Digital agents are considered a general-purpose technology. They spread quickly in private and organizational contexts, including education. Yet, research lacks a conceptual framing to describe interaction with such agents in a holistic manner. While focusing on the interaction with a pedagogical agent, i.e., a digital agent capable of natural-language interaction with a learner, we propose a model of learning activity based on activity theory. We use this model and a review of prior research on digital agents in education to analyze how various characteristics of the activity, including features of a pedagogical agent or learner, influence learning outcomes. The analysis leads to identification of IS research directions and guidance for developers of pedagogical agents and digital agents in general. We conclude by extending the activity theory-based model beyond the context of education and show how it helps designers and researchers ask the right questions when creating a digital agent.

Depression is a common disease worldwide. It is difficult to diagnose and continues to be underdiagnosed. Because depressed patients constantly share their symptoms, major life events, and treatments on social media, researchers are turning to user-generated digital traces on social media for depression detection. Such methods have distinct advantages in combating depression because they can facilitate innovative approaches to fight depression and alleviate its social and economic burden. However, most existing studies lack effective means to incorporate established medical domain knowledge in depression detection or suffer from feature extraction difficulties that impede greater performance. Following the design science research paradigm, we propose a Deep Knowledge-aware Depression Detection (DKDD) framework to accurately detect social media users at risk of depression and explain the critical factors that contribute to such detection. Extensive empirical studies with real-world data demonstrate that, by incorporating domain knowledge, our method outperforms existing state-of-the-art methods. Our work has significant implications for IS research in knowledge-aware machine learning, digital traces utilization, and NLP research in IS. Practically, by providing early detection and explaining the critical factors, DKDD can supplement clinical depression screening and enable large-scale evaluations of a population's mental health status.

If your vehicle were self-driving on the road, will it crash into a truck towing a trailer as Tesla did in March 2019?a Despite the fatal accidents involving autonomous vehicles, such vehicles represent an unstoppable trend that will reshape the world. In this Viewpoint, we highlight why current autonomous vehicles would not be preferred by their users. Furthermore, we present a concise framework for profiling the characteristics of various autonomous vehicles based on intelligence quotient (IQ), ethical quotient (EQ), and adversity quotient (AQ). As presented in Figure 1, there are already major players focused on the automated driving market.

Speaking of the Millennial and the next generation, which distinguishes us from our predecessors is discovery, humans have now created and further built almost everything we can touch virtually. The only thing that is common among us, our predecessors and the next generation is the brain – which changes our communication behavior and how we view things. Artificial intelligence, most commonly known as AI has been a forecast for decades but was initially associated with robots only. However, at that time, AI joined in almost everything we used and called smart. AI is something where software acts as a human; show behavior like humans.

Artificial Intelligence (AI) has rapidly emerged as a key disruptive technology in the 21st century. At the heart of modern AI lies Deep Learning (DL), an emerging class of algorithms that has enabled today's platforms and organizations to operate at unprecedented efficiency, effectiveness, and scale. Despite significant interest, IS contributions in DL have been limited, which we argue is in part due to issues with defining, positioning, and conducting DL research. Recognizing the tremendous opportunity here for the IS community, this work clarifies, streamlines, and presents approaches for IS scholars to make timely and high-impact contributions. Related to this broader goal, this paper makes five timely contributions. First, we systematically summarize the major components of DL in a novel Deep Learning for Information Systems Research (DL-ISR) schematic that illustrates how technical DL processes are driven by key factors from an application environment. Second, we present a novel Knowledge Contribution Framework (KCF) to help IS scholars position their DL contributions for maximum impact. Third, we provide ten guidelines to help IS scholars generate rigorous and relevant DL-ISR in a systematic, high-quality fashion. Fourth, we present a review of prevailing journal and conference venues to examine how IS scholars have leveraged DL for various research inquiries. Finally, we provide a unique perspective on how IS scholars can formulate DL-ISR inquiries by carefully considering the interplay of business function(s), application areas(s), and the KCF. This perspective intentionally emphasizes inter-disciplinary, intra-disciplinary, and cross-IS tradition perspectives. Taken together, these contributions provide IS scholars a timely framework to advance the scale, scope, and impact of deep learning research.

Speaking of Millennials and the next rising generation -- what differentiates us from our predecessors is the inventions. Humans have now created and add further constructions to almost everything that we can touch in our virtual lives. The only thing common between us, our predecessors and the next generation will be our brains. There is a change in the demeanor of the Millennial -- and those who care to keep up with them, who are older. The mode of communication is changing -- and how the Millennial looks at things.

Person-Job Fit is the process of matching the right talent for the right job by identifying talent competencies that are required for the job. While many qualitative efforts have been made in related fields, it still lacks of quantitative ways of measuring talent competencies as well as the job's talent requirements. To this end, in this paper, we propose a novel end-to-end data-driven model based on Convolutional Neural Network (CNN), namely Person-Job Fit Neural Network (PJFNN), for matching a talent qualification to the requirements of a job. To be specific, PJFNN is a bipartite neural network which can effectively learn the joint representation of Person-Job fitness from historical job applications. In particular, due to the design of a hierarchical representation structure, PJFNN can not only estimate whether a candidate fits a job, but also identify which specific requirement items in the job posting are satisfied by the candidate by measuring the distances between corresponding latent representations. Finally, the extensive experiments on a large-scale real-world dataset clearly validate the performance of PJFNN in terms of Person-Job Fit prediction. Also, we provide effective data visualization to show some job and talent benchmark insights obtained by PJFNN.

'In god we trust, everyone else please bring data' Analytics can perhaps be taken to be the basis of building the foundation of a society based on reason and logic, not just feelings or gut. It not only helps in finding patterns through an iterative process but also in making predictions on never before seen data. Marketing has a great deal to benefit from this field which is still in its budding phase. The main trigger of such frenzy over data science and analytics can be owed [1] to not only modern cheaper sources of data storage but also to high processing power of systems, ease of internet connectivity and enhanced security features. There are numerous models and algorithms [2] for implementation of machine learning, however the coverage of the article is around the future possibilities of analytics applications, especially with a marketing perspective.