Cyber-risk Management Framework for Online Gaming Firms: an Artificial Neural Network Approach

2023 | Information Systems Frontiers | Citations: 0

Authors: Sharma, Kalpit; Mukhopadhyay, Arunabha

Abstract: Hackers have used Distributed-Denial-of-Service attacks to overwhelm a firm’s cy ... Expand

Abstract: Hackers have used Distributed-Denial-of-Service attacks to overwhelm a firm’s cyber-resources resulting in disrupted access to legitimate end-users. Globally, DDoS attacks cost firms between US$ 120 K to US$ 2 M for each incident. Apart from the monetary loss, they also disrupt service quality and damage the brand reputation of firms. In 2018-2019, Massively Multiplayer Online Gaming (MMOG) firms witnessed 74% of the total DDoS attacks. MMOG firms form a lucrative segment for hackers because of their large customer base and the massive incentive to cause disruptions and losses. Our Feedforward Neural Network-based Cyber-risk Assessment and Mitigation (FNN-CRAM) model consists of three modules: assessment, quantification, and mitigation. The cyber-risk assessment module uses FNN, which takes seven inputs comprising DDoS attack intensity and duration for five DDoS attack types, vulnerability data (i.e., their counts and score), and the vulnerability trends over time. This layer is connected to a ten-neuron hidden layer and one neuron output layer that estimates the probability of these attacks. We also observe that the probability of these DDoS attacks follows a Weibull distribution. Next, our cyber-risk quantification module computes the expected loss. We note that expected losses due to these DDoS attacks follow a gamma distribution. Our cyber-risk mitigation module uses a heat matrix to help the CTO (i) prioritize the cyber-risk associated with a DDoS attack and (ii) decide whether to reduce, accept, or pass the cyber-risk using technological and cyber-insurance interventions. Collapse

Topics: denial of service attack IT security risk management IT security threat extensible markup language

Methods: artificial neural network descriptive statistic logistic regression k-nearest neighbor support vector machine

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

Creative Appeals in Firm-Generated Content and Product Performance

2022 | Information Systems Research | Citations: 0

Authors: Mu, Jifeng; Zhang, Jonathan; Borah, Abhishek; Qi, Jiayin

Abstract: The authors of this research investigate the effects of hedonic and utilitarian ... Expand

Abstract: The authors of this research investigate the effects of hedonic and utilitarian message appeals in firm-generated content on product performance measured by daily sales. The results from a multiproduct category, large-scale empirical study with data collected from a variety of sources show that hedonic appeals have a higher positive impact than utilitarian appeals on product performance. The authors also illustrate that message appeals have positive effects on product performance only when message quality and message goal consistency surpass a certain threshold and that these positive effects are stronger for hedonic than for utilitarian appeals. The authors further demonstrate that message complexity negatively moderates the effects of hedonic and utilitarian appeals on product performance with a stronger negative moderating effect for hedonic appeals. The research provides guidance on the proper combination of message styles and suggests that firms’ creative capabilities to curate online messages that resonate with consumers can produce significant performance advantages. Collapse

Topics: Twitter Facebook social media personality marketing management

Methods: Student's t-test parametric test instrumental variables estimation sentiment analysis descriptive statistic

How IT Investments Help Hospitals Gain and Sustain Reputation in the Media: The Role of Signaling and Framing

2022 | Information Systems Research | Citations: 1

Authors: Salge, Torsten Oliver; Antons, David; Barrett, Michael; Kohli, Rajiv; Oborn, Eivor; Polykarpou, Stavros

Abstract: How can information technology (IT) help hospitals gain and sustain reputation i ... Expand

Abstract: How can information technology (IT) help hospitals gain and sustain reputation in the media? Combining signaling theory and technology frames, we examine if, how, and to what extent IT investments over time shape three facets of reputation: generalized favorability, being known, and being known for something. In accessing healthcare services, most patients are unable to assess a hospital’s quality of care directly. Faced with such information asymmetries, patients tend to consider a hospital’s reputation in the media when making care decisions. Indeed, journalists are well positioned to detect even the weaker quality signals—among which are state-of-the-art IT—that a hospital emits. As information intermediaries, journalists aggregate and interpret IT-related signals against the backdrop of their technology frames, which reflect their expectations of how a modern hospital IT should look. Perceived congruence between their IT-related expectations and observations on the ground is likely to translate into less critical writing about a hospital. We test our theorizing based on a comprehensive panel data set of 152 English hospital organizations spanning five consecutive years of IT investments and subsequent changes in media reputation as reflected in 175,973 articles in English newspapers. We find that investments in IT staff increase the “being known” facet of reputation as evidenced in the volume of media coverage. Investments in IT equipment, in contrast, positively affect a hospital’s general favorability as mirrored in the tenor of its media coverage. Our econometric analysis as well as our complementary content analysis of newspaper articles and follow up interviews with journalists allow us to attribute this effect primarily to more visible IT equipment investments that prompt journalists to write less negatively about a hospital. These findings suggest that investments in IT equipment can buffer hospitals from negative press, thereby helping them to gain and maintain a strong reputation in the media. Collapse

Topics: IT investment IT workforce Wikipedia website accounting

Methods: qualitative interview longitudinal research econometric modeling theory development qualitative content analysis

Theories: signaling theory

Social Capital Accumulation through Social Media Networks: Evidence from a Randomized Field Experiment and Individual-Level Panel Data

2022 | Management Information Systems Quarterly | Citations: 0

Authors: Weiler, Michael; Stolz, Simon; Lanz, Andreas; Schlereth, Christian; Hinz, Oliver

Abstract: Work-related social media networks (SMNs) like LinkedIn introduce novel networki ... Expand

Abstract: Work-related social media networks (SMNs) like LinkedIn introduce novel networking opportunities and features that promise to help individuals establish, extend, and maintain social capital (SC). Typically, work-related SMNs offer access to advanced networking features exclusively to premium users in order to encourage basic users to become paying members. Yet little is known about whether access to these advanced networking features has a causal impact on the accumulation of SC. To close this research gap, we conducted a randomized field experiment and recruited 215 freelancers in a freemium, work-related SMN. Of these recruited participants, more than 70 received a randomly assigned voucher for a free 12- month premium membership. We observe that individuals do not necessarily accumulate more SC from their ability to access advanced networking features, as the treated freelancers did not automatically change their online networking engagement. Those features only reveal their full utility if individuals are motivated to proactively engage in networking. We found that freelancers who had access to advanced networking features increased their SC by 4.609% for each unit increase on the strategic networking behavior scale. We confirmed this finding in another study utilizing a second, individual-level panel dataset covering 52,392 freelancers. We also investigated the dynamics that active vs. passive features play in SC accumulation. Based on these findings, we introduce the “theory of purposeful feature utilization”: essentially, individuals must not only possess an efficacious “networking weapon”—they also need the intent to “shoot” it. Collapse

Topics: social capital social network electronic mail IT career affordance

Methods: experiment survey longitudinal research field experiment experimental group

Theories: theory of affordance big five model

Why your Past Matters: Disentangling the Influence of “Static” and “Dynamic” Emotional Properties in Social Media Content on User Engagement

2021 | International Conference on Information Systems | Citations: 0

Authors: Alibakhshi, Reza; Srivastava, Shirish C.

Abstract: Grounding our work in the literature on emotion dynamics and affect theory of s ... Expand

Abstract: Grounding our work in the literature on emotion dynamics and affect theory of social exchange, we theorize an emotion-centric model for visual social media posts comprising both static and dynamic aspects of emotions. Specifically, we hypothesize and empirically evaluate the relative salience of 'peak visual emotion' displayed in a social media post versus the 'emotional variability' and 'emotional inertia' of posts from a profile owner in determining the longevity of user engagement. Through this study, we make three key contributions. First, we emphasize the importance of considering dynamic emotional features in addition to the static emotional attributes embedded in the context of visual social media posts. Second, we reiterate the economic rationale for examining the 'longevity of user engagement' with the visual social media posts as a measure of social media success. Finally, we complement prior studies offering novel emotion dynamics constructs and invaluable insights for social media practitioners. Collapse

Topics: social media Instagram online community marketing management Microsoft Azure

Methods: autoregressive integrated moving average longitudinal research image processing robustness check autoregressive model

Theories: affect theory information systems theory

How do Virtual Badges Incentivize Voluntary Contributions to Online Communities?

2021 | Information & Management | Citations: 18

Authors: Cavusoglu, Huseyin; Li, Zhuolun; Kim, Seung Hyun

Abstract: Online communities rely heavily on badges to incentivize users to participate in ... Expand

Abstract: Online communities rely heavily on badges to incentivize users to participate in voluntary activities. We examine the relationship between earned badges/pursued badges and contributions in the Stack Overflow question and answer community. Our main results reveal that different levels of earned and pursued badges increase the amount of subsequent answering activity. In addition, earned gold badges are the most influential in the motivation of users while earned bronze badges are the least impactful. Furthermore, the effect of badges in­ volves positive spillovers from higher levels of earned badges to lower ones. Our findings offer important theoretical and managerial implications for online communities. Collapse

Topics: online community IT career gamification participatory design user experience

Methods: statistical hypothesis test design methodology Granger causality econometric modeling quantitative observational study

Theories: goal setting theory motivation theory self determination theory

The Comparative Performance of Online Referral Channels in E-Commerce

2021 | Journal of Management Information Systems | Citations: 0

Authors: Duan, Wenjing; Zhang, Jie

Abstract: The means by which e-commerce websites can reach and track online customers have ... Expand

Abstract: The means by which e-commerce websites can reach and track online customers have expanded enormously through the use of various digital marketing referral channels. However, evaluating comparative effectiveness and return on investment (ROI) across different referral channels remain difficult undertakings for many companies. This study aims to contribute to this line of investigation by quantifying the relative effectiveness, the dynamics, and the interdependencies among three types of major online referral channels: search engines, social media, and third-party websites. To this end, we employ the vector autoregressive (VARX) model on a large-scale clickstream dataset and have the following findings. Though search engine referrals demonstrate strong impact on sales, our results show that social media referrals have the strongest immediate and cumulative effects on e-commerce websites' conversion rates. Our results also demonstrate the synergies and interdependencies across these channels. This study contributes to the multi-channel analytics literature and sheds new light to digital marketing managers on assessing the cumulative impact and the economic value of online referral channels. Collapse

Topics: website social media search engine electronic commerce marketing management

Methods: longitudinal research time series analysis augmented Dickey–Fuller test Granger causality autocorrelation analysis

Dynamic Effects of Falsehoods and Corrections on Social Media: A Theoretical Modeling and Empirical Evidence

2021 | Journal of Management Information Systems | Citations: 3

Authors: King, Kelvin K.; Wang, Bin; Escobari, Diego; Oraby, Tamer

Abstract: Government agencies and fact-checking websites have been combating the spread of ... Expand

Abstract: Government agencies and fact-checking websites have been combating the spread of falsehoods on social media by issuing correction messages. There has been, however, no research on the effectiveness of correction messages on falsehoods and their dynamic interaction. We develop a theoretical model of the competition between falsehoods and correction messages on Twitter and show different interventions under which falsehoods could be hampered. Moreover, we use panel vector autoregressive models and machine learning techniques to empirically investigate the dynamic interactions between falsehoods and correction messages through a unique longitudinal dataset of 279,597 tweets. We find that correction messages cause an increase in the propagation of falsehoods on social media if their use is not optimized. This study highlights the importance of having government agencies, fact-checking websites, and social media platforms work together to optimize effective correction messages. We argue such an effort will counter the spread of falsehoods. Collapse

Topics: Twitter hurricane social media website misinformation

Methods: longitudinal research descriptive statistic theory development simulation robustness check

Online to Offline: The Impact of Social Media on Offline Sales in the Automobile Industry

2021 | Information Systems Research | Citations: 16

Authors: Wang, Yen-Yao; Guo, Chenhui; Susarla, Anjana; Sambamurthy, Vallabh

Abstract: Given the limited research into the impact of social media on offline sales of d ... Expand

Abstract: Given the limited research into the impact of social media on offline sales of durable goods, this study examines the dynamic relationships between firm-generated content (FGC), user-generated content (UGC), traditional media, and offline light vehicle sales. Data were collected from the official Facebook and Twitter pages of 30 U.S. car brands from 2009 to 2015. We utilized a panel vector autoregressive model to investigate the dynamic relationships among multiple time series variables while controlling for influential durable goods characteristics. The empirical results suggest that Facebook and Twitter have heterogeneous effects on offline vehicle sales. Moreover, FGC is more effective than UGC for influencing offline light vehicle sales. Viral impressions from Facebook and Twitter are essential, although the effects vary by social media platform (Facebook versus Twitter) and content type (FGC versus UGC). The result of the impulse response function analysis indicates that both the firm’s marketing efforts (FGC and traditional media) and UGC have a long-term effect on offline sales, with the long-term effect of a firm’s marketing efforts outlasting that of UGC. Incorporating FGC and UGC from Facebook and Twitter and traditional media could improve the performance of offline sales prediction. We also documented the within-Twitter synergistic effect between FGC and UGC for offline car sales and cross-channel substitution relationships (FGC on both Facebook and Twitter serves as a substitution to traditional media). Finally, we provide guidance for managers seeking to leverage multichannel marketing to boost the offline sales of durable goods. Collapse

Topics: Twitter Facebook social media marketing management database system

Methods: longitudinal research Granger causality Monte Carlo simulation time series analysis cross sectional research