ACM SIGMIS Database: the DATABASE for Advances in Information Systems最新文献

A Trust-Enhanced Patent Recommendation Approach to University-Industry Technology Transfer 大学-产业技术转让的信任增强型专利推荐方法

ACM SIGMIS Database: the DATABASE for Advances in Information Systems Pub Date : 2024-02-06 DOI: 10.1145/3645057.3645061

Yuwen Chen, Peihu Zhu, Jian Ma, Xiaomin Huang, Jin Qin

{"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}

引用次数: 0

Future Business Workforce: Crafting a Generative AI-Centric Curriculum Today for Tomorrow's Business Education 未来的商业人才：今天为未来的商业教育打造以人工智能为中心的生成课程

ACM SIGMIS Database: the DATABASE for Advances in Information Systems Pub Date : 2024-02-06 DOI: 10.1145/3645057.3645059

Benyawarath Nithithanatchinnapat, Joshua Maurer, Xuefei (Nancy) Deng, K. D. Joshi

引用次数: 0

How Do Job Seekers Respond to Cybervetting? An Exploration of Threats, Fear, and Access Control 求职者如何应对网络招聘？对威胁、恐惧和访问控制的探讨

ACM SIGMIS Database: the DATABASE for Advances in Information Systems Pub Date : 2024-02-06 DOI: 10.1145/3645057.3645064

Robin L. Wakefield, Lane T. Wakefield

{"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}

引用次数: 0

Explicating Geo-Tagging Behavior on Social Media: Role of Interpersonal Competence, Self-Regulation, Online Affiliation, and Privacy Calculus 解释社交媒体上的地理标记行为：人际交往能力、自我调节、在线关系和隐私计算的作用

ACM SIGMIS Database: the DATABASE for Advances in Information Systems Pub Date : 2024-02-06 DOI: 10.1145/3645057.3645060

Sarbottam Bhagat, Dan J. Kim

{"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}

引用次数: 0

A Trust-Enhanced Patent Recommendation Approach to University-Industry Technology Transfer 大学-产业技术转让的信任增强型专利推荐方法

ACM SIGMIS Database: the DATABASE for Advances in Information Systems Pub Date : 2024-02-06 DOI: 10.1145/3645057.3645061

Yuwen Chen, Peihu Zhu, Jian Ma, Xiaomin Huang, Jin Qin

{"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}

引用次数: 0

Mitigating Algorithm Aversion in Recruiting: A Study on Explainable AI for Conversational Agents 减轻招聘中的算法厌恶：对话式代理的可解释人工智能研究

ACM SIGMIS Database: the DATABASE for Advances in Information Systems Pub Date : 2024-02-06 DOI: 10.1145/3645057.3645062

Jürgen Fleiß, Elisabeth Bäck, Stefan Thalmann

{"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}

引用次数: 1