Yuwen Chen, Peihu Zhu, Jian Ma, Xiaomin Huang, Jin Qin
Technology transfer enables the technology from legal owners to be used by others, and it is essential for technology innovation in modern society. However, transferring technology from academia to industry has become a challenging task due to the "cultural divide" problem, where researchers in universities tend to focus on knowledge discovery, while companies focus on making profits with application of proven technologies. This creates a mistrust problem for companies to use academic patents invented by universities. Various recommendation methods have been proposed for technology transfer purposes, but few have addressed the trust issue caused by the cultural divide. This paper proposes a multidimensional trust-enhanced recommendation approach to promote academic patent trading. The approach extracts patent information, users' online interactions, and technology transfer information for recommendation calculation. It includes 1) measuring the degree of connectivity between companies and patents by the Personalized PageRank model; 2) measuring the trustworthiness of a potential patent transaction from the aspects of patent quality, inventor, and university; and 3) adopting a logistic regression model to integrate the above measurements. The results of our user-based experiment show that the proposed recommendation approach obtains higher average hit rate and higher willingness scores than current recommendation methods.
{"title":"A Trust-Enhanced Patent Recommendation Approach to University-Industry Technology Transfer","authors":"Yuwen Chen, Peihu Zhu, Jian Ma, Xiaomin Huang, Jin Qin","doi":"10.1145/3645057.3645061","DOIUrl":"https://doi.org/10.1145/3645057.3645061","url":null,"abstract":"Technology transfer enables the technology from legal owners to be used by others, and it is essential for technology innovation in modern society. However, transferring technology from academia to industry has become a challenging task due to the \"cultural divide\" problem, where researchers in universities tend to focus on knowledge discovery, while companies focus on making profits with application of proven technologies. This creates a mistrust problem for companies to use academic patents invented by universities. Various recommendation methods have been proposed for technology transfer purposes, but few have addressed the trust issue caused by the cultural divide. This paper proposes a multidimensional trust-enhanced recommendation approach to promote academic patent trading. The approach extracts patent information, users' online interactions, and technology transfer information for recommendation calculation. It includes 1) measuring the degree of connectivity between companies and patents by the Personalized PageRank model; 2) measuring the trustworthiness of a potential patent transaction from the aspects of patent quality, inventor, and university; and 3) adopting a logistic regression model to integrate the above measurements. The results of our user-based experiment show that the proposed recommendation approach obtains higher average hit rate and higher willingness scores than current recommendation methods.","PeriodicalId":517361,"journal":{"name":"ACM SIGMIS Database: the DATABASE for Advances in Information Systems","volume":"33 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139896594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Benyawarath Nithithanatchinnapat, Joshua Maurer, Xuefei (Nancy) Deng, K. D. Joshi
In an era where generative AI is reshaping the landscape of business and technology, this editorial addresses the critical imperative for transformative reform in business education. It emphasizes the dual nature of generative AI as both a formidable disruptor and a catalyst for innovation, necessitating a shift in how we educate the future workforce. The editorial calls for a proactive and comprehensive reevaluation of current educational models, advocating for an integration of AI literacy and ethical considerations into the core of business curricula. We aim to galvanize academia into action, advocating for an educational evolution that not only acknowledges the challenges posed by AI but also harnesses its potential to enrich and advance business education in preparing students for an AI-driven future.
{"title":"Future Business Workforce: Crafting a Generative AI-Centric Curriculum Today for Tomorrow's Business Education","authors":"Benyawarath Nithithanatchinnapat, Joshua Maurer, Xuefei (Nancy) Deng, K. D. Joshi","doi":"10.1145/3645057.3645059","DOIUrl":"https://doi.org/10.1145/3645057.3645059","url":null,"abstract":"In an era where generative AI is reshaping the landscape of business and technology, this editorial addresses the critical imperative for transformative reform in business education. It emphasizes the dual nature of generative AI as both a formidable disruptor and a catalyst for innovation, necessitating a shift in how we educate the future workforce. The editorial calls for a proactive and comprehensive reevaluation of current educational models, advocating for an integration of AI literacy and ethical considerations into the core of business curricula. We aim to galvanize academia into action, advocating for an educational evolution that not only acknowledges the challenges posed by AI but also harnesses its potential to enrich and advance business education in preparing students for an AI-driven future.","PeriodicalId":517361,"journal":{"name":"ACM SIGMIS Database: the DATABASE for Advances in Information Systems","volume":"362 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139895742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The cybervetting activities of potential employers present a significant threat to job-seeking social media users because their content becomes vulnerable to unwanted access and scrutiny. Without control over access, personal information may be gathered from social media and used in ways that harm the job seeker. Access control is a critical element of information privacy that has not received much attention but can help explain individuals' privacy behaviors. We use a protection motivation framework and a fear appeal to examine how job-seeking SNS users respond to cybervetting. We analyze the responses of 375 job-seeking SNS users to understand the relationships among threat perceptions, fear, coping responses, access control, and intention to implement and use an ephemeral application. We argue that when confronted by cybervetting, job-seeking SNS users are favorable toward using an ephemeral application because it bolsters privacy and meets the psychological need for control over access. Our results show that access control moderates the fear response to cybervetting, it is prompted by users' coping responses, and it helps explain why response efficacy and self-efficacy are predictive of behavioral intention.
{"title":"How Do Job Seekers Respond to Cybervetting? An Exploration of Threats, Fear, and Access Control","authors":"Robin L. Wakefield, Lane T. Wakefield","doi":"10.1145/3645057.3645064","DOIUrl":"https://doi.org/10.1145/3645057.3645064","url":null,"abstract":"The cybervetting activities of potential employers present a significant threat to job-seeking social media users because their content becomes vulnerable to unwanted access and scrutiny. Without control over access, personal information may be gathered from social media and used in ways that harm the job seeker. Access control is a critical element of information privacy that has not received much attention but can help explain individuals' privacy behaviors. We use a protection motivation framework and a fear appeal to examine how job-seeking SNS users respond to cybervetting. We analyze the responses of 375 job-seeking SNS users to understand the relationships among threat perceptions, fear, coping responses, access control, and intention to implement and use an ephemeral application. We argue that when confronted by cybervetting, job-seeking SNS users are favorable toward using an ephemeral application because it bolsters privacy and meets the psychological need for control over access. Our results show that access control moderates the fear response to cybervetting, it is prompted by users' coping responses, and it helps explain why response efficacy and self-efficacy are predictive of behavioral intention.","PeriodicalId":517361,"journal":{"name":"ACM SIGMIS Database: the DATABASE for Advances in Information Systems","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139895814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Geo-tagging features in social media apps allow users to announce their precise location with great ease and convenience, but geo-tagging poses some serious risks to users' privacy since it involves revelation of one's physical location, a form of personal data, to other users within and across social networks, making them vulnerable to various online and offline attacks, ranging from users being stalked to their identities being stolen. Despite these risks, geo-tagging is increasingly becoming a popular culture in the virtual realm of social media. This study explores why individuals geo-tag on social media by drawing from self-determination theory and privacy calculus to illustrate the underlying factors that influence users to engage in geo-tagging behavior on social media platforms. Based on an online survey administered to 834 active users of social media, this study contends that users' interpersonal competence and self-regulation influence their online affiliation need, which, in turn, affects their geo-tagging behavior. Additionally, we find that perceived benefit and risk have moderation effects on the association between users' online affiliation need and their geo-tagging behavior.
{"title":"Explicating Geo-Tagging Behavior on Social Media: Role of Interpersonal Competence, Self-Regulation, Online Affiliation, and Privacy Calculus","authors":"Sarbottam Bhagat, Dan J. Kim","doi":"10.1145/3645057.3645060","DOIUrl":"https://doi.org/10.1145/3645057.3645060","url":null,"abstract":"Geo-tagging features in social media apps allow users to announce their precise location with great ease and convenience, but geo-tagging poses some serious risks to users' privacy since it involves revelation of one's physical location, a form of personal data, to other users within and across social networks, making them vulnerable to various online and offline attacks, ranging from users being stalked to their identities being stolen. Despite these risks, geo-tagging is increasingly becoming a popular culture in the virtual realm of social media. This study explores why individuals geo-tag on social media by drawing from self-determination theory and privacy calculus to illustrate the underlying factors that influence users to engage in geo-tagging behavior on social media platforms. Based on an online survey administered to 834 active users of social media, this study contends that users' interpersonal competence and self-regulation influence their online affiliation need, which, in turn, affects their geo-tagging behavior. Additionally, we find that perceived benefit and risk have moderation effects on the association between users' online affiliation need and their geo-tagging behavior.","PeriodicalId":517361,"journal":{"name":"ACM SIGMIS Database: the DATABASE for Advances in Information Systems","volume":"26 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139896613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yuwen Chen, Peihu Zhu, Jian Ma, Xiaomin Huang, Jin Qin
Technology transfer enables the technology from legal owners to be used by others, and it is essential for technology innovation in modern society. However, transferring technology from academia to industry has become a challenging task due to the "cultural divide" problem, where researchers in universities tend to focus on knowledge discovery, while companies focus on making profits with application of proven technologies. This creates a mistrust problem for companies to use academic patents invented by universities. Various recommendation methods have been proposed for technology transfer purposes, but few have addressed the trust issue caused by the cultural divide. This paper proposes a multidimensional trust-enhanced recommendation approach to promote academic patent trading. The approach extracts patent information, users' online interactions, and technology transfer information for recommendation calculation. It includes 1) measuring the degree of connectivity between companies and patents by the Personalized PageRank model; 2) measuring the trustworthiness of a potential patent transaction from the aspects of patent quality, inventor, and university; and 3) adopting a logistic regression model to integrate the above measurements. The results of our user-based experiment show that the proposed recommendation approach obtains higher average hit rate and higher willingness scores than current recommendation methods.
{"title":"A Trust-Enhanced Patent Recommendation Approach to University-Industry Technology Transfer","authors":"Yuwen Chen, Peihu Zhu, Jian Ma, Xiaomin Huang, Jin Qin","doi":"10.1145/3645057.3645061","DOIUrl":"https://doi.org/10.1145/3645057.3645061","url":null,"abstract":"Technology transfer enables the technology from legal owners to be used by others, and it is essential for technology innovation in modern society. However, transferring technology from academia to industry has become a challenging task due to the \"cultural divide\" problem, where researchers in universities tend to focus on knowledge discovery, while companies focus on making profits with application of proven technologies. This creates a mistrust problem for companies to use academic patents invented by universities. Various recommendation methods have been proposed for technology transfer purposes, but few have addressed the trust issue caused by the cultural divide. This paper proposes a multidimensional trust-enhanced recommendation approach to promote academic patent trading. The approach extracts patent information, users' online interactions, and technology transfer information for recommendation calculation. It includes 1) measuring the degree of connectivity between companies and patents by the Personalized PageRank model; 2) measuring the trustworthiness of a potential patent transaction from the aspects of patent quality, inventor, and university; and 3) adopting a logistic regression model to integrate the above measurements. The results of our user-based experiment show that the proposed recommendation approach obtains higher average hit rate and higher willingness scores than current recommendation methods.","PeriodicalId":517361,"journal":{"name":"ACM SIGMIS Database: the DATABASE for Advances in Information Systems","volume":"25 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139893596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The use of conversational agents (CAs) based on artificial intelligence (AI) is becoming more common in the field of recruiting. Organizations are now adopting AI-based CAs for applicant (pre-)selection, but negative news coverage, especially the black-box character of AI, has hindered adoption. So far, little is known about the contextual factors influencing users' perception of AI-based CAs in general and the effect of provided explanations by explainable AI (XAI) in particular. While research on algorithm aversion provides some initial explanations, information regarding the effects of different XAI approaches on different types of decisions on the attitudes of (potential) applicants is scarce. Therefore, in this study, we use a quantitative, quota-representative study (n = 490) to assess the acceptance of CAs in recruiting. By applying an experimental within-subject design, we provide a more nuanced perspective on why and when providing explanations increases user acceptance. We also show that contextual factors such as the type of assessed skills are major determinants of this effect, and we conclude that XAI is not a "one-size-fits-all approach." Based on the insight that contextual factors of the decision problem are more important than the type of XAI approach itself, we argue that the use and the effects of explainability in recruiting need a more nuanced perspective, focusing on the fit of explanations with the user's characteristics and preferences.
基于人工智能（AI）的会话代理（CA）在招聘领域的使用越来越普遍。目前，各组织正在采用基于人工智能的 CA 进行应聘者（预）筛选，但负面新闻报道，尤其是人工智能的黑箱特性，阻碍了 CA 的采用。迄今为止，人们对影响用户对基于人工智能的CA的感知的背景因素知之甚少，尤其是对可解释的人工智能（XAI）所提供的解释的影响知之甚少。虽然有关算法厌恶的研究提供了一些初步解释，但有关不同 XAI 方法对不同类型决策对（潜在）申请人态度的影响的信息却很少。因此，在本研究中，我们采用了定量、定额代表性研究（n = 490）来评估 CA 在招聘中的接受度。通过采用受试者内实验设计，我们提供了一个更细致的视角，来说明为什么以及何时提供解释会提高用户的接受度。我们还表明，情境因素（如被评估技能的类型）是这一效果的主要决定因素，并得出结论：XAI 并非 "放之四海而皆准的方法"。基于决策问题的情境因素比 XAI 方法本身的类型更重要这一见解，我们认为，招聘中可解释性的使用和效果需要一个更加细致入微的视角，重点关注解释与用户特征和偏好的契合度。
{"title":"Mitigating Algorithm Aversion in Recruiting: A Study on Explainable AI for Conversational Agents","authors":"Jürgen Fleiß, Elisabeth Bäck, Stefan Thalmann","doi":"10.1145/3645057.3645062","DOIUrl":"https://doi.org/10.1145/3645057.3645062","url":null,"abstract":"The use of conversational agents (CAs) based on artificial intelligence (AI) is becoming more common in the field of recruiting. Organizations are now adopting AI-based CAs for applicant (pre-)selection, but negative news coverage, especially the black-box character of AI, has hindered adoption. So far, little is known about the contextual factors influencing users' perception of AI-based CAs in general and the effect of provided explanations by explainable AI (XAI) in particular. While research on algorithm aversion provides some initial explanations, information regarding the effects of different XAI approaches on different types of decisions on the attitudes of (potential) applicants is scarce. Therefore, in this study, we use a quantitative, quota-representative study (n = 490) to assess the acceptance of CAs in recruiting. By applying an experimental within-subject design, we provide a more nuanced perspective on why and when providing explanations increases user acceptance. We also show that contextual factors such as the type of assessed skills are major determinants of this effect, and we conclude that XAI is not a \"one-size-fits-all approach.\" Based on the insight that contextual factors of the decision problem are more important than the type of XAI approach itself, we argue that the use and the effects of explainability in recruiting need a more nuanced perspective, focusing on the fit of explanations with the user's characteristics and preferences.","PeriodicalId":517361,"journal":{"name":"ACM SIGMIS Database: the DATABASE for Advances in Information Systems","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139896208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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