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.
Growing pressure on increasing decision-making speed in all spheres of human life is one of the basic phenomena of today. Immediately after the first wave of the coronavirus pandemic, we can consider the ability of making good decisions quickly as one of the most important aspects of our being. The main objective of this article is to find out the utilization rate of several basic decision-making approaches in selected companies with an emphasis on newly used methods such as data analysis and business intelligence tools. The first part of the article presents a short introduction of the decision-making process and an overview of hitherto known and used tools facilitating the whole procedure. The submitted study of available literature leads to the presentation of own classification of the most widely used decisionmaking methods. Based on a questionnaire survey, in the second section, the pilot research examines the involvement of five different groups of methods in business decision-making, such as intuition and previous experiences, consultation with colleagues, data analysis (historical), MCDM methods and consultation with experts. Afterwards, the most common obstacles that employees must face in introducing new tools have been identified. In general, the results show that time and the associated pressure on decision-making speed play a crucial role in the decision-making process.
Purpose
The purpose of this paper is to assess the level of the organizational goal accomplishment by assessing the reliance relationship between organizational data and organizational goals.
Design/methodology/approach
The evaluation of the organizational goals is based on design and operational level, which can serve in ranking of the organizational goals achievement and hence assist the decision-making process in achieving the organizational goals. To achieve this aim, the authors propose an ontology to develop the relationship between organizational data and organizational goals.
Findings
Data goals dependency shows the dependency relationship between organizational data and organizational goals. At the same time, data goals dependency assists the process of identifying data attributes, where the authors suggest that these data attributes are relevant in relation to the organizational goals.
Originality/value
The contribution of this paper will serve as the first step to evaluate the relevance of organizational data to assist decision-making in relation to the organizational goals.
PurposeIn recent years, rapid changes in the economic situation and high levels of competition have increased the need for innovation in order to gain success. In such circumstances, organizational strategists are considered as critical in determining the success or failure of organizations. Using innovation in various aspects of organizational operations is the most important factor to achieve sustainable competitive advantages in industry. As a result, analyzing the effective factors involved in promoting the efficiency of innovative activities in the organization and ways of achieving it are of utmost importance. Thus, this paper examines the relationship between communication and innovation performance with respect to the intermediary role of strategic decision-making process speed.Design/methodology/approachThe present study has used quantitative methodology and questionnaire to collect data from 450 managers and members who are involved in the decision-making process in 150 companies operating in the food-industry sector. Data analysis was done by using structural equation modeling and AMOS software.FindingsThe results of the data analysis suggest that communication and strategic decision-making speed possess a significant positive impact on innovation performance. Also, strategic decision-making speed has sufficiently played the intermediary role between communication and innovation performance.Originality/valueThis survey specifies the effects of communication on the success of making fast strategic decision and innovation performance which aid Iranian food companies to tackle one of the managerial challenges: postponing strategic decisions due to lack of efficient communication to get information. In addition, to the best of the authors' knowledge, this essay is a first in Iran.
The analysis of large volumes of data is an important activity in manufacturing companies, since they allow improving the decision-making process. The data analysis has generated that the services and products are personalized, and how the consumption of the products has evolved, obtaining results that add value to the companies in real time.
In this case study, developed in a large manufacturing company of electronic components as robots and AC motors; a strategy has been proposed to analyze large volumes of data and be able to analyze them to support the decision-making process; among the proposed activities of the strategy are: Analysis of the technological architecture, selection of the business processes to be analyzed, installation and configuration of Hadoop software, ETL activities, and data analysis and visualization of the results. With the proposed strategy, the data of nine production factors of the motor PCI boards were analyzed, which had a greater incidence in the rejection of the components; a solution was made based on the analysis, which has allowed a decrease of 28.2% in the percentage of rejection.
In the contemporary world, Data analysis is a challenge in the era of varied inters disciplines through there is a specialization in the respective disciplines. In other words, effective data analytics helps in analyzing the data of any business system. Flight delays hurt airlines, airports, and passengers. Their prediction is crucial during the decision-making process for all players of commercial aviation. The goal of our project is to get monthly wise statistics of airline data and taking particular airport as target we are further analyzing the data to get the hourly statistics. And also we are finding out the most popular source-destination pairs and calculating the average delays at every airport. The data for this project comes from the stat-computing.org website. In particular, in the year 2008 data 70,09,728 titles recorded there which includes information on the Origin, Destination, Month, Year, DayofWeek, DayofMonth, DepDelay, ArvDelay, DepTime, ArvTime and a few other less interesting variables. Conveniently, you can export the data directly as a csv file.
Competitive advantage is at the heart of a firm’s performance in today’s challenging and rapidly changing environment. One of the central bases for achieving competitive advantage is the organizational capability to create new knowledge and transfer it across various levels of the organization. Traditional methods of data analysis, based mainly on human dealing directly with the data, simply do not scale to handle with large data sets. This explosive growth in data and databases has generated an urgent need for new techniques and tools that can intelligently and automatically transform the processed data into useful information and knowledge. Consequently, data mining has become a research area with increasing importance. Organizations of all sizes have started to develop and deploy data mining technologies to leverage data resources to enhance their decision making capabilities. Business information received from data analysis and data mining is a critical success factor for companies wishing to maximize competitive advantage. In this study, the importance of gaining knowledge for organizations in today’s competitive environment are discussed and data mining method in decision making process is analyzed as an innovative technique for organizations.
Coding is a ubiquitous part of the qualitative research process, but it is often under-considered in research methods training and literature. This article explores a number of questions about the coding process which are often raised by beginning researchers, in the light of the recommendations of methods textbooks and the factors which contribute to an answer to these questions. I argue for a conceptualisation of coding as a decision-making process, in which decisions about aspects of coding such as density, frequency, size of data pieces to be coded, are all made by individual researchers in line with their methodological background, their research design and research questions, and the practicalities of their study. This has implications for the way that coding is carried out by researchers at all stages of their careers, as it requires that coding decisions should be made in the context of an individual study, not once and for all.
Integrating collaborative data in data-driven Business Intelligence (BI) system brings an opportunity to foster the decision-making process towards improving tourism competitiveness. This article presents BITOUR, a BI platform that integrates four collaborative data sources (Twitter, Openstreetmap, Tripadvisor and Airbnb). BITOUR follows a classical BI architecture and provides functionalities for data transformation, data processing, data analysis and data visualization. At the core of the data processing, BITOUR offers mechanisms to identify tourists in Twitter, assign tweets to attractions and accommodation sites from Tripadvisor and Airbnb, analyze sentiments in opinions issued by tourists, and all this using geolocation objects in Openstreetmap. With all these ingredients, BITOUR enables data analysis and visualization to answer questions like the most frequented places by tourists, the average stay length or the view of visitors of some particular destination.
Executing job tasks faster is essential for employees working in the operation section of manufacturing organizations nowadays in order to achieve target profit and gain customer satisfaction. Thus, having a tool to assist them in performing their work faster is necessary. Existing IT system does exist to supply the data they need. However, the entire process is invisible because there is no real-time information available. This causes delay in decisions that they need to make for the operation of the section. To address these issues, this paper presents the Operational Dashboard (OD) for the workers in the operation section of the manufacturing firm. The workers’ needs were first identified to ensure that a right dashboard is being constructed, which is the OD. The OD system was then implemented in the manufacturing firm following the users’ requirements. The implementation of the OD had shown its effectiveness in shortening the time of the data analysis by the employees in the section. This eventually led to improvement in the decision making process in such a way that the process was done faster as compared to the previous practice.
Evaluation of application of new decision-making methods in selected companies: the use of business intelligence in practice