Strategic decision process in SME’s context : a new perspective using indigenous, institution, firm, and environment characteristics

Purpose: This paper aimed to contribute to fill the gap of the strategic decision-making process framework in the context of an SME. Not only adds a new perspective in the strategic decision-making process framework, but also suggests new perspectives of firms, environment, institutions and indigenous characteristics as the new approach that magnifies strategic decision-making’s models with respect to SMEs’ scale. Design/methodology/approach: The purposive sampling method has been used in this study. First, we used the CEO as the respondent to fulfilll decision involvement criteria. Second, the samples were selected based on the criterion that the last project of this SME has finished in the last 3 years. Then we choose a project bidding decision from construction SMEs to minimize decision bias. Finally, from 4253 SMEs listed in Papua, we finished with 350 respondents. The study had collected 156 SME's project decisions. Findings: The Heuristic decision, that previously neglected because of information bias and short decision process, deemed to be the most profound dimension in strategic decision making and demonstrated significant results toward SMEs’ project performance. All variables, exclude institution, shows good and significant results. The research uses project decision in the construction industry as the main unit analysis. Practical implications: Exploring strategic decision-making theory in the SME context could convince SME’s CEO to evaluate its external factors before taking a project, processing all the information needed toward its project performance. Originality/value: This heuristic measurement scale is the first valid and reliable tool, based on several previous researches (Busenitz and Barney, 1997; Artinger et al., 2015), that could identify and measure the heuristic process in a strategic decision process.peer-reviewe

Interval type–2 fuzzy decision making

This paper concerns itself with decision making under uncertainty and theconsideration of risk. Type-1 fuzzy logic by its (essentially) crisp nature is limited in modelling decision making as there is no uncertainty in the membership function. We are interested in the role that interval type–2 fuzzy sets might play in enhancing decision making. Previous work by Bellman and Zadeh considered decision making to be based on goals and constraint. They deployed type–1 fuzzy sets. This paper extends this notion to interval type–2 fuzzy sets and presents a new approach to using interval type-2 fuzzy sets in a decision making situation taking into account the risk associated with the decision making. The explicit consideration of risk levels increases the solution space of the decision process and thus enables better decisions. We explain the new approach and provide two examples to show how this new approach works

A canonical theory of dynamic decision-making

Decision-making behavior is studied in many very different fields, from medicine and eco- nomics to psychology and neuroscience, with major contributions from mathematics and statistics, computer science, AI, and other technical disciplines. However the conceptual- ization of what decision-making is and methods for studying it vary greatly and this has resulted in fragmentation of the field. A theory that can accommodate various perspectives may facilitate interdisciplinary working. We present such a theory in which decision-making is articulated as a set of canonical functions that are sufficiently general to accommodate diverse viewpoints, yet sufficiently precise that they can be instantiated in different ways for specific theoretical or practical purposes. The canons cover the whole decision cycle, from the framing of a decision based on the goals, beliefs, and background knowledge of the decision-maker to the formulation of decision options, establishing preferences over them, and making commitments. Commitments can lead to the initiation of new decisions and any step in the cycle can incorporate reasoning about previous decisions and the rationales for them, and lead to revising or abandoning existing commitments. The theory situates decision-making with respect to other high-level cognitive capabilities like problem solving, planning, and collaborative decision-making. The canonical approach is assessed in three domains: cognitive and neuropsychology, artificial intelligence, and decision engineering

Systematic Approach to A New Service Ideas Conceptualisation: Quantitative Decision Making

Contradictory opinions about some stages in a new service development cause many uncertainties, especially when transferring actions of theoretical implementation into practice. Such ambiguities are particularly typical for the process of conception formation, also evaluation and service system creation. The aim of the paper is to provide a justified model of new service concept(s) formation, its assessment and service system (new service technology) designing. Because of the lack of data about service concept formation, reference was also made to the sources of information about product concept creation. Scientific information on the issues of concept formation was analysed (systematised, structured and synthesised basing on sparse works of other authors) after being assessed in terms of logic and integrity, availability and practical appropriateness for service business. Research findings are used to develop a process model. It consists of the following main components: determining the purposefulness of new service development, concepts designing, concepts assessment according to two different sets of criteria, decision making about service system designing (new service technology designing). Application of the proposed model will show a real ways for the formation of new service concepts. Flexible construction of the model allows reducing time needed for the evaluation and implementation a new service

Likelihood decision functions

In both classical and Bayesian approaches, statistical inference is unified and generalized by the corresponding decision theory. This is not the case for the likelihood approach to statistical inference, in spite of the manifest success of the likelihood methods in statistics. The goal of the present work is to fill this gap, by extending the likelihood approach in order to cover decision making as well. The resulting decision functions, called likelihood decision functions, generalize the usual likelihood methods (such as ML estimators and LR tests), in the sense that these methods appear as the likelihood decision functions in particular decision problems. In general, the likelihood decision functions maintain some key properties of the usual likelihood methods, such as equivariance and asymptotic optimality. By unifying and generalizing the likelihood approach to statistical inference, the present work offers a new perspective on statistical methodology and on the connections among likelihood methods

Quantum decision making by social agents

The influence of additional information on the decision making of agents, who are interacting members of a society, is analyzed within the mathematical framework based on the use of quantum probabilities. The introduction of social interactions, which influence the decisions of individual agents, leads to a generalization of the quantum decision theory developed earlier by the authors for separate individuals. The generalized approach is free of the standard paradoxes of classical decision theory. This approach also explains the error-attenuation effects observed for the paradoxes occurring when decision makers, who are members of a society, consult with each other, increasing in this way the available mutual information. A precise correspondence between quantum decision theory and classical utility theory is formulated via the introduction of an intermediate probabilistic version of utility theory of a novel form, which obeys the requirement that zero-utility prospects should have zero probability weights.Comment: This paper has been withdrawn by the authors because a much extended and improved version has been submitted as arXiv:1510.02686 under the new title "Role of information in decision making of social agents

Integrated management of hierarchical levels: towards a CAPE tool

The integration of decision-making procedures usually assigned to different hierarchical production systems requires the use of complex mathematical models and high computational efforts, in addition to the need of an extensive management of data and knowledge within the production systems. This work addresses this integration problem and proposes a comprehensive solution approach, as well as guidelines for Computer Aided Process Engineering (CAPE) tools managing the corresponding cyberinfrastructure. This study presents a methodology based on a domain ontology which is used as the connector between the introduced data, the different available formulations developed to solve the decision-making problem, and the necessary information to build the finally required problem instance. The methodology has demonstrated its capability to help exploiting different available decision-making problem formulations in complex cases, leading to new applications and/or extensions of these available formulations in a robust and flexible way.Peer ReviewedPostprint (author's final draft