Value Creation Tradeoff in Business Ecosystems: Leveraging Complementarities While Managing Interdependencies

2023 | Organization Science | Citations: 0

Authors: Agarwal, Shiva; Kapoor, Rahul

Abstract: Complementary assets play an important role in shaping an innovation’s commercia ... Expand

Abstract: Complementary assets play an important role in shaping an innovation’s commercialization success. In this paper, we broaden the locus of complementarities to examine the role of complementary technologies residing in the business ecosystems that are becoming an important source of value creation for innovating firms. We argue that, on one hand, complementary technologies help innovations create more value for their users. On the other hand, they can also limit the focal innovation’s value creation by exposing them to performance bottlenecks as the underlying technological architecture of the ecosystem evolves. We further extend the notion of specialization of complementary assets to ecosystems by considering complementary technologies that are specialized to a focal ecosystem and those that are available across multiple ecosystems. We highlight that, although the complementary technologies that are specialized to an ecosystem facilitate greater value creation, they are more likely to subject the focal innovation to performance bottlenecks. Evidence from 244,034 apps launched by software developers for Apple’s iPhone ecosystem during 2008–2015 offers strong support for our framework. In summary, the study sheds light on the value creation tradeoff for firms innovating in business ecosystems—the opportunities associated with leveraging complementarities and the challenges associated with managing technological interdependencies. Funding: S. Agarwal was funded by the McCombs School of Business at The University of Texas at Austin [Research Excellence Grant]. Collapse

Topics: mobile application innovation management value creation Apple iPhone iOS

Methods: statistical hypothesis test instrumental variables estimation cross sectional research proportional hazards model robustness check

Towards a thematic dimensional framework of online fraud: An exploration of fraudulent email attack tactics and intentions

2023 | Decision Support Systems | Citations: 1

Authors: Bera, Debalina; Ogbanufe, Obi; Kim, Dan J.

Abstract: Despite anti-phishing filters, social engineering-based cyber-attacks still resu ... Expand

Abstract: Despite anti-phishing filters, social engineering-based cyber-attacks still result in billions of dollars lost annually, significant personal identity theft, loss of corporate secrets, and espionage. We review the phishing literature on psychological attacks and design tactics employed for deception by attackers. The result reveals the need to continuously identify tactics embedded in fraudulent email content to understand why users still fall prey to phishing attacks. Using the literature as a backdrop, we identify and conceptualize a thematic dimensional framework of fraudulent phishing attacks. This study uses benchmark datasets of fraudulent email to empirically validate the proposed dimensional framework. Specifically, a combination of machine learning-based content analysis and topic modeling found a majority of the discovered topics were labeled against the proposed di­ mensions. Statistical analysis was employed to provide empirical evidence of the thematic dimensions of fraudulent email attacks. This study thus, not only extends the cybersecurity literature by identifying and vali­ dating eight dimensions that enrich our understanding of phishing and attack identification but stimulates cu­ mulative research endeavors to develop yet more comprehensive dimensional frameworks of phishing email attacks. Collapse

Topics: phishing email phishing electronic mail email spam personalization

Methods: term frequency–inverse document frequency natural language processing topic model discriminant analysis literature study

The Power of Identity Cues in Text-Based Customer Service: Evidence from Twitter

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Gao, Yang; Rui, Huaxia; Sun, Shujing

Abstract: Text-based customer service is emerging as an important channel through which c ... Expand

Abstract: Text-based customer service is emerging as an important channel through which companies can assist customers. However, the use of few identity cues may cause customers to feel limited social presence and even suspect the human identity of agents, especially in the current age of advanced algorithms. Does such a lack of social presence affect service interactions? We studied this timely question by evaluating the impact of customers' perceived social presence on service outcomes and customers' attitudes toward agents. Our identification strategy hinged on Southwest Airlines' sudden requirement to include a first name in response to service requests on Twitter, which enhanced customers' perceived level of social presence. This change led customers to become more willing to engage and more likely to reach a resolution upon engagement. We further conducted a randomized experiment to understand the underlying mechanisms. We found that the effects were mainly driven by customers who were ex ante uncertain or suspicious about the human identity of agents, and the presence of identity cues improved service outcomes by enhancing customers' perceived levels of trust and empathy. Additionally, we found no evidence of elevated verbal aggression from customers toward agents with identity cues, although a mechanism test revealed the moderating role of customers' emotional states. Our study highlights the importance of social presence in text-based customer service and suggests a readily available and almost costless strategy for firms: signal humanization through identity cues. Collapse

Topics: customer service Twitter social presence social media customer satisfaction

Methods: experiment computational algorithm synthetic control method survey support vector machine

Theories: social presence theory big five model

She? The Role of Perceived Agent Gender in Social Media Customer Service

2023 | International Conference on Information Systems | Citations: 0

Authors: Ke, Junyuan; Gao, Yang; Sun, Shujing; Rui, Huaxia

Abstract: This paper investigated the role of perceived agent gender in customer behavior ... Expand

Abstract: This paper investigated the role of perceived agent gender in customer behavior using a unique dataset from Southwest Airlines' Twitter account. We inferred agent gender based on the first names provided by agents when responding to customers. We measured customer behavior using three outcomes: whether a customer decided to continue the service conversation upon receiving an agent's initial response as well as the valence and arousal levels in their second tweet if the customer chose to continue the interaction. Our identification strategy relied on the Backdoor Criterion and hinged on the assumption that customer service requests are assigned to the next available agent, independent of agent gender. The findings revealed that customers were more likely to continue interactions with female agents than male agents and they were more negative in valence but less intense in arousal with the former group than with the latter. Collapse

Topics: Twitter customer service application programming interface Google+ service quality

Methods: word embedding computational algorithm machine learning support vector machine experiment

Quantifying Learning and Competition among Crowdfunding Projects: Metrics and a Predictive Model

2023 | HICSS | Citations: 0

Authors: Liu, Xiexin; Rahmani Moghaddam, Maryam; Fan, Weiguo (Patrick)

Abstract: The performance of a crowdfunding project is highly situational-dependent. In th ... Expand

Abstract: The performance of a crowdfunding project is highly situational-dependent. In this study, we quantify the interactions between crowdfunding projects in order to understand how these interactions can help predict the performance of crowdfunding campaigns. Specifically, we utilize Natural Language Processing (NLP) techniques to create a semi-automated system to label the associated product for each crowdfunding campaign. We also propose three sets of metrics to measure how crowdfunding projects learn from and compete with each other. Finally, we propose a machine learning model and demonstrate that the proposed metrics and the proposed model outperform other combinations when predicting the performance of crowdfunding projects. Collapse

Topics: crowdsourcing crowdfunding research and development mobile system knowledge sharing

Methods: cluster analysis natural language processing machine learning support vector machine term frequency–inverse document frequency

The Decoy Effect and Recommendation Systems

2023 | Information Systems Research | Citations: 0

Authors: Mousavi, Nasim; Adamopoulos, Panagiotis; Bockstedt, Jesse

Abstract: In this paper, we explore the decoy effect in recommendation systems. Including ... Expand

Abstract: In this paper, we explore the decoy effect in recommendation systems. Including a decoy item in a set of alternatives can influence the attractiveness of the other items by facilitating decision making. Prior research literature has indeed shown the decoy effect to be robust in traditional choice settings, with consistent reporting of an overall positive impact. Practitioners often use decoys to help drive demand for specific items. Recommendation systems too are increasingly being used to present item choice sets to customers and users. Both recommendation systems and the decoy effect can be used as strategies to help facilitate decision making. However, previous work has not examined the decoy effect in the context of recommendations. The decoy effect may facilitate consumer decision making and positively impact user behavior when used with recommendation systems. However, in the recommendation context, customers often have different expectations for the reliability and quality of the presented information. Hence, a decoy as a recommendation could signal issues in system reliability, resulting in a negative effect. We perform a randomized, controlled laboratory experiment and use persuasion theory as the theoretical lens to demonstrate that the decoy effect works differently in the context of recommendation systems. Specifically, we show that depending on the recommendation context, the decoy effect can or cannot drive demand for target items. We find that including a decoy minimizes the demand for the target option when personalized recommendations are presented, which deviates from the traditional decoy effect. However, a decoy increases the target’s demand when nonpersonalized recommendations are shown, following the conventional decoy effect. We explore the mechanism behind these findings and show the robustness of our results by conducting multiple analyses and additional experiments. The findings of our paper have important implications for the design of recommendation systems and our understanding of consumer decision making. History: Ravi Bapna, Senior Editor; Idris Adjerid, Associate Editor. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1197. Collapse

Topics: recommender system usability personalization database system user behavior

Methods: experiment experimental group laboratory experiment computational algorithm experimental task

Does Social Media Accelerate Product Recalls? Evidence from the Pharmaceutical Industry

2022 | Information Systems Research | Citations: 0

Authors: Gao, Yang; Duan, Wenjing; Rui, Huaxia

Abstract: Social media has become a vital platform for voicing product-related experiences ... Expand

Abstract: Social media has become a vital platform for voicing product-related experiences that may not only reveal product defects, but also impose pressure on firms to act more promptly than before. This study scrutinizes the rarely studied relationship between these voices and the speed of product recalls in the context of the pharmaceutical industry in which social media pharmacovigilance is becoming increasingly important for the detection of drug safety signals. Using Federal Drug Administration drug enforcement reports and social media data crawled from online forums and Twitter, we investigate whether social media can accelerate the product recall process in the context of drug recalls. Results based on discrete-time survival analyses suggest that more adverse drug reaction discussions on social media lead to a higher hazard rate of the drug being recalled and, thus, a shorter time to recall. To better understand the underlying mechanism, we propose the information effect, which captures how extracting information from social media helps detect more signals and mine signals faster to accelerate product recalls, and the publicity effect, which captures how firms and government agencies are pressured by public concerns to initiate speedy recalls. Estimation results from two mechanism tests support the existence of these conceptualized channels underlying the acceleration hypothesis of social media. This study offers new insights for firms and policymakers concerning the power of social media and its influence on product recalls. Collapse

Topics: social media Twitter anonymity Google Search database system

Methods: hazard function survival analysis econometric modeling support vector machine robustness check

Box office sales and social media: A cross-platform comparison of predictive ability and mechanisms

2021 | Decision Support Systems | Citations: 0

Authors: Bogaert, Matthias; Ballings, Michel; Van den Poel, Dirk; Oztekin, Asil

Abstract: This paper aims to determine the power of social media data (Facebook and Twitte ... Expand

Abstract: This paper aims to determine the power of social media data (Facebook and Twitter) in predicting box office sales, which platforms, data types and variables are the most important and why. To do so, we compare several models based on movie data, Facebook data, and Twitter data. We benchmark these model comparisons using various prediction algorithms. Next, we apply information-fusion sensitivity analysis to evaluate which variables are driving the predictive performance. Our analysis shows that social media data significantly increases the predictive power of traditional box office prediction models. Facebook data clearly outperform Twitter data and including user-generated content next to marketer-generated always improves predictive power. Our sensitivity analysis reveals that volume and valence based combination variables pertaining to Facebook comments are the most important variables. Furthermore, we provide an in-depth analysis of the potential mechanisms driving differential predictive ability of Facebook and Twitter. Our findings suggest that Twitter has less of an impact on box office sales than Facebook because Twitter users have less source credibility than Facebook users. Our results are important for practitioners, marketers and academics who want to employ social media data for box office sales predictions. Collapse

Topics: Twitter Facebook social media application programming interface user-generated content

Methods: computational algorithm sensitivity analysis decision tree classification sentiment analysis random forest classification

Theories: signaling theory

Dynamic, Multidimensional, and Skillset-Specific Reputation Systems for Online Work

2021 | Information Systems Research | Citations: 0

Authors: Kokkodis, Marios

Abstract: Reputation systems in digital workplaces increase transaction efficiency by buil ... Expand

Abstract: Reputation systems in digital workplaces increase transaction efficiency by building trust and reducing information asymmetry. These systems, however, do not yet capture the dynamic multidimensional nature of online work. By uniformly aggregating reputation scores across worker skills, they ignore skillset-specific heterogeneity (reputation attribution), and they implicitly assume that a worker’s quality does not change over time (reputation staticity). Even further, reputation scores tend to be overly positive (reputation inflation), and, as a result, they often fail to differentiate workers efficiently. This work presents a new augmented intelligence reputation framework that combines human input with machine learning to provide dynamic, multidimensional, and skillset-specific worker reputation. The framework includes three components: The first component maps skillsets into a latent space of finite competency dimensions (word embedding), and, as a result, it directly addresses reputation attribution. The second builds dynamic competency-specific quality assessment models (hidden Markov models) that solve reputation staticity. The final component aggregates these competency-specific assessments to generate skillset-specific reputation scores. Application of this framework on a data set of 58,459 completed tasks from a major online labor market shows that, compared to alternative reputation systems, the proposed approach (1) yields more appropriate rankings of workers that form a closer-to-normal reputation distribution, (2) better identifies “nonperfect” workers who are more likely to underperform and are harder to predict, and (3) improves the ranking of within-opening choices and yields significantly better outcomes. Additional analysis of 77,044 restaurant reviews shows that the proposed framework successfully generalizes to alternative contexts, where assigned feedback scores are overly positive and service quality is multidimensional and dynamic. Collapse

Topics: online reputation system recommender system word of mouth online labor market individual productivity

Methods: machine learning descriptive statistic computational algorithm XGBoost word embedding

Predicting donation behavior: Acquisition modeling in the nonprofit sector using Facebook data

2021 | Decision Support Systems | Citations: 0

Authors: Schetgen, Lisa; Bogaert, Matthias; Van den Poel, Dirk

Abstract: The purpose of this study is to demonstrate the value of Facebook data in predic ... Expand

Abstract: The purpose of this study is to demonstrate the value of Facebook data in predicting first-time donation behavior. More specifically, we provide evidence that Facebook data can be used as a valuable data source for nonprofit organizations in acquiring new donors. To do so, we evaluate three different dimensionality reduction techniques (i.e., singular value decomposition, non-negative matrix factorization, and latent Dirichlet allocation) over seven classification techniques (i.e., logistic regression, k-nearest neighbors, bagged trees, random forest, adaboost, extreme gradient boosting, and artificial neural networks) using five times twofold cross-validation. Next, we assess what type of Facebook data and which predictors are most important. The results indicate that we can predict first-time donation behavior based on Facebook data with high predictive performance. Our benchmark indicates that the combination of singular value decomposition and logistic regression outperforms all other analytical methodologies with an area under the receiver operating characteristic of 0.72 and a top decile lift of 3.33. The results show that Facebook pages and categories of Facebook pages are the most important data types. The most important predictors are dimensions related to age, education, residence, materialism, responsible consumption, and interest in nonprofits. The presented acquisition models can be used by nonprofit organiza­ tions to implement a one-to-one targeted marketing campaign towards Facebook fans. To the best of our knowledge, our study is the first to determine the predictive value of Facebook data for nonprofits in a real-life acquisition context. Collapse

Topics: Facebook social media decision support system web page source code

Methods: machine learning data transformation computational algorithm dimensionality reduction F-test