Big data technologies have a strong impact on different industries, starting from the last decade, which continues nowadays, with the tendency to become omnipresent. The financial sector, as most of ...the other sectors, concentrated their operating activities mostly on structured data investigation. However, with the support of big data technologies, information stored in diverse sources of semi-structured and unstructured data could be harvested. Recent research and practice indicate that such information can be interesting for the decision-making process. Questions about how and to what extent research on data mining in the financial sector has developed and which tools are used for these purposes remains largely unexplored. This study aims to answer three research questions: (i) What is the intellectual core of the field? (ii) Which techniques are used in the financial sector for textual mining, especially in the era of the Internet, big data, and social media? (iii) Which data sources are the most often used for text mining in the financial sector, and for which purposes? In order to answer these questions, a qualitative analysis of literature is carried out using a systematic literature review, citation and co-citation analysis.
This paper increases understanding of the concept of smart factories system and its implementation in the automotive industry in terms of addressing necessary changes in human aspect of system, ...namely, employees' competencies, as well as to new job profiles. A qualitative research approach was used by utilizing a case study of the automotive industry in Slovenia. For the purpose of data collection, we used a semistructured interview on a sample of experts from governmental, educational, and private automotive sectors. In order to analyse gathered data, we used the method of content analysis. Research results indicate that significant changes are essential in the transformation from traditional to a smart factory system, especially regarding organizational mindset, the architecture of system and its processes, and the inclusion of Industry 4.0 technologies at all levels. Also, results indicate changes are essential regarding future job profiles and competencies. Although there are different perceptions about future jobs and competencies needed for Industry 4.0‐induced changes, depending on the sector in which the expert is employed, there is an overall agreement that job profiles related to programming, mechatronics, robotics, data analysis, Internet of Things, design, and maintenance of smart systems, process analysis, and bionics are the new job profiles needed in smart factory systems. In addition to the high emphasis on technical skill and knowledge, soft competencies are considered important, as well as competencies of continues learning, flexibility, creativity, problem solving, critical, and analytical thinking. Our findings possess important implications for conceptualizing job profiles and competencies in smart factories systems and practical implications for managers concerned with human resource management activities. We provide an outlook to the future job profiles that are underresearched, especially in the field of smart manufacturing. As well, our paper provides insight into the importance of different competencies recognized as crucial for complete functioning and optimal performance of smart factories system. This study provides a starting point for further research regarding human resource management issues in smart factories in the automotive sector, especially related to future job profiles and employee competencies, and their role in system performance.
The existing literature fails to identify to which extent the utilization of social media could be relevant for increasing the effectiveness of knowledge management, in respect to overall business ...operations. In order to shed some light on this area we define three goals. Firstly, we investigate to what extent the different activities of clients on social media (SM), are important to the processes of knowledge management (KM) in companies. Secondly, we examine to what extent KM functions can be relevant in attaining the quality of IT services. Thirdly, we analyze to what extent KM mediates between SM and the quality of IT services, that is, which client activities on SM should be formalised in the form of KM processes so as to influence the quality of IT services. In order to asses these goals, the decision tree method was used at the sample of B2B companies, more specifically at the sample of those companies offering Knowledge Intensive Business Services (KIBS). The study has shown that: (i) SM client activities have the largest importance for building efficient KM; (ii) KM functions are relevante to the overall quality of IT services, and (iii) those SM options and activities have been identified, whose importance for the assessment of the quality of IT services is indirectly transmitted through KM. This paper offers new empirical evidence which can lead to a better understanding of the role of KM in KIBS. Thanks to the obtained findings, managers will be able to define the goals of their companies in relation to the utilization of SM, more specifically: their presentation on SM, monitoring the outcomes of the SM, usage improving their KM practices and, thus, define strategies to increase the quality of the IT services they offer.
Pejic-Bach talks about the utilization of electronic commerce in the time of the Covid-19 crisis. In the time of the Covid-19 quarantine, electronic commerce remains the sole source of various types ...of communication addressing both business and personal needs. Quarantine forces most of us to communicate in person solely to the members of our households. Video services has become the primary way for communication with people outside of our households. In brief, without video conferencing solutions, face-to-face communication would not be possible with people outside of our households. Therefore, in the short term, video conferencing services are perceived as saviors for various processes, such as collaborating with colleagues in the office and delivering e-learning solutions.
Industrial informatics is a rapidly developing scientific field that deals with the knowledge-based automation of industrial design and manufacturing processes. In the last decade, industrial ...informatics has been strongly influenced by the rapid rise of data-based technologies such as Data Science, Big Data, and artificial intelligence. The goal of this paper is to provide a literature review of academic research analyzing the extensive spectrum of industrial informatics. Articles indexed in Scopus with the term “Industrial Informatics” in the title, abstract, or keywords were extracted since the term emerged in the 1990s, over a period of 29 years. The main journals, conferences, authors and countries were studied using bibliometric analysis. Text mining using VosViewer was used to extract the thematic groups of research related to industrial informatics, which are as follows: (i) Internet of Things, (ii) machine learning, (iii) engineering education, (iv) cyber–physical systems, and (v) embedded systems. We also found that China, Germany, and Brazil dominate research in industrial computing. The results showed that research in industrial informatics is related to the emergence of new methods and tools, and is nowadays shifting towards the application of intelligent methods such as machine learning and Big Data.
PurposeThe purpose of this study is to disclose ontology of DSI as a novel concept in servitization community, explore the research context and themes (i.e. technological and industrial sectors) ...where DSI emerges, unveil methodological complexities of the research on digital servitization and DSI and provide guidelines for future research avenues regarding DSI.Design/methodology/approachBearing in mind the relative novelty of DSI as a concept in servitization literature, the authors adopted a systematic literature review approach to identify 111 peer-reviewed articles published in English language and available in business and management disciplines via scholar databases (Scopus). The analysis of literature discloses descriptive and thematic insights regarding digital servitization and DSI.FindingsThe study provides valuable insights from the descriptive and thematic analyses where classification of articles per publication year, citations, methodology/type of the paper, geographical location of data collection, as well as industrial sector and technological contexts are discussed. Moreover, the unique value of this study is observed through its specific focus on the characteristics of DSI-related literature.Originality/valueThe study is among the first of its kind to provide extensive descriptive and thematic insights on the available literature dealing with digital servitization and DSI, mapping out prior research across a wide spectrum of publication outlets and illustrating the chronological evolution of research on digital servitization and DSI.
Customer response models have gained popularity due to their ability to significantly improve the likelihood of targeting the customers most likely to buy a product or a service. These models are ...built using databases of previous customers’ buying decisions. However, a smaller number of customers in these databases often bought the product or service than those who did not do so, resulting in unbalanced datasets. This problem is especially significant for online marketing campaigns when the class imbalance emerges due to many website sessions. Unbalanced datasets pose a specific challenge in data-mining modelling due to the inability of most of the algorithms to capture the characteristics of the classes that are unrepresented in the dataset. This paper proposes an approach based on a combination of random undersampling and Support Vector Machine (SVM) classification applied to the unbalanced dataset to create a Balanced SVM (B-SVM) data pre-processor resulting in a dataset that is analysed with several classifiers. The experiments indicate that using the B-SVM strategy combined with classification methods increases the base models’ predictive performance, indicating that the B-SVM approach efficiently pre-processes the data, correcting noise and class imbalance. Hence, companies may use the B-SVM approach to more efficiently select customers more likely to respond to a campaign.
In the complex organisational landscape, managing workforce diversity effectively has become crucial due to rapid technological advancements and shifting societal values. This study explores ...strategic workforce management through the novel methodological framework consisting of the evolutionary game theory concept integrated with replicator dynamics and traditional game theory, addressing a notable gap in the literature and suggesting an evolutionarily stable workforce structure. Key findings indicate that targeted rewards for the most Enthusiastic employee type can reduce overall costs and enhance workforce efficiency, although managing a diverse team remains complex. The study reveals that while short-term incentives boost immediate productivity, long-term rewards facilitate favourable behavioural changes, which are crucial for sustaining organisational performance. Additionally, the role of artificial intelligence (AI) is highlighted, emphasising its potential to integrate with these theoretical models, thereby enhancing decision-making processes. The study underscores the importance of strategic leadership in navigating these dynamics, suggesting that leaders must tailor their approaches to balance short-term incentives and long-term rewards to maintain an optimal workforce structure.
Data mining is the process of knowledge extraction from the data with the algorithms that identify hidden relationships and patterns, which are usually not noticeable at first glance. Data mining has ...become omnipresent in various domains in the recent decade, but its usage in small and medium enterprises (SMEs) is still under-represented. This paper investigates the determinants of data mining usage in SMEs using the TOE Framework (Technology-Organisation-Environment). A model has been proposed to test the impact of individual components of the TOE framework on the intensity of data mining and, in turn, test the effect of data mining implementation on business performance. The survey has been carried out on a sample of small and medium-sized Italian enterprises. Two methodologies have been used to analyze structural equation modeling (SEM) and artificial neural networks (ANN). Using a hybrid SEM-ANN methodology, hypotheses were tested. It was shown that the TOE framework could explain the intensity of knowledge discovery use in databases, utilizing the importance-performance map analysis to reveal the significance and performance of each determinant.
Purpose
Organizations introduce business intelligence (BI) to increase their performance, but often, this initiative is not aligned with the business process management (BPM) initiative, which also ...aims to improve organizational performance. Although some findings from the literature indicate that BI implementation has a positive impact on organizational performance, the impact seems to be indirect. Therefore, the purpose of this study is to enhance the understanding of how BI maturity is translated into organizational performance. Alignment of BI and BPM initiatives seems one possible way for creating business value with BI, particularly because BI enables process performance measurement and management, which allows the BI initiative to become more business focused.
Design/methodology/approach
A questionnaire was prepared and used to collect data in Croatian and Slovenian organizations with more than 50 employees. A BI–BPM alignment measurement instrument was developed for the purpose of this study using the recommended process of scale development and validation. A total of 185 responses were analyzed by the structural equation modeling technique.
Findings
Our results provide evidence that the effect of BI on organizational performance is fully mediated by alignment of BI and BPM initiatives, and therefore, BI business value can be generated through the use of common terminology and methodologies, as well as a strong communication between BI and BPM experts, managers and teams in order to coordinate the two initiatives.
Originality/value
This study has responded to the call for better understanding of how the impact of BI on organization performance is realized. It confirmed that BI and BPM initiatives should be aligned in order to give BI a business value.
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