scholarly journals DATAFICATION AS A NECESSARY STEP IN THE PROCESSING OF BIG DATA IN DECISION-MAKING TASKS OF BUSINESS

2021 ◽  
Vol 2 ◽  
pp. 75-80
Author(s):  
Martin Misut ◽  
Pavol Jurik
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Quantitative Methods ◽  
Digital Data ◽  
Classification Model ◽  
Decision Making Process ◽  
Digital Form ◽  
Suitable Alternative ◽  
The Given ◽  
The Relationship

The digital transformation of business in the light of opportunities and focusing on the challenges posed by the introduction of Big Data in enterprises allows for a more accurate reflection of the internal and external environmental stimuli. Intuition ceases to be present in the decision-making process, and decision-making becomes strictly data-based. Thus, the precondition for data-based decision-making is relevant data in digital form, resulting from data processing. Datafication is the process by which subjects, objects and procedures are transformed into digital data. Only after data collection can other natural steps occur to acquire knowledge to improve the company's results if we move in the industry's functioning context. The task of finding a set of attributes (selecting attributes from a set of available attributes) so that a suitable alternative can be determined in its decision-making is analogous to the task of classification. Decision trees are suitable for solving such a task. We verified the proposed method in the case of logistics tasks. The analysis subject was tasks from logistics and 80 well-described quantitative methods used in logistics to solve them. The result of the analysis is a matrix (table), in which the rows contain the values of individual attributes defining a specific logistic task. The columns contain the values of the given attribute for different tasks. We used Incremental Wrapper Subset Selection IWSS package Weka 3.8.4 to select attributes. The resulting classification model is suitable for use in DSS. The analysis of logistics tasks and the subsequent design of a classification model made it possible to reveal the contours of the relationship between the characteristics of a logistics problem explicitly expressed through a set of attributes and the classes of methods used to solve them.

The Effects of Conflict on Team Decision Making

2011 ◽  
Vol 39 (2) ◽  
pp. 189-198 ◽  
Author(s):  
Long Cheng ◽  
Zhong-Ming Wang ◽  
Wei Zhang
Keyword(s):  
Decision Making ◽  
Task Conflict ◽  
Relationship Conflict ◽  
Decision Making Process ◽  
Team Decision Making ◽  
Team Decision ◽  
Relationship Of ◽  
The Relationship

The aim in this study was to examine the relationship between task and relationship conflict and their effect on team decision-making. A sample of 120 participants, divided into 40 teams, was recruited. We found that the relationship of task and relationship conflict was moderated by the decision-making process and teams performed better when making good use of task conflict, while relationship conflict was reduced.

Big Data Myth

2019 ◽  
pp. 223-237
Author(s):  
Agata Mardosz-Grabowska
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analysis ◽  
Qualitative Data ◽  
New Technologies ◽  
Marketing Management ◽  
Individual Experience ◽  
Decision Making Process

Organizations are expected to act rationally; however, mythical thinking is often present among their members. It refers also to myths related to technology. New inventions and technologies are often mythologized in organizations. People do not understand how new technologies work and usually overestimate their possibilities. Also, myths are useful in dealing with ambivalent feelings, such as fears and hopes. The text focuses on the so-called “big data myth” and its impact on the decision-making process in modern marketing management. Mythical thinking related to big data in organizations has been observed both by scholars and practitioners. The aim of the chapter is to discuss the foundation of the myth, its components, and its impact on the decision-making process. Among others, a presence of a “big data myth” may be manifested by over-reliance on data, neglecting biases in the process of data analysis, and undermining the role of other factors, including intuition and individual experience of marketing professionals or qualitative data.

The Full Knowledge of Big Data in the Integration of Inter-Organizational Information

Web Services ◽  
2019 ◽  
pp. 803-821
Author(s):  
Thiago Poleto ◽  
Victor Diogho Heuer de Carvalho ◽  
Ana Paula Cabral Seixas Costa
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Decision Support ◽  
Decision Making Process ◽  
Data Repositories ◽  
Modeling And Analysis ◽  
Incremental Change ◽  
Working Together ◽  
Full Knowledge ◽  
Using Data

Big Data is a radical shift or an incremental change for the existing digital infrastructures, that include the toolset used to aid the decision making process such as information systems, data repositories, formal modeling, and analysis of decisions. This work aims to provide a theoretical approach about the elements necessary to apply the big data concept in the decision making process. It identifies key components of the big data to define an integrated model of decision making using data mining, business intelligence, decision support systems, and organizational learning all working together to provide decision support with a reliable visualization of the decision-related opportunities. The concepts of data integration and semantic also was explored in order to demonstrate that, once mined, data must be integrated, ensuring conceptual connections and bequeathing meaning to use them appropriately for problem solving in decision.

Big Data in Business

2021 ◽  
pp. 235-249
Author(s):  
Farid Huseynov
Keyword(s):  
Decision Making ◽  
Big Data ◽  
New Products ◽  
Critical Role ◽  
Digital Data ◽  
Business Decisions ◽  
Business Operations ◽  
Operational Costs ◽  
Big Data Technologies ◽  
Different Sources

The term “big data” refers to the very large and diverse sets of structured, semi-structured, and unstructured digital data from different sources that accumulate and grow very rapidly on a continuous basis. Big data enables enhanced decision-making in various types of businesses. Through these technologies, businesses are able to cut operational costs, digitally transform business operations to be more efficient and effective, and make more informed business decisions. Big data technologies enable businesses to better understand their markets by uncovering hidden patterns behind consumer behaviors and introduce new products and services accordingly. This chapter shows the critical role that big data plays in businesses. Initially, in this chapter, big data and its underlying technologies are explained. Later, this chapter discusses how big data digitally transforms critical business operations for enhanced decision-making and superior customer experience. Finally, this chapter ends with the possible challenges of big data for businesses and possible solutions to these challenges.

Consumer decision-making in a retail store: the role of mental imagery and gender difference

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jung-Hwan Kim ◽  
Minjeong Kim ◽  
Jungmin Yoo ◽  
Minjung Park
Keyword(s):  
Decision Making ◽  
Mental Imagery ◽  
Decision Making Process ◽  
Consumer Decision Making ◽  
Consumer Decision ◽  
Content Type ◽  
Perceived Ownership ◽  
Decision Satisfaction ◽  
The Relationship

PurposeThe purpose of the study is to investigate how mental imagery evoked from sensory in-store experience influences consumer anticipatory emotion, perceived ownership and decision satisfaction which eventually impact positive consumer responses such as behavioural intent. In this study, gender difference is proposed as a moderator to completely understand the role of mental imagery in the in-store decision-making process.Design/methodology/approachUsing a market research agency in South Korea, an online survey was employed to collect data. A total of 455 useable respondents (men = 224 and women = 231) largely living in the two most populous provinces in South Korea (i.e. Seoul and Gyeonggi provinces) completed the survey. A number of path analyses were conducted to test hypotheses.FindingsThe results of the study showed that mental imagery evoked from sensory product experience played a critical part in facilitating the consumer decision-making process by influencing anticipatory emotion and perceived ownership. The relationship among anticipatory emotion, perceived ownership, decision satisfaction and behavioural intent was significant except for the relationship between perceived ownership and behavioural intent. This study further indicated that the way mental imagery influences the in-store decision-making process differs between men and women.Originality/valueThe effect of mental imagery in a physical retail context is largely ignored. This study addressed the crucial role of mental imagery in a physical apparel retail setting and examined its impact on consumer decision-making processes. By exploring how to enhance consumers' in-store sensory shopping experiences through mental imagery to influence their positive shopping outcomes, this study offers vital insights into how retailers operating physical stores can successfully utilize their stores.

The role of heuristic-driven biases in entrepreneurial strategic decision-making: evidence from an emerging economy

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Maqsood Ahmad ◽  
Syed Zulfiqar Ali Shah ◽  
Yasar Abbass
Keyword(s):  
Decision Making ◽  
Emerging Economy ◽  
Strategic Decision ◽  
Strategic Decision Making ◽  
Decision Making Process ◽  
Strategic Decisions ◽  
Content Type ◽  
The Relationship

PurposeThis article aims to clarify the mechanism by which heuristic-driven biases influence the entrepreneurial strategic decision-making in an emerging economy.Design/methodology/approachEntrepreneurs' heuristic-driven biases have been measured using a questionnaire, comprising numerous items, including indicators of entrepreneurial strategic decision-making. To examine the relationship between heuristic-driven biases and entrepreneurial strategic decision-making process, a 5-point Likert scale questionnaire has been used to collect data from the sample of 169 entrepreneurs who operate in small- and medium-sized enterprises (SMEs). The collected data were analyzed using SPSS and Amos graphics software. Hypotheses were tested using structural equation modeling (SEM) technique.FindingsThe article provides empirical insights into the relationship between heuristic-driven biases and entrepreneurial strategic decision-making. The results suggest that heuristic-driven biases (anchoring and adjustment, representativeness, availability and overconfidence) have a markedly negative influence on the strategic decisions made by entrepreneurs in emerging markets. It means that heuristic-driven biases can impair the quality of the entrepreneurial strategic decision-making process.Practical implicationsThe article encourages entrepreneurs to avoid relying on cognitive heuristics or their feelings when making strategic decisions. It provides awareness and understanding of heuristic-driven biases in entrepreneurial strategic decisions, which could be very useful for business actors such as entrepreneurs, managers and entire organizations. Understanding regarding the role of heuristic-driven biases in entrepreneurial strategic decisions may help entrepreneurs to improve the quality of their decision-making. They can improve the quality of their decision-making by recognizing their behavioral biases and errors of judgment, to which we are all prone, resulting in a more appropriate selection of entrepreneurial opportunities.Originality/valueThe current study is the first to focus on links between heuristic-driven bias and the entrepreneurial strategic decision-making in Pakistan—an emerging economy. This article enhanced the understanding of the role that heuristic-driven bias plays in the entrepreneurial strategic decisions and more importantly, it went some way toward enhancing understanding of behavioral aspects and their influence on entrepreneurial strategic decision-making in an emerging market. It also adds to the literature in the area of entrepreneurial management specifically the role of heuristics in entrepreneurial strategic decision-making; this field is in its initial stage, even in developed countries, while, in developing countries, little work has been done.

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