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The purpose of this paper is to provide a theoretical framework of how knowledge management (KM) systems can facilitate the incorporation of big data into strategic decisions. Advanced analytics are becoming increasingly critical in making strategic decisions in any organization from the private to public sectors and from for-profit companies to not-for-profit organizations. Despite the growing importance of capturing, sharing and implementing people’s knowledge in organizations, it is still unclear how big data and the need for advanced analytics can inform and, if necessary, reform the design and implementation of KM systems.

To address this gap, a combined approach has been applied. The KM and data analysis systems implemented by companies were analyzed, and the analysis was complemented by a review of the extant literature.

Four types of data-based decisions and a set of ground rules are identified toward enabling KM systems to handle big data and advanced analytics.

The paper proposes a practical framework that takes into account the diverse combinations of data-based decisions. Suggestions are provided about how KM systems can be reformed to facilitate the incorporation of big data and advanced analytics into organizations’ strategic decision-making.

This is the first typology of data-based decision-making considering advanced analytics.

This paper aims to identify the expressions and flows of tacit knowledge in the unstructured decision process. In this type of process, decision-makers use not only the explicit knowledge but also aspects such as intuition, experience and other forms of tacit knowledge. The research developed a qualitative approach, through a study of multiple cases, and applied semi-structured interviews to ten executives. The analysis of data was carried out according to Flores (1994) interpretative analysis of text technique. Results indicated that there was the insertion of tacit knowledge in all unstructured decision-making routines. It was also detected the need to explicitly add the routine of evaluation to the Mintzberg et al.’s (1976) model as elements of tacit knowledge were also identified at this stage of the decision-making process.

The research has taken a qualitative approach, through a study of multiple cases, applying semi-structured interviews to ten executives. The analysis of data was carried out according to technique for interpretative analysis of the text.

Results indicated that there was tacit knowledge in all unstructured decision-making routines. Also detected was the need to explicitly add the routine of evaluation to the model.

It was unable to perform psychological studies to investigate the deepest cognitive and emotional aspects of managers, and it does not address, in depth, some issues that are related to tacit knowledge in decisions and that would be considered relevant.

Although this research was unable to dissect the composition of tacit knowledge in unstructured decision process, a better understanding of the aspects that make up the knowledge in question has been developed, providing some decision-making guidelines to managers.

The language between communications actors can share decision-making rules to assist in the production and process of arguments necessary for the debate, evaluation and attribution of institutionally recurrent decisions.

The original contribution is present in a detailed description of the expressions of flows of tacit knowledge in unstructured decision-making processes, based on the model of Mintzberg et al. (1976). From the influence of tacit knowledge, it was found that the model in question needs to consider the relevance of the evaluation phase, as a stage equivalent to the other described by Mintzberg et al. (1976). These aspects have been better explained in the introduction and conclusion. Participant observation was not possible because the decision had already been taken by the informant at the moment of the interviews.

This paper aims to introduce a structuring and processing model (SPM) as a framework for tourism decision making research.

The study employs McCracken's long interview to collect data in field settings. The study introduces advances in Mintzberg et al.'s approach to structuring seemingly unstructured decision making to explain travelers' decision‐making processes.

SPM enables mapping and comparing visitors' plans, motivations, choices, and consequences. The results demonstrate nuanced decision‐behavior dynamics and complexities of visitors' travel‐related unconscious/conscious thinking and behavior.

SPM does not attempt to generalize findings to large survey samples.

Travel planning and execution dynamics dictate that a decision‐making funnel metaphor in consumer research does not capture such trip complexity because additional decisions are made when the traveler arrives at the destination.

SPM is dynamic and inclusive explaining simultaneous planning elements as well as considering sub‐decisions occurring before and after different phases in the process. This model includes both conscious and unconscious internal retrievals as well as contextual influences relating to current planning affect the decision‐making process.

The purpose of this paper is to change the traditional supplier selection methods by shifting the emphasis from using a single model to using multiple models in the unstructured decision‐making context and to provide a tool for decision makers to make informed decisions of supplier selection in the multiple perspectives.

There are various supplier selection models available in the literature. However, using the result of a single model as a basis for making the final decision could lead to a biased decision given the fact that any model has its limitations. The qualities of the decision‐making process and the decision itself increase by applying a multiple perspectives approach rather than a single model. The multiple perspectives decision‐making allows collaboration and knowledge sharing among the participants which leads to a less‐biased decision. This study examines commonly applied supplier selection models, formulates general perspectives of these models, and proposes a framework of multiple perspectives decision making for supplier selection. It further provides a structure of supplier selection system based on the proposed approach. Through a prototype of web portal, the study demonstrates the usefulness of the proposed multiple perspective system approach in the decision context of collaboration and knowledge sharing.

The general finding from this study is that the multiple perspectives approach to supplier selection enables the decision makers to actively participate and fully understand the decision‐making process through knowledge sharing which in turn ensures high quality of the final decisions.

Supplier selection decision makers can make more informed decisions through collaboration among all decision‐making participants in the multiple perspectives. It informs supply chain managers of the potentially positive effect of knowledge sharing on the decision‐making process in supplier selection.

Multiple perspectives decision making provides a novel approach that emphasizes on the knowledge sharing and collaboration between the experts, who are familiar with the supplier relations, and the decision makers who are responsible for the final decisions.

This paper aims to demonstrate how to make emergency decision when decision makers face a complex and turbulent environment that needs quite different decision-making processes from conventional ones. Traditional decision techniques cannot meet the demands of today’s social stability and security.

The main work is to develop an instance-driven classifier for the emergency categories based upon three fuzzy measures: features for an instance, solution for the instance and effect evaluation of the outcome. First, the information collected from the past emergency events is encodes into a prototype model. Second, a three-dimensional space that describes the locations and mutual distance relationships of the emergency events in different emergency prototypes is formulated. Third, for any new emergency event to be classified, the nearest emergency prototype is identified in the three-dimensional space and is classified into that category.

An instance-driven classifier based on prototype theory helps decision makers to describe emergency concept more clearly. The maximizing deviation model is constructed to determine the optimal relative weights of features according to the characteristics of the new instance, such that every customized feature space maximizes the influence of features shared by members of the category. Comparisons and discusses of the proposed method with other existing methods are given.

To reduce the affection to economic development, more and more countries have recognized the importance of emergency response solutions as an indispensable activity. In a new emergency instance, it is very challengeable for a decision maker to form a rational and feasible humanitarian aids scheme under the time pressure. After selecting a most suitable prototype, decision makers can learn most relevant experience and lessons in the emergency profile database and generate plan for the new instance. The proposed approach is to effectively make full use of inhomogeneous information in different types of resources and optimize resource allocation.

The combination of instances can reflect different aspects of a prototype. This feature solves the problem of insufficient learning data, which is a significant characteristic of emergency decision-making. It can be seen as a customized classification mechanism, while the previous classifiers always assume key features of a category.

Auditors’ skills and knowledge of data analytics and big data can influence their judgment at the audit planning stage. At this stage, the auditor will determine the level of audit risk and estimate how long the audit will take. This study aims to test whether big data and data analytics affect auditors’ judgment by adopting the cognitive fit theory.

This was an experimental study involving 109 accounting students as participants. The 2 × 2 factorial design between subjects in a laboratory setting was applied to test the hypothesis.

First, this study supports the proposed hypothesis that participants who are provided with visual analytics information will rate audit risk lower than text analytics. Second, participants who receive information on unstructured data types will assess audit risk (audit hours) higher (longer) than those receiving structured data types. In addition, those who receive information from visual analytics results have a higher level of reliance than those receiving text analytics.

This research has implications for external and internal auditors to improve their skills and knowledge of data analytics and big data to make better judgments, especially when the auditor is planning the audit.

Previous studies have examined the effect of data analytics (predictive vs anomaly) and big data (financial vs non-financial) on auditor judgment, whereas this study examined data analytics (visual vs text analytics) and big data (structured and unstructured), which were not tested in previous studies.

Possible limitations on the successful formal modeling of human expertise can only be identified if the evolving thought processes involved in acquiring expertise are understood. This paper presents a 5‐stage description of the human skill‐acquisition process, applies it to the skill of business management, and draws conclusions about potential uses and abuses of formal modeling.

Subtractive manufacturing process is the controlled removal of unwanted material from the parent workpiece for having the desired shape and size of the product. Several types of available machine tools are utilized to carry out this manufacturing operation. Selection of the most appropriate machine tool is thus one of the most crucial factors in deciding the success of a manufacturing organization. Ill-suited machine tool may often lead to reduced productivity, flexibility, precision and poor responsiveness. Choosing the best suited machine tool for a specific machining operation becomes more complex, as the process engineers have to consider a diverse range of available alternatives based on a set of conflicting criteria. The paper aims to discuss these issues.

Case-based reasoning (CBR), an amalgamated domain of artificial intelligence and human cognitive process, has already been proven to be an effective tool for ill-defined and unstructured problems. It imitates human reasoning process, using specific knowledge accumulated from the previously encountered situations to solve new problems. This paper elucidates development and application of a CBR system for machine tool selection while fulfilling varying user defined requirements. Here, based on some specified process characteristic values, past similar cases are retrieved and reused to solve a current machine tool selection problem.

A software prototype is also developed in Visual BASIC 6.0 and three real time examples are illustrated to validate the application potentiality of CBR system for the said purpose.

The developed CBR system for machine tool selection retrieves a set of similar cases and selects the best matched case nearest to the given query set. It can successfully provide a reasonable solution to a given machine tool selection problem where there is a paucity of expert knowledge. It can also guide the process engineers in setting various parametric combinations for achieving maximum machining performance from the selected machine tool, although fine-tuning of those settings may often be required.

The purpose of this paper is to explore the characteristics of situations where managers analogise, and when they change to a different decision model; examine how the analogies are evoked, what characteristics they have and how they are used, and add to the understanding through taking a qualitative approach.

This is an illustrative case study of a new market entry attempt by a medium‐sized manufacturing firm based on interviews and analytical dialogues with management team members.

The paper finds that decision makers analogise when cause/effect‐relationships are unclear, and change decision models when the analogy has helped to formulate a theory of the nature of the problem or a recipe for handling the situation. They evoke analogies by automatic recognition, using internal and external sources, for transfer within and between domains. The use of analogy occurs in problem setting, problem solving, action and sensemaking modes.

Misunderstandings can occur in dialogue between researchers and decision makers. Future interpretive research should consider participant observation and conceptual modelling. A computational study might incorporate situational differences, roles, and the issues identified in this study.

Awareness of the prevalence of analogy in decision making can help practitioners critically evaluate the analogies used and consider multiple perspectives on problematic situations.

The paper adds to the literature by taking a qualitative approach to analogising. The findings offer some support to prior research using laboratory and analytical approaches, while suggesting reconsiderations and offering new insights.

This paper examines the role of managerial curiosity as a critical factor in determining the future impact of disruptive information technologies in a global organization. Specifically, this paper presents curiosity as a managerial characteristic that plays an important role in identifying disruptive information technologies and facilitating their early adoption. Further, it uses resource‐based theory as a theoretical lens to illustrate how managerial curiosity can be a source of sustained competitive advantage. Finally, it examines the individual decision styles that are best suited in assessing disruptive information technologies.