Human-Computer Negotiations: A Systematic Evaluation of the Effects of Timespan, Tactic, and Search Mechanism

Artificial Intelligence and Computer Agents are having a substantial impact on our everyday lives. The current paper focuses on the prospects of humans negotiating with computer agents in e-commerce settings. We conducted experiments where the subjects negotiated the purchase of mobile plans with computer agents acting as sellers. Three time-based negotiation tactics and two search mechanisms were employed in synchronous vs. asynchronous sessions. The results suggest that computer agents’ negotiation tactics and search mechanisms have significant effects on both the subjective and objective outcomes of the negotiations, while timespan has marginal effects on the agreement rate of the negotiation

Artificial Intelligence in Human Resources Management: A Scoping Review

There is a growing interest in the application of Artificial Intelligence in Human Resources Management, but there remains a substantial gap between the promise of AI and its practical application in organizations. In order to guide future research, a scoping review was conducted: 85 articles were identified and classified based on the 6 dimensions of the human resource Life Cycle. A seventh dimension – Legal and Ethical Issues – was also identified and integrated into the existing HR Life Cycle framework. Implications and future research opportunities are discussed

Can a Negotiator Build a Tough Impression Without Chatting? —— Implicit Power and its Influence on Human-Computer Negotiation

In this paper, we studied the influence of implicit power in an e-commerce setting where humans negotiated with computer agents. Implicit power is defined as a kind of perceived power gained indirectly through offer exchange. In much of the past research, power was always considered to be expressed directly through chat or natural language communications during negotiation. We suggest that there is another mode of expressing power other than chat: implicitly influencing. Specifically, we designed an experiment where several aspects of implicit power were studied: anchoring, agent profile image, and experiment subjects’ personality. In our experiment, the subjects negotiated the purchase of a laptop with computer agents acting as sellers. The result suggested that implicit power indeed influenced the negotiation result

Crisis Communications on Social Media: Insights from Canadian Officials Twitter Presence during COVID-19 Pandemic

COVID-19 pandemic is a unique case in crisis management given its length, scale, several different response systems, and public officials' extensive social media use for crisis communication. Leveraging text mining techniques, we examine Canadian officials' presence on Twitter during the pandemic by focusing on their COVID-19-related content. We identified eight themes of discussion that unveil 37 relevant sub-themes. Concentrating on the COVID-19-addressing themes, we reveal that educating citizens on the safety information and keeping them informed with the latest crisis information was the Canadian officials' primary focus during the pandemic. To fight COVID-19, Canadian officials used four policies, and to implement those, they promoted eight measures and practices. According to the volume of generated content, the evolution of COVID-19-addressing themes over time, and their coexistence; Test and trace was the most advocated policy by emphasizing screening the symptoms. To stop the spread of COVID-19, Canadian officials promoted wearing Mask, Social distancing, Hand washing, and Stay home, where Mask and Social distancing were the most frequent practices. Our study contributes to crisis communication and management by depicting how Canadian officials leveraged social media during such a big-scale crisis

CRISIS COMMUNICATION DURING HEALTH CRISES: THE CASE OF CANADIAN OFFICIALS’ SOCIAL MEDIA PRESENCE DURING THE COVID-19 PANDEMIC

To effectively manage a health crisis, citizens need to have shared Situational Awareness (SA) of the crisis. This study proposes that the public draws upon shared mental models of the crisis to achieve shared SA. Declarative, procedural, and strategic knowledge bases comprise the essential aspects of shared mental models of mission-critical situations like the COVID-19 pandemic. Therefore, public officials must provide a constant flow of crisis declarative, procedural, and strategic knowledge on social media. This study investigates Canadian officials’ presence on Twitter during the COVID-19 pandemic. Analyzing a dataset of 213,089 Canadian officials’ tweets shows that their presence was either for health crisis management (73.26%) or crisis-related topics (46.66%). Declarative (72.03%), procedural (38.1%), and strategic knowledge (30.18%) comprised 96% of the health crisis management tweets. This study informs research and practice by analyzing the essential role of knowledge types in creating a shared SA in managing health crises

Agents and E-commerce: Beyond Automation

The fast-growing information and communication technologies have shifted the contemporary commerce in both its information and market spaces. Businesses demand a new generation of agile and adaptive commerce systems. Towards this end, software agents, a type of autonomous artifacts, have been viewed as a promising solution. They have been taking an increasingly important part in facilitating e-commerce operations in the last two decades. This article presents a systematized overview of the diversity of agent applications in commerce. The paper argues that agents start playing more substantial role in determining social affairs. They also have a strong potential to be used to build the future highly responsive and smart e-commerce systems. The opportunities and challenges presented by proliferation of agent technologies in e-commerce necessitate the development of insights into their place in information systems research, as well as practical implications for the management

Artificial Intelligence in Human Resources Management: A Review and Research Agenda

Background: Researchers and practitioners both exhibit a growing interest in the application of Artificial Intelligence in Human Resources Management. However, research shows that there remains a substantial gap between the promise of AI and its practical application in organizations. Previous research has identified some of the challenges facing the application of Artificial Intelligence in Human Resources Management. Among these challenges is the varied nature of Human Resources functions. To address this, we adopt the Human Resource Life Cycle, which is composed of 6 dimensions that closely mirror the Human Resource functions that exist in many organizations: 1) Strategic Planning, 2) Recruitment and Deployment, 3) Training and Development, 4) Performance Management, 5) Compensation Management, and 6) Human Relations Management. Method: Through a scoping literature review, we have identified 85 articles on the topic and classified them based on the 6 dimensions of the Human Resource Life Cycle. Results: Our scoping review found that Artificial Intelligence has already been studied in relation to all 6 dimensions of the Human Resource Life Cycle. In addition, a seventh dimension was identified and integrated into the existing Human Resource Life Cycle framework: Legal and Ethical Issues. Based on the scoping review, a research agenda is presented to provide guidance for future research in the field of Artificial Intelligence in Human Resources Management. Conclusion: All 6 dimensions of the Human Resource Life Cycle, along with the seventh dimension – Legal and Ethical Issues – are already present in the literature. Future research could focus on the impact of AI on connections between dimensions, as well as the impact on HR-specific outcomes. Practitioners must recognize the limitations related to the application of AI in Human Resources Management, even though AI should still be viewed as a solution to many challenges facing Human Resources Management in organizations

Human-Agent Negotiations: The Impact Agents’ Concession Schedule and Task Complexity on Agreements

Employment of software agents for conducting negotiations with online customers promises to increase the flexibility and reach of the exchange mechanism and reduce transaction costs. Past research had suggested different negotiation tactics for the agents, and had used them in experimental settings against human negotiators. This work explores the interaction between negotiation strategies and the complexity of the negotiation task as represented by the number of negotiation issues. Including more issues in a negotiation potentially allows the parties more space to maneuver and, thus, promises higher likelihood of agreement. In practice, the consideration of more issues requires higher cognitive effort, which could have a negative effect on reaching an agreement. The results of human–agent negotiation experiments conducted at a major Canadian university revealed that there is an interaction between chosen strategy and task complexity. Also, when competitive strategy was employed, the agents\u27 utility was the highest. Because competitive strategy resulted in fewer agreements the average utility per agent was the highest in the compromising–competitive strategy