Organisational rules, both created internally and externally mandated, are vital to an enterprise. Yet, understanding and managing these rules is problematic, as they are a part of a complex system. Thus, there is a need to view them in a complex setting of organisational actors and interactions. It has been suggested that enterprises, particularly in situations like collaboration in healthcare, should be analysed as complex adaptive systems (CAS). However, only some enterprise modelling contributions can represent perspectives of CAS theory. In this paper, we set out to examine how organisational rules in complex adaptive systems has been modelled. A systematic mapping study was conducted on modelling languages of organisational rules in collaborations, resulting in 22 identified languages. The constructs and modelling patterns of the identified languages were mapped against an analytical framework that included 15 concepts from CAS theory. Overall, even though most CAS concepts had yet to be addressed by the identified languages, potentially useful approaches were found, related to: abstraction of large organisational rule systems through power relations; interpretation and implementation of rules; feedback loops to rule-makers, including delays.

Organisational rules and regulations are vital components of a business. However, their growing numbers, interdependencies, and often unpredictable interactions with social agents make them challenging to manage. Enterprise modelling has proven an effective technique for sensemaking and creating a shared understanding of organisational structures, such as rules and goals. However, what is captured or not in a model depends on the theory used to examine the organisation, whether implicit or explicit. Particularly in healthcare, many scholars view organisations from the lens of Complex Adaptive Systems (CAS), rather than General Systems Theory (GST). This paper discusses how enterprise modelling of organisational rules grounded in CAS theory will have a different focus than if grounded in GST. Four key themes for are identified: abstraction, rule-agent interaction, emergence, and feedback channels. Each is discussed in light of privacy regulation and healthcare practice, and proposals are made for future research directions in enterprise modelling.

Artificial Intelligence (AI) must be directed at humane ends. The development of AI has produced great uncertainties of ensuring AI alignment with human values (AI value alignment) through AI operations from design to use. For the purposes of addressing this problem, we adopt the phenomenological theories of material values and technological mediation to be that beginning step. In this paper, we first discuss the AI value alignment from the relevant AI studies. Second, we briefly present what are material values and technological mediation and reflect on the AI value alignment through the lenses of these theories. We conclude that a set of finite human values can be defined and adapted to the stable life tasks that AI systems will be called upon to accomplish. The AI value alignment can also be fostered between designers and users through technological mediation. Upon that foundation, we propose a set of common principles to understand the AI value alignment through phenomenological theories. This paper contributes the unique knowledge of phenomenological theories to the discourse on AI alignment with human values.

To improve the academic integrity of online examination, digital proctoring systems have been implemented in higher education worldwide, particularly during the COVID-19 pandemic. In this paper, we conducted a literature review of the research on digital proctoring in higher education. We found 115 relevant publications in nine databases. We applied topic modeling methods to analyze the corpus which resulted in eight topics. The review shows that the previous studies focus largely on the systems’ development, adoption of the systems, the effects of proctored online exams on students’ performance, and the legal, ethical, security, and privacy issues of digital proctoring. The annual topic trends indicate future research concerns, such as systems’ development, online programs (MOOCs) and proctoring, along with various issues of using digital proctoring. The results of the review provide useful insights as well asimplications for future research on digital proctoring, a crucial process for digitalizing higher education.

The need for a systematic approach to work with artificial intelligence (AI) is current and rapidly growing. It is important that Information Systems researchers get ahead of public sentiment and be able to provide proactive commentary about the current state-of-the-art, as well as solutions for future systems. One critical question is how can we ensure value alignment between AI and human values through AI operations from design to use? For the purposes of this discussion, we adopt the phenomenological theories of material values and technological mediation to be that beginning step. In this paper, we firstly analyze the AI phenomenon from selected resources from the top IS research outlets (basket of 8 journals and 5 AI journals in IS). Secondly, we briefly present what are material values and technological mediation and reflect on the AI value alignment principle through the lenses of these theories. Supported by these new understandings and reflections, we propose to build a common principle of human values to understand the AI value alignment principle through phenomenological theories. The paper contributes the unique aspect of material values to the discourse within current AI research.

Green Information Systems (IS) are defined in terms of certain sustainability-related characteristics. Sustainability itself is a concept based on subjective values and value judgments, which are political, value-laden, and context-dependent. However, Green IS literature does not provide a sufficient understanding of such subjective values nor their treatment. Also, value-judgments for Green IS have hardly been considered. We adapt material value-ethics to expose the fundamentals of subjective values. Reviewing and synthesizing work in which subjective values and value judgments have been explicitly considered in sustainability decision-making, we improve our understanding of their use and formalization. Finally, we discuss our findings through the lens of material-value ethics, and offer reflective arguments towards clarifying the role of values in Green IS. The paper contributes to a deeper understanding of subjective values and subjective value judgments for sustainability, along with their critical and significant implications for Green IS research.

A geographically-dispersed user base presents challenges for requirements engineering in enterprise systems, with problems including limited access to end users, a lack of trust between users and developers, and poor informal communication. Not only must these standard organizational and personnel difficulties be taken into consideration, but geographic dispersal also complicates the steps of the requirements engineering process. This paper investigates whether a method can be created to develop requirements for just such geographically-distributed outposts within the same organization. A psychological instrument that measures basic values was coupled with individual interviews to collect the value profiles for frontline security guards throughout Sweden. The results were used to generate requirements for field equipment, with goal- and feature models generated as a final result.

Consumer choices are enormously influential in the success of the companies and organizations behind the highly competitive global service and product offerings of today. Consumer choice relates to preference, i.e. a set of assumptions a person creates around a service or a product such as convenience, utility or aesthetics. Furthermore, consumer preferences allow ranking of different assumptions about products or services based on the expected or to-be-experienced satisfaction of consuming them. In our previous work, we proposed a conceptualization of consumer preferences—the Consumer Preference Meta-Model (CPMM)—to enable a classification and ranking of the preferences that would be the basis for deciding which of would be considered to be developed into supporting information systems/services. In this study we collect consumer preferences through crowdsourcing, and in particular Twitter, because of its increasing popularity as a source of up-to-date comments and information about current services and products. The tweets of four major American airlines were processed using different techniques from natural language processing (NLP) that enabled the classification of their objectives, content, and importance within CPMM. By next mapping the highest-ranked results from CPMM to goal models enabled a model-based linkage from a corpus of preferences contained within short texts to high-level requirements for system/services.

There is a pressing need in the modern business environment for business-supporting software products to address countless consumers’ desires, where customer orientation is a key success factor. Consumer preference is thus an essential input for the requirements elicitation process of public-facing enterprise systems. Previous studies in this area have proposed a process to capture and translate consumer preferences into system-related goals using the Consumer Preference Meta-Model (CPMM) used to integrate consumer values from the marketing domain into objectives of information systems. However, there exists a knowledge gap between how this process can be automated at a large scale, when massive data sources, such as social media data, are used as inputs for the process. To address this problem, a case in which social media data related to four major US airlines is collected from Twitter, is analyzed by a set of text mining techniques and hosted in a consumer preference model, and is further translated to goal models in the ADOxx modelling platform. The analysis of experimental results revealed that the collection, recognition, model creation, and mapping of consumer preferences can be fully or partly automated. The result of this study is a semi-automated method for capturing and filtering consumer preferences as goals for system development, a method which significantly increases the efficiency of large-scale consumer data processing.

The challenges of developing the information systems (IS) that support modern enterprises are becoming less about engineering and more about people. Many of the technical issues of the past, such as hardware size and power, connectivity, and robust software, are engineering problems that have largely been solved. In the next stage of computing, the human factor will be far more important than it has been in the past: the colors of an interface or the shape of an icon are the engineering problems of the past, and the availability and usefulness of such basic solutions is rapidly coming to a close. A new paradigm is needed that provides a roadmap of higher level conceptions and values, one about humane computing.

A part of this older, mechanistic approach are quantitative, economic values whose impact on IS are readily visible and acknowledged within software engineering. However, qualitative values, and in particular consumer preferences, have been researched to a lesser degree, and there has been very little direct application.  To create the next-generation information systems, requirements engineers and systems developers need new methods to capture the real preferences of consumers, conceptualize these abstract concepts, and then relate such preferences to concrete requirements for information systems.

To address this problem, this thesis establishes a conceptual link between the preferences of consumers and system requirements by accommodating the variations between them and expressing them via a conceptual model. Modeling such preferences and values so that they can be used as requirements for IS development is the primary contribution of this work. This is accomplished via a design science research paradigm to support the creation of the works’ primary artifact—the Consumer Preference-aware Meta-Model (CPMM).

CPMM is intended to improve the alignment between business and information systems by capturing and concretizing the real preferences of consumers and then expressing such preferences via the requirements engineering process, with the eventual output being information systems. CPMM’s development relies on theoretical research contributions within three areas in information systems—Business Strategy, Enterprise Architecture, and Requirements Engineering—whose relationships to consumer values have been under-researched and under-applied.

The case studies included in this thesis each demonstrate the significance of consumer preferences to each of these three areas.  In the first, a set of logical mappings between CPMM and a common approach to business strategy (strategy maps/balanced scorecards) is produced. In the second, CPMM provides the conceptual undergirding to process a massive amount of unstructured consumer-generated text to generate system requirements for the airline industry. In the concluding case, an investigation of foreign and domestic students at Swedish universities is structured through CPMM, one that first discovers the requirements for a consumer preference-based online education and then produces feature models for such a software product line-based system. The significance of CPMM as a lens for discovering new concepts and highlighting important information within consumer preference data is clearly seen, and the usefulness of the meta-model is demonstrated by its broad and beneficial applicability within information systems practice and research.