A machine-based personality oriented team recommender for software development organizations

Hiring the right person for the right job is always a challenging task in software development landscapes. To bridge this gap, software_rms start using psychometric instruments for investigating the personality types of software practitioners. In our previous research, we have developed an MBTI-like instrument to reveal the personality types ofsoftware practitioners. This study aims to develop a personality-based team recommender mechanism to improve the e_ectiveness of software teams. The mechanism is based on predicting the possible patterns of teams using a machine-based classi_er. The classi_er is trained with em-pirical data (e.g. personality types, job roles), which was collected from52 software practitioners working on _ve different software teams. 12software practitioners were selected for the testing process who were recommended by the classi_er to work for these teams. The preliminary results suggest that a personality-based team recommender system mayprovide an effective approach as compared with ad-hoc methods of teamformation in software development organizations. Ultimately, the overallperformance of the proposed classi_er was 83.3%. These _ndings seemacceptable especially for tasks of suggestion where individuals might beable to _t in more than one team

Questioning Racial and Gender Bias in AI-based Recommendations: Do Espoused National Cultural Values Matter?

One realm of AI, recommender systems have attracted significant research attention due to concerns about its devastating effects to society’s most vulnerable and marginalised communities. Both media press and academic literature provide compelling evidence that AI-based recommendations help to perpetuate and exacerbate racial and gender biases. Yet, there is limited knowledge about the extent to which individuals might question AI-based recommendations when perceived as biased. To address this gap in knowledge, we investigate the effects of espoused national cultural values on AI questionability, by examining how individuals might question AI-based recommendations due to perceived racial or gender bias. Data collected from 387 survey respondents in the United States indicate that individuals with espoused national cultural values associated to collectivism, masculinity and uncertainty avoidance are more likely to question biased AI-based recommendations. This study advances understanding of how cultural values affect AI questionability due to perceived bias and it contributes to current academic discourse about the need to hold AI accountable

A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics

In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to make talent-related decisions in a quantitative manner. Indeed, the recent development of Big Data and Artificial Intelligence (AI) techniques have revolutionized human resource management. The availability of large-scale talent and management-related data provides unparalleled opportunities for business leaders to comprehend organizational behaviors and gain tangible knowledge from a data science perspective, which in turn delivers intelligence for real-time decision-making and effective talent management at work for their organizations. In the last decade, talent analytics has emerged as a promising field in applied data science for human resource management, garnering significant attention from AI communities and inspiring numerous research efforts. To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of human resource management. Specifically, we first provide the background knowledge of talent analytics and categorize various pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant research efforts, categorized based on three distinct application-driven scenarios: talent management, organization management, and labor market analysis. In conclusion, we summarize the open challenges and potential prospects for future research directions in the domain of AI-driven talent analytics.Comment: 30 pages, 15 figure

Developing and Facilitating Temporary Team Mental Models Through an Information-Sharing Recommender System

It is well understood that teams are essential and common in many aspects of life, both work and leisure. Due to the importance of teams, much research attention has focused on how to improve team processes and outcomes. Of particular interest are the cognitive aspects of teamwork including team mental models (TMMs). Among many other benefits, TMMs involve team members forming a compatible understanding of the task and team in order to more efficiently make decisions. This understanding is sometimes classified using four TMM domains: equipment (e.g., operating procedures), task (e.g., strategies), team interactions (e.g., interdependencies) and teammates (e.g., tendencies). Of particular interest to this dissertation is accelerating the development of teammate TMMs which include members understanding the knowledge, skills, attitudes, preferences, and tendencies of their teammates. An accurate teammate TMM allows teams to predict and account for the needs and behaviors of their teammates. Although much research has highlighted how the development of the four TMM domains can be supported, promoting the development of teammate TMMs is particularly challenging for a specific type of team: temporary teams. Temporary teams, in contrast to ongoing teams, involve unknown teammates, novel tasks, short task times (alternatively limited interactions), and members disbanding after completing their task. These teams are increasingly used by organizations as they can be agilely formed with individual members selected to accomplish a specific task. Such teams are commonly used in contexts such as film production, the military, emergency response, and software development, just to name a few. Importantly, although these teams benefit greatly from teammate TMMs due to the efficiencies gained in decision making while working under limited deadlines, the literature is severely limited in understanding how to support temporary teams in this way. As prior research has suggested, an opportunity to accelerate teammate TMM development on temporary teams is through the use of technology to selectively share teammate information to support these TMMs. However, this solution poses numerous privacy concerns. This dissertation uses four studies to create a foundational and thorough understanding of how recommender system technology can be used to promote teammate TMMs through information sharing while limiting privacy concerns. Study 1 takes a highly exploratory approach to set a foundation for future dissertation studies. This study investigates what information is perceived to be helpful for promoting teammate TMMs on actual temporary teams. Qualitative data suggests that sharing teammate information related to skills/preferences, conflict management styles, and work ethic/reliability is perceived as beneficial to supporting teammate TMMs. Also, this data provides a foundational understanding for what should be involved in information-sharing recommendations for promoting teammate TMMs. Quantitative results indicate that conflict management data is perceived as more helpful and appropriate to share than personality data. Study 2 investigates the presentation of these recommendations through the factors of anonymity and explanations. Although explanations did not improve trust or satisfaction in the system, providing recommendations associated with a specific teammate name significantly improved several team measures associated with TMMs for actual temporary teams compared to teams who received anonymous recommendations. This study also sheds light on what temporary team members perceive as the benefits to sharing this information and what they perceive as concerns to their privacy. Study 3 investigates how the group/team context and individual differences can influence disclosure behavior when using an information-sharing recommender system. Findings suggest that members of teams who are fully assessed as a team are more willing to unconditionally disclose personal information than members who are assessed as an individual or members who are mixed assessed as an individual and a team. The results also show how different individual differences and different information types are associated with disclosure behavior. Finally, Study 4 investigates how the occurrence and content of explanations can influence disclosure behavior and system perceptions of an information-sharing recommender system. Data from this study highlights how benefit explanations provided during disclosure can increase disclosure and explanations provided during recommendations can influence perceptions of trust competence. Meanwhile, benefit-related explanations can decrease privacy concerns. The aforementioned studies fill numerous research gaps relating to teamwork literature (i.e., TMMs and temporary teams) and recommender system research. In addition to contributions to these fields, this dissertation results in design recommendations that inform both the design of group recommender systems and the novel technology conceptualized through this dissertation, information-sharing recommender systems

Annual Report 2016-2017

The College of Computing and Digital Media has always prided itself on curriculum, creative work, and research that stays current with changes in our various fields of instruction. As we looked back on our 2016-17 academic year, the need to chronicle the breadth and excellence of this work became clear. We are pleased to share with you this annual report, our first, highlighting our accomplishments. Last year, we began offering three new graduate programs and two new certificate programs. We also planned six degree programs and three new certificate programs for implementation in the current academic year. CDM faculty were published more than 100 times, had their films screened more than 200 times, and participated in over two dozen exhibitions. Our students were recognized for their scholarly and creative work, and our alumni accomplished amazing things, from winning a Student Academy Award to receiving a Pulitzer. We are proud of all the work we have done together. One notable priority for us in 2016-17 was creating and strengthening relationships with industry—including expanding our footprint at Cinespace and developing the iD Lab—as well as with the community, through partnerships with the Chicago Housing Authority, Wabash Lights, and other nonprofit organizations. We look forward to continuing to provide innovative programs and spaces this academic year. Two areas in particular we’ve been watching closely are makerspaces and the “internet of things.” We’ve already made significant commitments to these areas through the creation of our 4,500 square foot makerspace, the Idea Realization Lab, and our new cyber-physical systems bachelor’s program and lab. We are excited to continue providing the opportunities, curriculum, and facilities to support our remarkable students. David MillerDean, College of Computing and Digital Mediahttps://via.library.depaul.edu/cdmannual/1000/thumbnail.jp

Adoption of Artificial Intelligence in an Organizational Context: Analysis of the Factors Influencing the Adoption and Decision-Making Process

The emergence of Artificial Intelligence (AI) shifts the business environment to such an extent that this general-purpose technology (GPT) is prevalent in a wide range of industries, evolves through constant advancements, and stimulates complementary innovations. By implementing AI applications in their business practices, organizations primarily benefit from improved business process automation, valuable cognitive insights, and enhanced cognitive engagements. Despite this great potential, organizations encounter difficulties in adopting AI as they struggle to adjust to corresponding complex organizational changes. The tendency for organizations to face challenges when implementing AI applications indicates that AI adoption is far from trivial. The complex organizational change generated by AI adoption could emerge from intelligent agents’ learning and autonomy capabilities. While AI simulates human intelligence in perception, reasoning, learning, and interaction, organizations’ decision-making processes might change as human decision-making power shifts to AI. Furthermore, viewing AI adoption as a multi-stage rather than a single-stage process divides this complex change into the initiation, adoption, and routinization stages. Thus, AI adoption does not necessarily imply that AI applications are fully incorporated into enterprise-wide business practices; they could be at certain adoption stages or only in individual business functions. To address these complex organizational changes, this thesis seeks to examine the dynamics surrounding AI adoption at the organizational level. Based on four empirical research papers, this thesis presents the factors that influence AI adoption and reveals the impact of AI on the decision-making process. These research papers have been published in peer-reviewed conference proceedings. The first part of this thesis describes the factors that influence AI adoption in organizations. Based on the technology-organization-environment (TOE) framework, the findings of the qualitative study are consistent with previous innovation studies showing that generic factors, such as compatibility, top management, and data protection, affect AI adoption. In addition to the generic factors, the study also reveals that specific factors, such as data quality, ethical guidelines, and collaborative work, are of particular importance in the AI context. However, given these technological, organizational, and environmental factors, national cultural differences may occur as described by Hofstede’s national cultural framework. Factors are validated using a quantitative research design throughout the adoption process to account for the complexity of AI adoption. By considering the initiation, adoption, and routinization stages, differentiating and opposing effects on AI adoption are identified. The second part of this thesis addresses AI’s impact on the decision-making process in recruiting and marketing and sales. The experimental study shows that AI can ensure procedural justice in the candidate selection process. The findings indicate that the rule of consistency increases when recruiters are assisted by a CV recommender system. In marketing and sales, AI can support the decision-making process to identify promising prospects. By developing classification models in lead-and-opportunity management, the predictive performances of various machine learning algorithms are presented. This thesis outlines a variety of factors that involve generic and AI-specific considerations, national cultural perspectives, and a multi-stage process view to account for the complex organizational changes AI adoption entails. By focusing on recruiting as well as marketing and sales, it emphasizes AI’s impact on organizations’ decision-making processes

A Software Product Line Approach to Ontology-based Recommendations in E-Tourism Systems

This study tackles two concerns of developers of Tourism Information Systems (TIS). First is the need for more dependable recommendation services due to the intangible nature of the tourism product where it is impossible for customers to physically evaluate the services on offer prior to practical experience. Second is the need to manage dynamic user requirements in tourism due to the advent of new technologies such as the semantic web and mobile computing such that etourism systems (TIS) can evolve proactively with emerging user needs at minimal time and development cost without performance tradeoffs. However, TIS have very predictable characteristics and are functionally identical in most cases with minimal variations which make them attractive for software product line development. The Software Product Line Engineering (SPLE) paradigm enables the strategic and systematic reuse of common core assets in the development of a family of software products that share some degree of commonality in order to realise a significant improvement in the cost and time of development. Hence, this thesis introduces a novel and systematic approach, called Product Line for Ontology-based Tourism Recommendation (PLONTOREC), a special approach focusing on the creation of variants of TIS products within a product line. PLONTOREC tackles the aforementioned problems in an engineering-like way by hybridizing concepts from ontology engineering and software product line engineering. The approach is a systematic process model consisting of product line management, ontology engineering, domain engineering, and application engineering. The unique feature of PLONTOREC is that it allows common TIS product requirements to be defined, commonalities and differences of content in TIS product variants to be planned and limited in advance using a conceptual model, and variant TIS products to be created according to a construction specification. We demonstrated the novelty in this approach using a case study of product line development of e-tourism systems for three countries in the West-African Region of Africa