavior when the underlying firms are more financially constrained, and/or when the management is more myopic. In addition, we open the behavioral black box of firms' strategic innovation and demonstrate that patents can be employed opportunistically to meet government policies to further attract more government subsidies. Collapse

Topics: innovation management armed conflict missing data accounting database system

Methods: word2vec machine learning longitudinal research time series analysis regression analysis method

Theories: resource dependency theory
Customer Cybersecurity and Supplier Cost Management Strategy

2023 | International Conference on Information Systems | Citations: 0
Authors: Yang, Xu; Liang, Peng; Hu, Nan; Xue, Fujing

Abstract: In this paper, we explore the spillover effect of customer firms' data breaches ... Expand

Abstract: In this paper, we explore the spillover effect of customer firms' data breaches on their upstream supplier firms' cost management strategies, proxied by cost stickiness. Our primary analyses suggest that data breaches suffered by customer firms are associated with a decrease in cost stickiness among supplier firms. Furthermore, the reductions in supplier cost stickiness are stronger if suppliers are managed by CEOs from national cultural groups with high uncertainty avoidance, low long-term orientations, and/or low individualism. In sum, the findings contribute to both Information Systems (IS) and Operations Management (OM) disciplines in terms of data breach, cost management strategy, and the role of national culture in OM. In particular, the findings can facilitate the management and regulation of data breaches for managers and regulators. Collapse

Topics: data breach strategic management national culture supply chain management database system

Methods: descriptive statistic ordinary least square natural experiment longitudinal research difference in differences

Theories: cultural dimensions theory
Gamification and Marketing Management: A Literature Review and Future Agenda

2023 | Americas Conference on Information Systems | Citations: 0
Authors: Yang, Xinyi; Xi, Nannan; Tang, Hairui; Hamari, Juho

Abstract: Given the motivational effect of game elements and mechanisms on user experienc ... Expand

Abstract: Given the motivational effect of game elements and mechanisms on user experience in various non-game contexts, gamification has widely been used as an effective marketing technique to enhance the performance of business practices. In the past decade, a variety of studies have explored and investigated the value that gamification can provide in consumer-facing marketing activities. However, there is still a dearth of granular understanding of how gamification in marketing has been studied in the current literature. This paper follows the PRISMA literature review process and systematically reviews 93 papers consisting of 111 empirical studies on gamification and marketing management. The synthesized findings provide a holistic picture of the adopted research methods, different investigated gamification affordances and advergames, various gamified industries, and marketing performance indicators regarding products, services, and brands. Five agenda points, mainly relating to methodology and themes, are further suggested. Collapse

Topics: marketing management gamification affordance database system extended reality

Methods: literature study experiment literature sample structured literature research simulation

Theories: theory of affordance
Mitigating information asymmetry to acquire venture capital financing for digital startups in China: The role of weak and strong signals
PDF
2023 | Information Systems Journal | Citations: 1
Authors: Yang, Yuxue; Fang, Yulin; Wang, Nianxin; Su, Xiang

Abstract: This paper addresses how weak and strong signals affect venture capital funding ... Expand

Abstract: This paper addresses how weak and strong signals affect venture capital funding acquired by digital startups at their early stage in various industries of China. We also articulate the interaction mechanism of these strong and weak signals by demonstrating their complementary or substitutive effects in alleviating information asymmetry on startup quality, which can help digital startups secure venture capital financing. Drawing on signalling theory and institutional legitimacy theory, we introduce application (app) downloads as a novel strong signal that can reduce market legitimacy concerns, and previous-round venture capitalist reputation as a traditional strong signal that mitigates regulatory legitimacy concerns. We treat founders' startup and IT experience as weak signals, as they provide rhetorical and indirect information indicating a startup's potential to establish regulatory and market legitimacy. The study empirically investigates our hypotheses using data of 163 digital startups in various industries of China. Results confirm the positive relationships between strong signals and venture capital funding secured by a digital startup. Furthermore, signals of similar strength are found to complement each other's effects in certain situations, while strong signals can reduce the effects of weak signals on a digital startup's financing performance under specific conditions that create these mixed effects. Implications for digital startup research and practice as well as limitations and suggestions for future research are discussed. Collapse

Topics: digital entrepreneurship mobile application decision making business model database system

Methods: descriptive statistic theory development robustness check multiple regression statistical hypothesis test

Theories: lemon market theory signaling theory theory of bounded rationality
Compete with Me? The Impact of Online Gamified Competition on Exercise Behavior
PDF
2023 | Journal of the Association for Information Systems | Citations: 0
Authors: Yang, Yang; Goh, Khim; Teo, Hock Hai; Tan, Sharon

Abstract: Online gamified competition utilizes competition as a core gamification design ... Expand

Abstract: Online gamified competition utilizes competition as a core gamification design element with affordances from wearables and mobile applications to track competitive activities and visualize information in an integrated way to shape users' exercise behaviors. However, a clear understanding of how online gamified competition cultivates exercise behaviors in different types of individuals is still lacking. We take into account the individual differences in exercise behaviors and categorize exercisers into three groups (active, moderate, and inactive) based on an adapted recency, frequency, and monetary value framework using key exercise behavior metrics. Theorizing online gamified competition as a means of social and temporal self-comparison, we examine the effect of performance feedback from two distinct modes of comparison (performance rankings and performance gap), and participants' relationships with their social comparison referents (i.e., rivalry intensity), on the exercise behaviors of different exerciser groups. Our results reveal that online gamified competition has differential effects on exercise behaviors across different exerciser groups. Specifically, we find that positive performance improvements are more motivational for active and moderate exercisers, while performance deterioration relative to historical exercise performance level is more discouraging for inactive exercisers. Performance rankings exhibit a more salient effect for moderate and inactive exercisers, and rivalry intensity has a stronger positive effect on active exercisers' exercise behavior. The strengthening effect of awareness affordances in mobile fitness apps is more notable with regard to the impact of rivalry intensity on moderate and inactive exercisers. We derive theoretical and practical implications of gamified information systems that use competition as a core design element for shaping the exercise behavior of individuals in different exerciser groups. Collapse

Topics: fitness tracker mobile fitness app mobile system gamification smart wearable device

Methods: theory development cluster analysis k-means clustering descriptive statistic design principle

Theories: social comparison theory theory of economic development
Unlocking the Power of Voice for Financial Risk Prediction: A Theory-Driven Deep Learning Design Approach

2023 | Management Information Systems Quarterly | Citations: 0
Authors: Yang, Yi; Qin, Yu; Fan, Yangyang; Zhang, Zhongju

Abstract: Unstructured multimedia data (text and audio) provides unprecedented opportunit ... Expand

Abstract: Unstructured multimedia data (text and audio) provides unprecedented opportunities to derive actionable decision-making in the financial industry, in areas such as portfolio and risk management. However, due to formidable methodological challenges, the promise of business value from unstructured multimedia data has not materialized. In this study, we use a design science approach to develop DeepVoice, a novel nonverbal predictive analysis system for financial risk prediction, in the setting of quarterly earnings conference calls. DeepVoice forecasts financial risk by leveraging not only what managers say (verbal linguistic cues) but also how managers say it (vocal cues) during the earnings conference calls. The design of DeepVoice addresses several challenges associated with the analysis of nonverbal communication. We also propose a two-stage deep learning model to effectively integrate managers' sequential vocal and verbal cues. Using a unique dataset of 6,047 earnings call samples (audio recordings and textual transcripts) of S&P 500 firms across four years, we show that DeepVoice yields remarkably lower risk forecast errors than that achieved by previous efforts. The improvement can also translate into nontrivial economic gains in options trading. The theoretical and practical implications of analyzing vocal cues are discussed.Challenge 1: Time variation. Managers' vocal features can vary substantially during conference calls. For example, during the presentation section, when the manager is reading a forward-looking statement, the voice pitch may be quite stable. However, individuals' vocal cues may change as a result of being nervous, uncertain, or excited (Scherer et al., 1991). As such, in the question-answer section, when the manager is being scrutinized by analysts, the voice pitch may change. Mayew et al. (2020) found that the voice pitch of managers during dialogues with analysts is lower by about 4 Hz, on average, compared to their voice pitch during presentation sessions. Therefore, the time-varying patterns of managers' vocal cues may contain useful information. However, prior studies have ignored the time-varying pattern of managers' vocal cues in earnings conference calls (Mayew et al., 2020;, and it is unclear how a predictive model could effectively summarize time-varying vocal sequences.Challenge 2: Vocal-verbal integration. According to nonverbal communication theory (Mehrabian, 1972), vocal cues play a key role in nonverbal communication, as they can either affirm or discredit a message. For example, if a manager claims the company has strong future growth but uses a pessimistic tone of voice, market participants listening to the conference call may spot the irregularity and interpret the verbal message differently. This implies that to accurately gauge the information contained in managers' communication, one should integrate verbal language with the accompanying vocal cues. However, existing computational models (Poria et al., 2017) are limited in terms of integrating these two different communication channels (i.e., vocal and verbal).Challenge 3: Vocal complexity. Identifying the specific acoustic constructs from voice is a complex process, and the literature is still evolving without any consensus on an appropriate model for measuring affective states, emotions, and trust (Mayew & Venkatachalam, 2013). There is also little agreement about how a speaker's vocal cues should be combined to identify emotional states (Schuller, 2010)-e.g., which vocal cues to investigate and measure and how much weight to place on each vocal cue. As a result, attempts to develop constructs from vocal cues may suffer from high measurement errors. Collapse

Topics: database system enterprise information system website accounting social media

Methods: deep learning experiment meta design machine learning design artifact

Theories: transaction cost economics communication theory

Previous Page 279 of 5740 Next

AIS11

Journals

Conferences

Semantic Filter

Found 57399 articles

Getting Personal: A Deep Learning Artifact for Text-Based Measurement of Personality

Optimal IP-based content provision model for digital content platforms

Responding to Online Reviews in Competitive Markets: A Controlled Diffusion Approach

Recommendation content matters! Exploring the impact of the recommendation content on consumer decisions from the means-end chain perspective

Corporate Trade War Uncertainty and Patent Bubble

Customer Cybersecurity and Supplier Cost Management Strategy

Gamification and Marketing Management: A Literature Review and Future Agenda

Mitigating information asymmetry to acquire venture capital financing for digital startups in China: The role of weak and strong signals

Compete with Me? The Impact of Online Gamified Competition on Exercise Behavior

Unlocking the Power of Voice for Financial Risk Prediction: A Theory-Driven Deep Learning Design Approach