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

Cyber threat detection: Unsupervised hunting of anomalous commands (UHAC)

2023 | Decision Support Systems | Citations: 0

Authors: Kayhan, Varol O.; Agrawal, Manish; Shivendu, Shivendu

Abstract: The cyber security industry is rapidly adopting threat hunting as a proactive to ... Expand

Abstract: The cyber security industry is rapidly adopting threat hunting as a proactive tool for early and faster detection of suspected malicious actors. In this paper, we propose a machine learning-based method, Unsupervised Hunting of Anomalous Commands (UHAC), to detect text-based anomalous commands in security information and event management (SIEM) logs that are good candidates for threat hunting. A unique feature of the proposed method is that it first creates a feature set based on the augmentation of document-term and document-character matrices. Then, an autoencoder-based detector is trained on this feature set using a custom loss function. UHAC consis­ tently outperforms other feature sets and algorithms such as one-class support vector machine, density-based spatial clustering of applications with noise, and word-embedding based models such as word2vec. The UHAC detector identifies 84–89% of anomalies in the top 10% of the data. Findings have implications for cybersecurity analysts who perform threat hunting in SIEM logs for process auditing on endpoint devices. Collapse

Topics: IT security artificial intelligence Python decision support system email phishing

Methods: autoencoder data transformation DBSCAN anomaly detection machine learning

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

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

Dynamics of trending topics between social media, news, and scientific literature

2022 | European Conference On Information Systems | Citations: 0

Authors: Bender, Micha; Frank, Sebastian; Panz, Sven

Abstract: Information is disseminating more rapidly in today's world than ever before in ... Expand

Abstract: Information is disseminating more rapidly in today's world than ever before in history. Every now and then, topics simultaneously gain massive attention in social media, dominate news headlines, and attract interest from researchers around the globe. While individual domains and networks are studied extensively, one question remains less addressed so far: How does information spread across different channels, considering dynamics between social media, news and, scientific literature? In this paper, we aim to identify frequent patterns in the dissemination of information over multiple channels. Based on an adapted pattern mining algorithm for multivariate time series, we provide strong indications for the existence of distinctive information diffusion effects between social media, news and scientific literature. We find that when all information channels simultaneously cover a certain topic, the preceding period is characterized either by a sole growth of social media coverage or a simultaneous growth of social media and news coverage. Collapse

Topics: Twitter social media mobile system cloud computing internet of things

Methods: longitudinal research time series analysis computational algorithm cluster analysis literature sample

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