The Impact of Anthropomorphic and Functional Chatbot Design Features in Enterprise Collaboration Systems on User Acceptance

Information technology is rapidly changing the way how people collaborate in enterprises. Chatbots integrated into enterprise collaboration systems can strengthen collaboration culture and help reduce work overload. In light of a growing usage of chatbots in enterprise collaboration systems, we examine the influence of anthropomorphic and functional chatbot design features on user acceptance. We conducted a survey with professionals familiar with interacting with chatbots in a work environment. The results show a significant effect of anthropomorphic design features on perceived usefulness, with a strength four times the size of the effect of functional chatbot features. We suggest that researchers and practitioners alike dedicate priorities to anthropomorphic design features with the same magnitude as common for functional design features in chatbot design and research

How May I Help You? – State of the Art and Open Research Questions for Chatbots at the Digital Workplace

Chatbots become quite hyped in recent times as they can provide an intuitive and easy-to-use natural language human-computer interface. Nevertheless, they are not yet widespread in enterprises. Corresponding application areas for collaboration at digital workplaces are lacking and prior research contributions on this topic are limited. In this research paper, we aim at surveying the state of the art as well as showing future research topics. Thus, we conducted a structured literature review and showed that only few first research contributions exist. We also outline current potentials and objectives of chatbot applications. In the discussion of the results of our structured literature review, we show that research gaps are present. To tackle the research gaps, we derive open research questions

Requirements for AI-based Teammates: A Qualitative Inquiry in the Context of Creative Workshops

Innovation requires organizations to tap into the knowledge and creativity of teams. However, teams are confronted with massive amounts of data and information, necessitating a broad set of knowledge, methodologies, and approaches to solve problems and innovate. Consequently, team composition has become a critical challenge. Recent advances in artificial intelligence (AI) may assist in addressing this challenge. As AI is permeating both business and private sectors, organizational teams may be augmented with AI team members. However, given the nascent nature of this phenomenon, little is known about the specific roles and requirements for such AI teammates. Within an interview study we discover common challenges in teams and identify recurring capability gaps of participants and behaviors that negatively impact the team's collective performance. Based on our findings, we propose requirements for AI-based teammates to address these gaps and support beneficial collaboration between humans and AI in teams

LOOKING BENEATH THE TIP OF THE ICEBERG: THE TWO-SIDED NATURE OF CHATBOTS AND THEIR ROLES FOR DIGITAL FEEDBACK EXCHANGE

Enterprises are forecasted to spend more on chatbots than on mobile app development by 2021. Up to today little is known on the roles chatbots play in facilitating feedback exchange. However, digitization and automation put pressure on companies to setup digital work environments that enable reskilling of employees. Therefore, a structured analysis of feedback-related chatbots for Slack was conducted. Our results propose six archetypes that reveal the roles of chatbots in facilitating feedback exchange on performance, culture and ideas. We show that chatbots do not only consist of conversational agents integrated into instant messenger but are tightly linked to complementary front-end systems such as mobile and web apps. Like the upper part of an iceberg, the conversational agent is above water and visible within the chat, whereas many user interactions of feedback-related chatbots are only possible outside of the instant messenger. Further, we extract six design principles for chatbots as digital feedback systems. We do this by analyzing chatbots and linking empirically observed design features to (meta-)requirements derived from explanatory theory on feedback, self-determination and persuasive systems. The results suggest that chatbots benefit the social environment of conversation agents and the richness of the graphical user interface of external applications

Interactive Software Refactoring Bot

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153326/1/ASE2019_RefactoringBot__Copy_deepblue.pd

A Systematic Literature Review on Software Refactoring

Due to the growing complexity of software systems, there has been a dramatic increase in research and industry demand on refactoring. Refactoring research nowadays addresses challenges beyond code transformation to include, but not limited to, scheduling the opportune time to carry refactoring, recommending specific refactoring activities, detecting refactoring opportunities and testing the correctness of applied refactoring. Very few studies focused on the challenges that practitioners face when refactoring software systems and what should be the current refactoring research focus from the developers’perspective and based on the current literature. Without such knowledge, tool builders invest in the wrong direction, and researchers miss many opportunities for improving the practice of refactoring. In this thesis, we collected papers from several publication sources and analyzed them to identify what do developers ask about refactoring and the relevant topics in the field We found that developers and researchers are asking about design patterns, design and user interface refactoring, web services, parallel programming, and mobile apps. We also identified what popular refactoring challenges are the most difficult and the current important topics and questions related to refactoring. Moreover, we discovered gaps between existing research on refactoring and the challenges developers face.Master of ScienceSoftware Engineering, College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/154827/1/Jallal Elhazzat Final Thesis.pdfDescription of Jallal Elhazzat Final Thesis.pdf : Thesi

Chatbots at Digital Workplaces – A Grounded-Theory Approach for Surveying Application Areas and Objectives

Background: Chatbots are currently on the rise as more and more researchers tackle this topic from different perspectives. Simultaneously, workplaces and ways of working are increasingly changing in the context of digitalization. However, despite the promised benefits, the changes still show problems that should be tackled more purposefully by chatbots. Application areas and underlying objectives of a chatbot application at digital workplaces especially have not been researched yet. Method: To solve the existing problems and close the research gap, we did a qualitative empirical study based on the grounded-theory process. Therefore, we interviewed 29 experts in a cross-section of different industry sectors and sizes. The experts work in the information systems domain or have profound knowledge of (future) workplace design, especially regarding chatbots. Results: We identified three fundamental usage scenarios of chatbots in seven possible application areas. As a result of this, we found both divisional and cross-divisional application areas at workplaces. Furthermore, we detected fifteen underlying objectives of a chatbot operation, which can be categorized from direct over mid-level to indirect ones. We show dependencies between them, as well. Conclusions: Our results prove the applicability of chatbots in workplace settings. The chatbot operation seems especially fruitful in the support or the self-service domain, where it provides information, carries out processes, or captures process-related data. Additionally, automation, workload reduction, and cost reduction are the fundamental objectives of chatbots in workplace scenarios. With this study, we contribute to the scientific knowledge base by providing knowledge from practice for future research approaches and closing the outlined research gap. Available at: https://aisel.aisnet.org/pajais/vol12/iss2/3

Psychological and Agentic Effects of Human-Bot Delegation in Open-Source Software Development (OSSD) Communities: An Empirical Investigation of Information Systems Delegation Framework

Bots are agentic AI that automatically interact with software developers, also known as contributors, to coordinate work in open-source software development (OSSD). The proliferation of bots in OSSD communities like Kubernetes led them to become the disruptive new teammates central to the coordinating mechanisms for implementing source code changes using pull requests. These bots provide procedural rationality and enhance predictability in OSSD communities akin to clerks and managers in traditional organizations. However, despite acknowledging the criticality of the bots’ agentic role in coordinating the OSSD, research on the OSSD dynamics in the Information Systems literature has failed to reveal the role of bots on contributors’ behavioral outcomes. Bot-driven OSSD communities serve as an excellent example of successful new forms of organizing that necessitate theoretical modeling of the human-bot collaboration, the central mechanism, enhancing contribution patterns, and the overall sustainability of the OSSD community. Using 289 survey responses from Kubernetes contributors, we empirically tested the model and identified the factors enabling contributors’ fit appraisal of collaborating with the bots. Contributors appraised adaptive and reliable bots that offered explainable feedback. Our findings highlight the role of contributors’ self-efficacy and their instrumentality in the project as the predictors of their fit appraisal. More importantly, the empirical results revealed the role of agentic coordination as the enabler of contributors’ satisfaction via explicit and implicit coordination mechanisms. Furthermore, we find that contributors intend to continue contributing if satisfied with their contribution experience, leading to their commitment to the OSSD community. The model offers a more nuanced perspective of the human-bot collaboration in OSSD communities. A profound understanding of the dyadic delegation patterns, leading to contributor satisfaction, could inform researchers and practitioners in designing bots and OSSD platforms that ultimately enhance the contribution experiences, leading to their willingness to continue contributing to the OSSD community. Our results and discussion of findings offer actionable insights to enable bot design for optimal utilization in OSSD and other similar knowledge-intensive voluntary communities. The study findings offer implications for the future forms of organizing, the design of human-bot collaborative environments, and the sustainability and success of OSSD communities