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This paper first explores the decision-making process in agile teams using scrum practices and second identifies factors that influence the decision-making process during the Sprint Planning and Daily Scrum Meetings. We conducted 34 semi-structured interviews and 18 observations across four agile teams. Our findings show that a rational decision-making process is sometimes followed in the Sprint Planning and Daily Scrum Meetings and that three factors can influence the rational decision-making process: sprint duration, experience and resource availability. Additionally, decisions are not always made in a collaborative manner by team members. This research contributes to the decision-making literature and project management literature by highlighting difficulties pertinent to decision making in agile teams.

For the design process of mechanical or electrical devices it is often necessary to consider multiple objectives. The design problem can then be formulated as multi-objective optimization problem. Multiple objectives can be conflicting and to pick a design solution a trade-off between those is required. A good trade-off is important for a successful product. Different decision making methods are available aiming towards a successful design trade-off; a commonly used method is the VIKOR method. This paper focuses on aspects of this method and reveals some weaknesses. However, a different normalization method is introduced that overcomes these aspects. Next, a minimum weight margin is established that gives information about the stability of a design solution picked by the VIKOR method. The weight margin is helpful for elucidating the decision maker’s uncertainty in the original weight assignment. The modified VIKOR method is then applied to the design of a wearable body sensor network and the design of an EEG electrode. The two design examples show the strength of the new modified VIKOR method resolving shortcomings by the original VIKOR method. Finally, the modified VIKOR method is compared with the theory of fuzzy measures and integrals as a multi-criteria decision making method.

Existing, staged models of consumer adoption behavior offer little in the way of theoretical justification, and their empirical validity is in doubt. A qualitative empirical study provides another view by exploring the behavioral decision process by which transportation consumers adopt alternatives to single occupancy vehicles. The study finds that adoption behavior consists of interplay between three conscious, cognitively distinct processes: selecting, evaluating and maintaining. All three processes are characterized in detail in this study. What emerges from the study's data is the first theory to explain the "how" of technology adoption in terms of endogenous mental processes. The study lays the foundation for a more comprehensive causal theory of the adoption process that will provide a step-by-step explanation of how events or life experiences cause a consumer's beliefs about a technology to change over time. It also identifies evaluating, selecting and maintaining as fundamental "microlaws" of innovation: regular rules that describe the generating processes of emergent innovations.

We compare different actual forms of democracy and analyse in which way they are variations of a “natural consensus decision process”. We analyse how a “pure consensus procedure” is open to boycott of a minority, and how a “consensus decision followed by majority voting” is open to “false play” by a majority. We introduce a “random consensus procedure” in order to come closer to a natural decision process, and investigate how for such decision procedure false play of a minority is plausible to happen. We introduce the combined notion of “quantum parliament” and “quantum decision procedure”, and prove it to be the only one, when applied after consensus decision, that is immune to false play. We define a new form of democracy applying a quantum consensus system for its decision procedures.

Understanding and addressing risks are fundamental to partnership success in offshore IT outsourcing. This chapter develops and demonstrates a fuzzy risk assessment framework to effectively assess risk for a client as well as a service provider prior to entering into a formal contract. The sources of risk in IT outsourcing were extracted based on industry survey and prior research. A fuzzy inference engine which embeds human expert knowledge expressed through natural language gives a superior capability to this approach. The case of offshore software development simulated using the fuzzy framework showed that this method could capture imprecise perceptions about risk factors and quantify them effectively. It also showed that human knowledge embedded as intelligence could effectively map and quantify sources of risk into different categories. This assessment could enable objective comparison of different projects and informed design of contracts and thus lead to partnership success in outsourcing.