Persuasion can be a complex process. Persuaders may need to use a high degree of sensitivity to understand a persuadee's states, traits, and values. They must navigate the nuanced field of human interaction. Research on persuasive systems often overlooks the delicate nature of persuasion, favoring "one-size-fits-all" approaches and risking the alienation of certain users. This study examines the considerations made by professional burglary prevention advisors when persuading clients to enhance their home security. It illustrates how advisors adapt their approaches based on each advisee's states and traits. Specifically, the study reveals how advisors deviate from intended and technologically supported practices to accommodate the individual attributes of their advisees. It identifies multiple advisee-specific aspects likely to moderate the effectiveness of persuasive efforts and suggests strategies for addressing these differences. These findings are relevant for designing personalized persuasive systems that rely on conversational modes of persuasion.

Multiorganizational, multistakeholder (MO-MS) collaborations that may span organizational and national boundaries, present design challenges beyond those of smaller-scale collaborations. This study opens an exploratory research stream to discover and document design concerns for MO-MS collaboration systems beyond those of the single-task collaborations that have been the primary focus of collaboration engineering research. We chose the healthcare industry as the first target for this research because it has attributes common to many MO-MS domains, and because it faces significant challenges on a global scale, like the recent COVID-19 pandemic, for which MO-MS collaboration could offer solutions, as, for example, evidenced by the rapid collaborative development and distribution of COVID-19 vaccines. To this end, we reviewed 6,609 articles to find 100 articles that offered insights about the design of MO-MS collaboration systems, then conducted 50 semi-structured interviews in two countries with expert practitioners in the field. From those sources, we derived an eleven-category set of design concerns for MO-MS collaboration systems and argue their generalizability to other MO-MS domains. We offer exemplar probe questions that designers can use to increase the breadth and depth of requirements gathering for MO-MS collaboration systems.

Convergence is a collaborative activity in which members of group focus on what they consider the most promising or important contributions resulting from an ideation activity. Convergence is critical in helping a group focus their efforts on issues that are worthy of further attention. In the current study, we further research in this area by exploring and characterizing the effects of a particular convergence intervention, the FastFocus technique, in the context of a crowdsourcing project. We conducted an exploratory case study of artifacts generated by a crowd of managers addressing a real problem identification and clarification task in a large financial services organization. Using an online crowdsourcing tool, a professional facilitator led participants during preset periods through a convergence activity that focused on the brainstorming contributions that had been generated prior. To better understand the effects of the convergence technique on the group's ideas, we compared the raw problem statements to the final output of the convergence activities in terms of the number of unique ideas present, as well as the ambiguity of the ideas. Using the FastFocus convergence technique reduced the number of concepts by 76%. Ambiguity was reduced from 45% in the set of problem statements to 3% in the converged set of problem statements. We demonstrate with these findings that the outcomes of group convergence processes in real settings can be measured, enabling future research which seeks to evaluate and understand convergence in groups. Aspects of brainstorming instructions were also identified that may make it possible to reduce the ambiguity of problem statements.

Satisfaction is a central concern to IS research and practice because people who feel dissatisfied by system experiences tend to abandon them even if they create substantial value, while those who feel satisfied tend to continue use. The literature offers many models of satisfaction that make conflicting predictions, yet there is ample empirical evidence to support each. Yield Shift Theory (YST) was derived to resolve this paradox. This paper reports an experimental study to test a counterintuitive prediction of YST, i.e. that, under certain conditions, goal-replacement stimuli should invoke differing satisfaction responses toward identical system experiences. 211 students in an asynchronous online undergraduate course were assigned to positive or negative goal replacement treatments before reporting satisfaction with the learning experience. Positivetreatment students reported higher average satisfaction scores than did negative treatment students, although all had identical learning stimuli. Results offer support for the logic of YST’s and suggest that it may be useful to IS professionals to improve stakeholder satisfaction toward the elements of information systems, thereby increasing the likelihood of system success.

In the 1960s, software development centered on single-purpose applications to run on stand-alone computers. The intervening decades have seen exponential growth in system complexity with, for example, integrated enterprise-wide suites of integrated capabilities serving users across the globe. Enterprise system infrastructure includes multiple levels of security, collaboration capabilities for the people working as teams, fraud detection, and data fusion, to name but a few. These aspects add to the complexity of the application. Also, software can do more as a result of processing speed and data storage. This special issue presents three papers, each of which considers the complexity of current systems from a different perspective. The first paper, “Decision Problems in Blockchain Governance: Old Wine in New Bottles or Walking in Someone Else’s Shoes? ” by Rafael Ziolkowski, Gianluca Miscione, and Gerhard Schwabe, addresses the complex question of managing software capabilities built on blockchain technology. Current research presents a wide array of novel potentially disruptive blockchain application domains, but a recent Gartner survey of CEO’s found that few companies have current plans to implement such applications. A primary impediment appears to be that managers still do not understand how it works or what they can do with it. This paper focuses on complex issues pertaining to the governance of blockchain systems derived from examination of 14 such systems in four application domains. Based on academic literature, semi-structured interviews with representatives from those organizations, and content analysis of grey literature, common problems in blockchain governance have been singled out and contextualized. The identification of these problems enriches the scarce body of knowledge on the governance of blockchain systems, resulting in a better understanding of how blockchain governance links to existing concepts and how it is enacted in practice. The next paper, “Idea Convergence Quality in Open Innovation Crowdsourcing: A Cognitive Load Perspective” by Xusen Chen, Shizuan Fu, Triparna de Vreede, GertJan de Vreede, Isabella Seeber, Ronald Maier, and Barbara Weber, examines another complexity challenge. Today’s systems can support large-scale collaboration involving hundreds or thousands of people who are using vast stores of semi-structured and unstructured data: how can participants converge on and build shared understanding of the ideas that will be useful for attaining their goals in a short amount of time? This paper prototypes and tests a solution. The exemplar domain for this study is an open innovation crowdsourcing application for online crowds that can quickly converge from massive

Advances in artificial intelligence (AI) technologies have inspired businesses and researchers to identify new ways in which AI can improve our way of life. One such quest lies in giving AIs complex human capabilities like leadership. We take the first step towards that goal and propose a pattern-based approach to leadership. We argue that leadership best practices are actually a series of mini interventions each of which results in a consistent and desired response from the followers. When codified, these repeatable interventions can serve as foundational blocks for AI algorithms. To this end, we introduce LeadLets: A pattern language that codifies named, scripted, and repeatable leadership techniques that have a predictable influence causing a purposeful effect on one or more individuals. We argue that a pattern-based approach such as LeadLets can create leadership templates that inform programing leadership behavior into AI artifacts and designing leaders development programs.

Many governments and organizations recognize the potential of open innovation (OI) models to create value with large numbers of people beyond the organization. It can be challenging, however, to design an effective collaborative process for a massive group. Collaboration engineering (CE) is an approach for the design and deployment of repeatable collaborative work practices that can be executed by practitioners themselves without the ongoing support of external collaboration engineers. To manage the complexity of the design process, they use a modeling technique called facilitation process models (FPM) to capture high-level design decisions that serve different purposes, such as documenting and communicating a design, etc. FPM, however, was developed to support designs for groups of fewer than 100 people. It does not yet represent design elements that become important when designing for groups of hundreds or thousands of participants, which can be found in many OI settings. We use a design science approach to identify the limitations of the original FPM and derive requirements for extending FPM. This article contributes to the CE and to the OI literature by offering an FPM 2.0 that assists CE designers to design new OI processes, with a special focus on outside-in OI.

Research shows that innovation training can increase the number of innovative ideas that are proposed and successfully executed by an organization. Training satisfaction is also a strong predictor of the degree to which people use the knowledge they gain in training. To improve innovation training processes, therefore, it would be useful to have a theoretically sound, empirically validated instrument to measure innovation training satisfaction. We propose and validate such an instrument derived from the Yield Shift Theory of satisfaction in a field study of innovation training satisfaction in the U.S.A and China. The findings of the study demonstrated the convergent and discriminant validity of the instrument, and the results were consistent with the causal relationships proposed by the theory, suggesting that the theory may be a useful explanation for satisfaction effects and as a way to measure training satisfaction. We discuss the implications of the findings for research and practice.

Collaboration Engineering (CE) is an approach for the design and deployment of repeatable collaborative work practices that can be executed by practitioners themselves without the ongoing support of external collaboration professionals. A key design activity in CE concerns modeling current and future collaborative work practices. CE researchers and practitioners have used the Facilitation Process Model (FPM) technique. However, this modeling technique suffers from a number of shortcomings to model contemporary collaborative work practices. We use a design science approach to identify the main challenges with the original FPM technique, derive requirements and design a revised modeling technique that is based on the current technique enriched by BPMN 2.0 elements. This paper contributes to the CE literature by offering a revised FPM technique that assists CE-designers to capture new forms of collaborative work practices.

Much Collaboration Engineering research focuses on collaboration systems for teams of five to fifty members. That research can also inform large-scale multi-organizational multi-stakeholder (MO-MS) collaborations such as disaster relief, joint ventures, and healthcare. These larger contexts, though, present design concerns beyond those for smaller teams, and not all these concerns are self-evident. This paper explores the design concerns for IT-supported MO-MS collaboration. We selected the healthcare industry as the first exemplar domain for this inquiry mainly because research shows high potential benefits from, and substantial challenges to implementing systems for collaborative healthcare. We draw on an extensive literature review, and 50 semi-structured interviews with experts to discover and validate collaboration challenges presented by in-house and cloud-based IT services for healthcare. We derive an eleven-class typology of design concerns related to MO-MS collaboration, and derive requirements-elicitation design questions for each class. To demonstrate its utility, we draw on exploratory findings to elaborate the generalizable typology with design probes specific to healthcare collaboration systems.

Collaboration Engineering (CE) is an approach for the design and deployment of repeatable collaborative work practices that can be executed by domain experts without the ongoing support of external collaboration professionals. Since 2001, CE has been an active and productive topic of research that has attracted scientists from different backgrounds and disciplines. CE research started with studies on ways to transfer professional collaboration expertise to novices using a pattern language called thinkLets. Subsequent research focused on the development of theories to explain key phenomena, the development of a structured design methodology, training methods, technology support, design theories, and various field and experimental studies focusing on specific aspects of the CE approach. This paper details the contributions from CE research and practice based on a literature assessment of 331 publications. It extracts the key insights from the body of CE research thus far, identifies significant areas of inquiry that have not yet been explored, and looks ahead at the CE research opportunities that are emerging as our society, organizations, technologies, and the nature of collaboration evolve.

We propose solution-based probing as an extension of action design research. The core idea is that researchers bring a prototype solution (probe) into one or more fields and explore to synthesize robust and generalizable design knowledge, along with knowledge of the phenomena and correlations we discover. We believe proposing solutions creates opportunities for researchers to innovate and to document the impact. In addition, solutions can be effective probes for advancing theory, in terms of design theories and in creating exploratory foundations for behavioral and causal theory. We illustrate solution-based probing with four exemplar studies in the areas governance of municipalities, police work in informing citizens, learning in public schools, and naval decision making. We identify critical activities for ideating and initiating solution-based probing and for deriving sustainable solutions and scholarly knowledge from such studies. Finally, we discuss future directions for improving researchers' ability to conduct high-impact solutionbased probing research.

Information Systems (IS) is, and by definition must be, in part, an engineering research discipline. The unique purpose that defines IS as a discipline, and distinguishes us from our reference disc...

Humans will soon need to adapt to a collaborative setting in which technology becomes a smart collaboration partner that works with a group to achieve its goals. It is therefore time for collaboration researchers to explore the vast opportunities afforded by smart technology and to test its utility for enhancing team processes and outcomes. In this paper, we take a long view on the implications of smart technology for collaboration process design, and propose a research agenda for the next decade of collaboration research. We create a reference model to frame the research agenda.

Collaboration Engineering (CE) is an approach for the design and deployment of repeatable collaborative work practices that can be executed by practitioners without the ongoing support of external collaboration professionals. Research on CE started in the early 2000s with studies on ways to transfer professional collaboration expertise to novices using a pattern language called thinkLets. Subsequent research focused the development of theories to explain key phenomena, the development of a structured design methodology, training methods, technology support, design theories, and various field and experimental studies focusing on specific aspects of the CE approach. This paper provides an overview of the different phases and key contributions of CE research and looks ahead at the research opportunities that are emerging as our society, organizations, technologies, and the nature of collaboration evolve.

The authors find themselves in the midst of a global social transformation that is shaping the common perception of reality. The development of technology-enabled collaborative networks, virtual collaboration, structured collaboration processes, and digital team collaboration affects every part of society. Research on collaboration and collaboration systems has achieved sufficient maturity and scope that an overall conceptual definition of collaboration is now needed and possible. This article proposes a conceptual approach and terminology as a step towards bridging isolated communities of collaborating researchers in various fields. The authors offer a fundamental philosophical description of what collaboration is (and is not) based on relevant epistemological, metaphysical, and axiological insights derived from a synthesis of existing collaboration research, and the authors outline the most obvious needs for further research toward formalizing a more fully-realized philosophy of collaboration.

A Collaboration Engineering Methodology (CEM) comprises a set of defined, standardized, docu-mented, and discoverable objectives, deliverables, key actions, tools/templates, principles and policies for establishing effective, efficient, satisfying col-laborative work practices for high-value organi-zational tasks. First-generation CEMs address design and development CE solutions. Existing CEMs, though, focus on the design/build phase, but lack the pre-design and post-build elements that are common to methodologies for adjacent disciplines. We use Design Science Research to situate existing design/build CEMs in the larger context of CE programs and projects. We develop and validate an extended CEM in four phases: 1) Opportunity Assessment, 2) Development, 3) Deployment, and 4) Improvement (ODDI). Phase 1 concerns CE portfolio management and CE project planning; Phase 2 encapsulates existing design/build CEMs; Phase 3 concerns roll-out planning, change management, and implementation; Phase 4 concerns continuous optimization of a deployed work practice. The ODDI model advances CE another step towards becoming a fully realized professional practice, but more research is still required to derive a complete a design theory for CE.

An ongoing conversation in the Information Systems literature addresses the concern, "How can we conduct research that makes a difference?" A shortage of high-impact research will, over time, challenge the identity and weaken the viability of IS as an academic discipline. This paper presents the systematic high-impact research model (SHIR), an approach to conducting high-impact research. SHIR embodies the insight gained from three streams of high-impact research programs spanning more than 50 years. The SHIR framework rests on the proposition that IS researchers can produce higher-impact contributions by developing long-term research programs around major real-world issues, as opposed to ad hoc projects addressing a small piece of a large problem. These persistent research programs focus on addressing the entirety of an issue, by leveraging multidisciplinary, multiuniversity research centers that employ a breadth of research methods and large-scale projects. To function effectively, SHIR programs must be sustained by academic and practitioner partnerships, research centers, and outreach activities. We argue that SHIR research programs increase the likelihood of high impact research.