All External Reference Prices Are Not the Same: How Magnitude, Source, and Fairness Shape Payment for Digital Goods

2023 | Information Systems Research | Citations: 0

Authors: Bassellier, Geneviève; Ramaprasad, Jui

Abstract: External reference prices (ERPs) are recognized to influence consumers’ payment ... Expand

Abstract: External reference prices (ERPs) are recognized to influence consumers’ payment for physical goods. We examine the role of ERPs in a digital goods context, emphasizing the importance of disentangling the effects of two components of ERPs: the magnitude and the source. We first examine the impact of the price magnitude and then investigate how the impact of ERP magnitude is moderated by its source―the specific context in which the reference price is set—and by its perceived fairness. Using a laboratory experiment with 471 participants, we first establish that as an ERP increases, payment for digital songs also increases. Furthermore, the source of the ERP motivates payment differently. Indeed, consumers’ willingness to pay is more strongly influenced by a price recommended by the site than by information on the price paid by others. Last, we show that the perceived fairness of the ERP matters. Results indicate that when the ERP is perceived to be high, the marginal effect of an increase in ERP on payment is smaller than when it is perceived to be fair. We contribute to the understanding of the effect of ERPs in digital context, where the source of an ERP can be varied, and perception of fairness can be heterogenous across consumers. This has implications for the design of digital platforms seeking to sell digital goods using a flexible pricing scheme. History: Anandasivam Gopal, Senior Editor; Sunil Wattal, Associate Editor. Funding: This work was supported by the Social Science and Humanities Research Council of Canada [Insight Development Grant 430-2017-01055]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2023.1206. Collapse

Topics: enterprise resource planning digital good website price management digital platform

Methods: experiment Student's t-test survey regression analysis method laboratory experiment

Theories: behavioral economics

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

Does Black Music also matter? The Effect of The George Floyd’s Death on Hip-hop Music Streaming in the United States

2023 | International Conference on Information Systems | Citations: 0

Authors: Wang, Yifei; Ozer, Gorkem Turgut; Gopal, Anand

Abstract: In the wake of George Floyd's death, individuals in the United States have turn ... Expand

Abstract: In the wake of George Floyd's death, individuals in the United States have turned to music, particularly hip-hop, as a source of information and a means of participating in discussions related to Floyd's death. We examine digital music consumption as a proxy for awareness of the need for race-based social justice within the United States. Using Spotify's streaming data, we found a significant increase in hip-hop listeners following Floyd's death relative to traditionally White-dominated genres, particularly in less racially diverse cities. Furthermore, we found that Black artists received more listenership across all sampled genres than their non-Black counterparts. This study contributes to the literature studying the societal impact of digital music platforms and how the music made by Black artists may serve as a source of information and discourse, aiding individuals in making sense of events related to racial inequality and anti-Black police violence in the United States. Collapse

Topics: digital service Spotify sensemaking music streaming service application programming interface

Methods: experimental group robustness check econometric modeling longitudinal research difference in differences

A collaborative filtering model incorporating media promotions and users' variety-seeking tendencies in the digital music market

2023 | Decision Support Systems | Citations: 0

Authors: Lee, Myounggu; Kim, Hye-Jin

Abstract: Understanding customer preferences and providing the right products at the right ... Expand

Abstract: Understanding customer preferences and providing the right products at the right time to customers via personalized recommendations have been among the major interests of online retailers and service providers. This paper proposes an improved collaborative filtering model that incorporates a firm's marketing effort vari­ ables (i.e., media promotional variables) to improve the prediction of customers' digital music choices. In addition, we assert that the predictive model's effectiveness is different for consumers depending on their varietyseeking tendencies in music. We compared our predictive model to benchmark models and demonstrated that our proposed model is superior in predicting users' download behavior. We also found that the overall predictive performance is higher for active variety seekers who consume diverse types of music via streaming. We provide some evidence that this may be due to differences in the degree to which the two groups are influenced by different types of media promotions. The results suggest that considering psychological characteristics such as variety-seeking tendencies provides more advantages in prediction and recommendation systems, which opens new avenues for improvement. Collapse

Topics: collaborative filtering YouTube marketing management music download social media

Methods: experiment computational algorithm random forest classification regression analysis method deep learning

DIGITAL PROCTORING IN HIGHER EDUCATION: A SYSTEMATIC LITERATURE REVIEW

2022 | European Conference On Information Systems | Citations: 0

Authors: Han, Shengnan; Nikou, Shahrokh; Ayele, Workneh Yilma; Balasuriya, Balasuriya Lekamalage Prasanna; Svee, Eric-Oluf

Abstract: To improve the academic integrity of online examination, digital proctoring sys ... Expand

Abstract: To improve the academic integrity of online examination, digital proctoring systems have been implemented in higher education worldwide, particularly during the COVID-19 pandemic. In this paper, we conducted a literature review of the research on digital proctoring in higher education. We found 115 relevant publications in nine databases. We applied topic modeling methods to analyze the corpus which resulted in eight topics. The review shows that the previous studies focus largely on the systems' development, adoption of the systems, the effects of proctored online exams on students' performance, and the legal, ethical, security, and privacy issues of digital proctoring. The annual topic trends indicate future research concerns, such as systems' development, online programs (MOOCs) and proctoring, along with various issues of using digital proctoring. The results of the review provide useful insights as well as implications for future research on digital proctoring, a crucial process for digitalizing higher education. Collapse

Topics: computer based testing artificial intelligence pandemic electronic authentication IS education

Methods: literature study literature sample topic model structured literature research computational algorithm

Beyond popularity: A user perspective on observable behaviours in a digital platform

2022 | Information Systems Journal | Citations: 2

Authors: Kunst, Katrine; Ringberg, Torsten; Vatrapu, Ravi

Abstract: The opinions and behaviours of others are recognised as powerful mechanisms for ... Expand

Abstract: The opinions and behaviours of others are recognised as powerful mechanisms for social influence in the digital sphere. The former, often referred to as electronic word of mouth (eWOM), is a thoroughly researched topic in the Information Systems literature. Conversely, the digital display of users' behaviours (e.g., number of past purchases) is less well understood despite the widespread adoption of this practice on digital platforms. Quantitative research has explored this interesting domain and found that observing others' behaviours entice observers to follow suit, but has left unaddressed the question of what sensemaking users derive from behavioural information. This is problematic as behavioural information is more open to interpretation compared to eWOM. In this article, we adopt the concept of electronic word of behaviour (eWOB) to denote such behavioural information. Through the lens of basic psychological needs theory and the qualitative means-end chain approach, we expose how eWOB is interpreted and used by users of a digital platform, the music service Spotify. We find that eWOB leads to satisfaction of the basic psychological needs for relatedness and competence when observing others' behaviours. We also show how exposure to one's own past behaviours can yield a positive sense of self when presented in meaningful and private manners, but that it can also negatively impact users when their needs for autonomy and competence are not heeded by the digital platform. Finally, based on our empirical findings we offer a set of design implications for how digital platforms can optimise the use of eWOB. Collapse

Topics: digital service Spotify digital platform social media usability

Methods: qualitative interview computational algorithm digital trace data design principle case study

Theories: mean end chain theory

Dancing to the #challenge: The Effect of TikTok on Closing the Artist Gender Gap

2022 | International Conference on Information Systems | Citations: 0

Authors: Wang, Yifei; Ramaprasad, Jui; Gopal, Anand

Abstract: This study examines how an interesting technological phenomenon called the "Has ... Expand

Abstract: This study examines how an interesting technological phenomenon called the "Hashtag Dance Challenge" (HDC), made popular on the short video platform TikTok, may drive artists' popularity on the digital music streaming platform, and, importantly, help women, artists, to achieve traction that is needed to succeed commercially. Using data from TikTok, Spotify, and music analytics companies, we analyze the impact of HDCs on artists' popularity growth rate on Spotify. We find that artists with an HDC-related song achieve a significant daily increase in followership on Spotify, representing traction and appeal within the music industry, relative to similar artists who do not have an HDC. Importantly, the daily growth of Spotify followers increases by approximately 3% more for female artists than male artists, given an HDC-associated song. Our findings shed new light on the role of social media with respect to artist self-promotion, especially in making the music more inclusive and attractive to female music artists. Collapse

Topics: TikTok digital service Spotify music streaming service social media

Methods: experimental group propensity score matching propensity score method logistic regression longitudinal research

Personalized Recommendation for Balancing Content Generation and Usage on Two-Sided Entertainment Platforms

2022 | International Conference on Information Systems | Citations: 0

Authors: Zhang, Hao; Guo, Zhiling; Wang, Mingzheng

Abstract: Online entertainment platforms such as Youtube host a vast amount of user-gener ... Expand

Abstract: Online entertainment platforms such as Youtube host a vast amount of user-generated content (UGC). The unique feature of two-sided UGC entertainment platforms is that creators' content generation and users' content usage can influence each other. However, traditional recommender systems often emphasize content usage but ignore content generation, leading to a misalignment between these two goals. To address the challenge, this paper proposes a prescriptive uplift framework to balance content generation and usage through personalized recommendations. Specifically, we first predict the heterogeneous treatment effects (HTEs) of recommended contents on creators' content generation and users' content usage, then consider these two predicted HTEs simultaneously in an optimization model to determine the recommended contents for each user. Using a large-scale real-world dataset, we demonstrate that the proposed recommendation method better balances content generation and usage and brings a 42% increase in participants' activity compared to existing benchmark methods. Collapse

Topics: recommender system cloud computing digital service music streaming service information system use

Methods: optimization model hidden Markov model regression analysis method machine learning causality analysis

Music intelligence: Granular data and prediction of top ten hit songs

2021 | Decision Support Systems | Citations: 1

Authors: Kim, Seon Tae; Oh, Joo Hee

Abstract: In the music market, superstars significantly dominate the market share, while p ... Expand

Abstract: In the music market, superstars significantly dominate the market share, while predicting the top hit songs is notoriously difficult. The music intelligence technology, retrieving and utilizing granular acoustic features of songs, provides opportunities to improve the prediction of top hit songs. Using data on 6209 unique songs that appeared in the weekly Billboard Hot 100 charts from 1998 to 2016, especially acoustic features provided by Spotify, we investigate empirically how the top-10-hit-songs likelihood prediction is improved by acoustic fea­ tures. We find that some acoustic features (e.g., danceability, happiness, and some metrics of timbre and pitch) significantly improve the model’s ability to predict the top-10-hit-songs probability. These results suggest that the granular data, provided by the music intelligence technology, carries a substantial predictive value in the era of online music streaming. Collapse

Topics: digital service Spotify music streaming service logistics management internet technology

Methods: computational algorithm machine learning descriptive statistic generalized linear model McNemar's test

A Taxonomy of Platform Envelopment: Revealing Patterns and Particularities

2020 | Americas Conference on Information Systems | Citations: 6

Authors: Hermes, Sebastian; Kaufmann-Ludwig, Jonas; Schreieck, Maximilian; Weking, Jörg; Böhm, Markus

Abstract: Platform envelopment describes a competitive move whereby a digital platform en ... Expand

Abstract: Platform envelopment describes a competitive move whereby a digital platform enters an adjacent market. On one hand, it might enable to dethrone an established platform. On the other hand, it might give rise to the creation of platform conglomerates, which increases the concentration of private power. Therefore, platform envelopment has recently attracted significant attention from regulators and scholars. However, the traditional view of platform envelopment does not consider recent platform envelopment practices observed in research and practice. In this study, we aim to determine and structure the complexity of platform envelopment. We investigated 20 cases and developed a taxonomy of platform envelopment. We further encoded these cases into the comprehensive taxonomy and derived platform envelopment patterns and particularities. Our work contributes to research by establishing a foundation for the conceptual understanding of platform envelopment. Regulators can use this taxonomy to classify platform envelopment cases and determine potentially anti-competitive conduct. Collapse

Topics: digital service digital platform mobile application Google+ digital distribution

Methods: case study cluster analysis triangulation constant comparative analysis theory development