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Journal articles on the topic 'Système Business Intelligence'

Author: Grafiati

Published: 5 June 2025

Last updated: 8 July 2025

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1

Mau, Jens. "Brachliegendes Potenzial." kma - Klinik Management aktuell 13, no. 11 (2008): 94. http://dx.doi.org/10.1055/s-0036-1574954.

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Dank Business-Intelligence-Systemen bekommen Entscheider im Krankenhaus gezielte Analysen und Statistiken. Doch die Systeme sind teuer und Extras kosten zusätzlich. Eine Lösung auf Microsoft-Basis ziehen viele nicht in Erwägung – aber es gibt sie. Im Krankenhaus existiert ein schwer zu beherrschender Datenzoo.

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2

Mollekopf, Heiner. "Nutzen von Business Intelligence-Systemen." ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb 104, no. 5 (2009): 411–14. http://dx.doi.org/10.3139/104.110077.

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3

Chamoni, Peter, and Peter Gluchowski. "Integrationstrends bei Business-Intelligence-Systemen." Wirtschaftsinformatik 46, no. 2 (2004): 119–28. http://dx.doi.org/10.1007/bf03250931.

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4

Tunowski, Remigiusz. "Organization Effectiveness and Business Intelligence Systems. Literature Review." Management and Business Administration. Central Europe 23, no. 4 (2015): 55–73. http://dx.doi.org/10.7206/mba.ce.2084-3356.157.

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5

Schwalm, Stephan, and Carsten Bange. "Einsatzpotenziale von XML in Business-Intelligence-Systemen." Wirtschaftsinformatik 46, no. 1 (2004): 5–14. http://dx.doi.org/10.1007/bf03250991.

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6

Humm, Bernhard, and Frank Wietek. "Architektur von Data Warehouses und Business Intelligence Systemen." Informatik-Spektrum 28, no. 1 (2005): 3–14. http://dx.doi.org/10.1007/s00287-004-0450-5.

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7

Anurag Reddy Basani, Maria, and Anudeep Kandi. "Data-Driven Decision Making: Advanced Database Systems for Business Intelligence." International Journal of Science and Research (IJSR) 13, no. 11 (2024): 844–50. http://dx.doi.org/10.21275/sr241114034006.

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8

Grünwald, Markus, and Dirk Taubner. "Business Intelligence." Informatik-Spektrum 32, no. 5 (2009): 398–403. http://dx.doi.org/10.1007/s00287-009-0374-1.

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9

Herring, Jan P. "Business Intelligence." Journal of Business Strategy 14, no. 3 (1993): 10–12. http://dx.doi.org/10.1108/eb039552.

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10

Schön, Dietmar, and Bernd Springenberg. "Planung und Steuerung im Controlling-Cockpit der TPPG, ein mittelständisches Handelsunternehmen." Controlling 31, no. 4 (2019): 29–37. http://dx.doi.org/10.15358/0935-0381-2019-4-29.

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Mittelständische Unternehmen benötigen zur erfolgreichen Unternehmenssteuerung leistungsfähige IT-gestützte Controlling-Systeme. Excel und andere suboptimale Systeme müssen konsequent durch Business-Intelligence-Technologie abgelöst werden. Hierzu zählt ein solides Datawarehouse und ein leistungsfähiges Frontend. Dieser Beitrag zeigt am Beispiel der Ter Plastic Polymer Group auf, welche Faktoren zu einer erfolgreichen Umsetzung eines BI-gestützten Controlling-Cockpits notwendig sind.

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11

Hawking, Paul, and Carmine Sellitto. "Business Intelligence Strategy." International Journal of Enterprise Information Systems 11, no. 1 (2015): 1–12. http://dx.doi.org/10.4018/ijeis.2015010101.

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Although Business Intelligence is seen as priority by many companies, the level of benefits achieved varies significantly between firms. Researchers have indicated that not having an effective Business Intelligence strategy is a significant issue in regards to trying to realize organizational benefits. This paper adopting a case study method investigates an Australian energy company's Business Intelligence adoption and the development of a Business Intelligence strategy that directly informed the firm's information needs. The important elements of this strategy included using a set of guiding principles to ensure that there was a close alignment of Business Intelligence outcomes with the company's needs. The paper provides insights for researchers and practitioners on the important factors need to be considered to achieve effective Business Intelligence.

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12

Das, Sanjay Kumar. "Modernizing QA Frameworks for Omni-Based Retirement Systems: A Shift from Manual Mainframe Testing to Intelligent Automation." International Interdisciplinary Business Economics Advancement Journal 06, no. 05 (2025): 68–89. https://doi.org/10.55640/business/volume06issue05-04.

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Quality Assurance (QA) in the retirement services industry is undergoing a paradigm shift as firms modernize legacy Omni-based recordkeeping systems. This paper explores the transition from manual mainframe testing to intelligent automation for Omni-based retirement platforms. We discuss the challenges posed by legacy mainframe environments and the strategies to modernize QA using tools like IBM Personal Communications (PCOMM) for terminal automation, Job Control Language (JCL) for batch processing, and OmniScript for custom business logic. Emphasis is placed on maintaining compliance with stringent retirement regulations and ensuring data accuracy and system performance. We present case studies of organizations that have successfully migrated from labor-intensive, error-prone QA processes to robust automated frameworks. Diagrams and figures illustrate the legacy vs. modern architecture and quantify improvements such as reduced testing cycle time and increased coverage. The results demonstrate that intelligent automation in Omni-based systems not only accelerates testing and enhances quality but also ensures greater reliability and compliance in a highly regulated domain. The findings aim to guide retirement services providers in upgrading their QA practices for better efficiency, accuracy, and risk management.

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13

Laqua, Ingo. "Der sinnvolle Einsatz von Business Intelligence-Systemen in der Produktion." ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb 105, no. 3 (2010): 237–40. http://dx.doi.org/10.3139/104.110268.

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14

Pranay, Mungara. "A Review on Business Intelligence for Small and Midsize Businesses (SMBS)." Journal of Scientific and Engineering Research 7, no. 6 (2020): 265–71. https://doi.org/10.5281/zenodo.12787725.

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Every single organization is confronted with an enormous number of obstacles, but this is especially true for small and medium-sized organizations who are seeking to grow with traditional technologies. Therefore, in order for organizations to be successful in overcoming the challenges, they need to implement business intelligence by utilizing the management of information technology systems. This study proposes a conceptual framework that identifies the potential factors that influence the adoption of business intelligence systems in the context of the small and medium-sized company (SME) sector in Libya. The SME sector is a sector that is comprised of small and medium-sized businesses. Two key concepts that served as the basis for this research effort were the technology acceptance model (TAM) and the unified theory of adopting and using technology (UTAUT). Both of these models were developed by the National Institute of Technology (NIST). This research suggested a conceptual framework that would incorporate a number of different aspects, such as the management of change, the sharing of knowledge, the quality of information, the management of business intelligence projects, the perceived usefulness of a business intelligence system (BIS), and the perceived simplicity of adopting a BIS. The findings of earlier research that explored this kind of influence are consistent with this proposal, which is in accordance with those findings. This study did not take into account the impact that environmental factors have on the adoption of a business intelligence system (BIS). This is because individual small and medium-sized businesses (SMBs) have their own distinct features in terms of the sector or industry type in which they operate.

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15

Lim, Ee-Peng, Hsinchun Chen, and Guoqing Chen. "Business Intelligence and Analytics." ACM Transactions on Management Information Systems 3, no. 4 (2013): 1–10. http://dx.doi.org/10.1145/2407740.2407741.

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16

Daradkeh, Mohammad, and Radwan Moh'd Al-Dwairi. "Self-Service Business Intelligence Adoption in Business Enterprises." International Journal of Enterprise Information Systems 13, no. 3 (2017): 65–85. http://dx.doi.org/10.4018/ijeis.2017070105.

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Despite the growing popularity of self-service business intelligence (SSBI) tools, empirical research that investigates their acceptance by business professionals is still scarce. This paper presents and tests an integrated model of the antecedents of users' acceptance of SSBI tools in business enterprises. The proposed model is developed based on the technology acceptance model (TAM) and incorporating information and system quality from DeLone and McLean IS success model. It also includes an important factor from the business intelligence literature called analysis quality. To test the model, data were collected through a questionnaire survey from 331 business users working in a variety of industries in Jordan. Data were analysed using structural equation modeling (SEM) techniques. The results demonstrated that the three quality factors– information quality, system quality and analysis quality – are key antecedents of perceived usefulness and ease of use, which in turn were found to be strong predictors of users' intention to use SSBI tools. The findings of this study provide several implications for research and practice, and thus should help in the design and deployment of more user-accepted SSBI tools.

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17

Bell, David, and Sara Robaty Shirzad. "Social Media Business Intelligence." International Journal of Sociotechnology and Knowledge Development 5, no. 3 (2013): 51–73. http://dx.doi.org/10.4018/ijskd.2013070104.

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Social media tools are increasingly used for relationships management among marketplace actors (e.g. organisations, suppliers and individuals). As markets become ever more global and dynamic, new entrants find themselves struggling to fully understand the marketplace, companies operating with it and changes that occur. The authors discuss Social Media Network (SMN) tools and outline a methodology and procedure that supports the identification of domain specific networks within particular global business-to-business environments. Research is carried out using SMN data about firms in the pharmaceutical industry. The authors use their own methodology to uncover market participants, linkages and prominent issues that may help new firms to position themselves effectively within a new marketplace. SMNs provide a sizable source of information and new approaches are required to fully leverage their considerable value. This paper explores how SMNs can be used as an effective source of business intelligence by utilising two popular SMN platforms.

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18

Rakaeva, A., and A. Rakaeva. "Optimizing Business Processes with AI, BPMN 2.0, and Workflow Management Systems." ECONOMIC Series of the Bulletin of the L.N.Gumilyov ENU, no. 4 (December 31, 2024): 129–42. https://doi.org/10.32523/2789-4320-2024-4-129-142.

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This research discusses the integration of artificial intelligence (AI), workflow management systems (WMS) and business process models and notes (BPMN) to improve the efficiency of modern business processes. Advanced technologies can significantly increase productivity, reduce costs and improve decision-making. The data analysis and automation capabilities of artificial intelligence enable companies to solve problems faster, optimize resource allocation and make better decisions. In combination with WMS, AI optimizes processes, automates workflows and increases operational efficiency. BPMN 2.0 is a clear, standardized method for visualizing and modeling business processes that helps improve communication and consistency between teams. This structured approach enables companies to effectively document, analyze and improve their workflows. Research shows the challenges companies face by using technologies, that also includes data quality issues, resistance to change and the need for a skilled workforce. Analyzing article, the authors consider the potential of quantum computing and developments in artificial intelligence algorithms and emphasize the importance of continuous innovation and adaptation for companies to remain competitive in an ever-evolving technology landscape.

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19

Milolidakis, Giannis, Demosthenes Akoumianakis, and Chris Kimble. "Digital traces for business intelligence." Journal of Enterprise Information Management 27, no. 1 (2014): 66–98. http://dx.doi.org/10.1108/jeim-09-2012-0061.

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Purpose – Data from social media (SM) has grown exponentially and created new opportunities for businesses to supplement their business intelligence (BI). However, there are many different platforms all of which are in a constant state of evolution. The purpose of this paper is to describe a generic methodology for the gathering of data from SM and transforming it into valuable BI. Design/methodology/approach – The approach taken is termed virtual excavation and builds on the similarities between the manipulation of technological artefacts virtual communities using various forms of SM and the excavation and analysis of physical artefacts found in archaeological settlements. Findings – The paper reports on a case study using this technique that looks at the Facebook fan pages of three mobile telecommunications service providers in Greece. The paper identifies many of the standard BI indicators as well as demonstrating that additional information relating to cross-page use can be collected by looking at how users manipulate artefact such as the “like” button in Facebook. Research limitations/implications – Although the methodology is widely applicable, the paper only reports on the analysis of one platform, Facebook, and is heavily reliant on visualization tools. Future work will examine different platforms and different tools for analysis. Practical implications – The paper discusses some of the ways in which this approach could be used and suggests some areas in which it might be applied. Originality/value – The approach of using virtual excavations to extract BI from virtual communities in online SM offers a systematic approach for dealing with a variety of information from a variety of different media that is not found in techniques based on information systems or management science.

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20

Cronin, Blaise. "Strategic intelligence and networked business." Journal of Information Science 26, no. 3 (2000): 133–38. http://dx.doi.org/10.1177/016555150002600302.

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21

Popoola, Nafisat Temilade. "Advanced Business Intelligence Systems Integrating Machine Learning for Strategic Financial Analysis and Corporate Performance Optimization." International Journal of Research Publication and Reviews 6, no. 3 (2025): 7245–56. https://doi.org/10.55248/gengpi.6.0325.1103.

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22

Francesco Di Tria, Ezio Lefons, and Filippo Tangorra. "Evaluation of Business Intelligence Systems." International Journal of Information Processing and Management 4, no. 3 (2013): 222–37. http://dx.doi.org/10.4156/ijipm.vol4.issue3.22.

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23

Weir, Jason. "A Web/Business Intelligence Solution." Information Systems Management 17, no. 1 (2000): 41–46. http://dx.doi.org/10.1201/1078/43190.17.1.20000101/31213.6.

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24

Gütler, Hannes, and Florian Schmidt. "Wie bei der Kelag Daten zu Informationen werden." BWK ENERGIE. 74, no. 9-10 (2022): 21–23. http://dx.doi.org/10.37544/1618-193x-2022-9-10-21.

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Mit M-Bus- und LoRaWAN-Sensorik sowie Systemen des Metering-Spezialisten Elvaco liest der Energieversorger Kelag stündlich vollautomatisiert 2 750 Zähler in ganz Österreich aus. Business Intelligence verwandelt die Daten in Informationen. Auf diese Weise kann das Unternehmen gesetzliche Vorgaben zur Energieeffizienz problemlos erfüllen und Potenziale für mehr Effizienz nutzen.

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25

Sattarapu, Praveen Kumar, Deepti Wadera, and Jaspreet Kaur. "Voice Engagement Leading to Business Intelligence." International Journal of Business Intelligence Research 12, no. 2 (2021): 1–23. http://dx.doi.org/10.4018/ijbir.294568.

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There are multiple studies establishing the importance of Business Intelligence (BI), in the Big Data Analytics context. Voice is yet to be seen as a contributing channel. Voice enabled assistants are at the forefront of conversational AI advancement. As humans speak to devices, brands and business are investing in engagement through voice channel. This voice engagement is resulting in both intangible and tangible benefits and generating voice commerce. The resultant voice data should be integral to BI, leading to Voice BI. This paper proposes a conceptual framework from engagement to intelligence, with support of five propositions to realise voice business intelligence. Type of applications and their engagement characterisation is segregated to create better understanding using Cross-Cases Observation Technique. Along with future research agenda to strengthen the propositions, this investigation observes building voice business intelligence by tracking relevant metrics which enable informed decisions.

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26

Akshay, Shrikant Nehre. "Business Intelligence Computational Intelligence in Vehicle and Transportation System." International Journal of Trend in Scientific Research and Development 4, no. 3 (2020): 61–64. https://doi.org/10.5281/zenodo.3892387.

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The Traffic and Transportation system is big problem in the world. So business intelligence in vehicle and transportation system solve this problem and solution with the help of new technologies. In the computational intelligence in vehicle and transportation system used computer electrical and electronic conversion technology management. Akshay Shrikant Nehre "Business Intelligence (Computational Intelligence in Vehicle and Transportation System)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30226.pdf Paper Url :https://www.ijtsrd.com/computer-science/artificial-intelligence/30226/business-intelligence-computational-intelligence-in-vehicle-and-transportation-system/akshay-shrikant-nehre

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27

McBride, Neil. "Business intelligence in magazine distribution." International Journal of Information Management 34, no. 1 (2014): 58–62. http://dx.doi.org/10.1016/j.ijinfomgt.2013.09.006.

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Castellanos, Malu, Florian Daniel, Irene Garrigós, and Jose-Norberto Mazón. "Business Intelligence and the Web." Information Systems Frontiers 15, no. 3 (2013): 307–9. http://dx.doi.org/10.1007/s10796-013-9435-8.

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Rouibah, Kamel, and Samia Ould-ali. "PUZZLE: a concept and prototype for linking business intelligence to business strategy." Journal of Strategic Information Systems 11, no. 2 (2002): 133–52. http://dx.doi.org/10.1016/s0963-8687(02)00005-7.

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30

Niu, Li, Jie Lu, Guangquan Zhang, and Dianshuang Wu. "FACETS: A cognitive business intelligence system." Information Systems 38, no. 6 (2013): 835–62. http://dx.doi.org/10.1016/j.is.2013.02.002.

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31

Cottrill, Ken. "TURNING COMPETITIVE INTELLIGENCE INTO BUSINESS KNOWLEDGE." Journal of Business Strategy 19, no. 4 (1998): 27–30. http://dx.doi.org/10.1108/eb039948.

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32

Cortez, Paulo, and Manuel Filipe Santos. "Knowledge Discovery and Business Intelligence." Expert Systems 30, no. 4 (2013): 283–84. http://dx.doi.org/10.1111/exsy.12042.

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33

Zhongyi Gong Rui, Wang. "Design and Application of Intelligent Scheduling System Based on Business and Labor Forecasting." International Journal of Science and Research (IJSR) 13, no. 3 (2024): 1174–76. http://dx.doi.org/10.21275/sr24312143206.

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34

Hawking, Paul, and Carmine Sellitto. "A Fast-Moving Consumer Goods Company and Business Intelligence Strategy Development." International Journal of Enterprise Information Systems 13, no. 2 (2017): 22–33. http://dx.doi.org/10.4018/ijeis.2017040102.

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The paper used the case study approach to report on how a Fast Moving Consumer Goods (FMCG) company developed their Business Intelligence strategy. Many companies are aware of the benefits that Business Intelligence brings to the corporation— however struggle to have an appropriate or well defined strategy. The authors document in the paper how the company experienced limited benefits with their initial Business Intelligence implementation— however, the subsequent development of a Business Intelligence strategy allowed the firm to redress initial project short-comings across several business areas. The company's Business Intelligence strategy included elements that directly addressed governance and resource management, business technology alignment, innovation and skills capability. The research investigated the experiences of a global FMCG company regarding their Business Intelligence use and the findings may not be generalizable.

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35

Wikamulia, Nathaniel, and Sani Muhamad Isa. "Predictive business intelligence dashboard for food and beverage business." Bulletin of Electrical Engineering and Informatics 12, no. 5 (2023): 3016–26. http://dx.doi.org/10.11591/eei.v12i5.5162.

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This research was conducted to provide an example of predictive business intelligence (BI) dashboard implementation for the food and beverage business (businesses that sell fast-expired goods). This research was conducted using data from a bakery's transactional database. The data are used to perform demand forecasting using extreme gradient boosting (XGBoost), and recency, frequency, and monetary value (RFM) analysis using mini batch k-means (MBKM). The data are processed and displayed in a BI dashboard created using Microsoft Power BI. The XGBoost model created resulted in a root mean square error (RMSE) value of 0.188 and an R2 score of 0.931. The MBKM model created resulted in a Dunn index value of 0.4264, a silhouette score value of 0.4421, and a Davies-Bouldin index value of 0.8327. After the BI dashboard is evaluated by the end user using a questionnaire, the BI dashboard gets a final score of 4.77 out of 5. From the BI dashboard evaluation, it was concluded that the predictive BI dashboard succeeded in helping the analysis process in the bakery business by: accelerating the decision-making process, implementing a data-driven decision-making system, and helping businesses discover new insights.

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Sapateiro, Cláudio Miguel, and Rui Miguel Bernardo. "Crossing Human Factors Research and Business Intelligence." International Journal of Enterprise Information Systems 16, no. 3 (2020): 78–92. http://dx.doi.org/10.4018/ijeis.2020070106.

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Starting from business intelligence (BI) reference models, this work proposes to extend the multi-dimensional data modelling approach to integrate human factors (HF)-related dimensions. The overall goal is to promote a fine grain understanding of the derived key performance indicators (KPIs) through an enhanced characterization of the operational level of work context. HF research has traditionally approached critical domains and complex socio-technical systems with a chief consideration of human situated action. Grounded on a review of the body of knowledge of the HF field, this work proposes the business intelligence for human factors (BI4HF) framework. It intends to provide guidance on pertinent data identification, collection methods, modelling, and integration within a BI project endeavour. BI4HF foundations are introduced, and a use case on a manufacturing industry organization is presented. The outcome of the enacted BI project referred in the use case allowed new analytical capabilities regarding newly derived and existing KPIs related to operational performance, providing insight into the value of the BI4HF framework.

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Yeoh, William, Andy Koronios, and Jing Gao. "Managing the Implementation of Business Intelligence Systems." International Journal of Enterprise Information Systems 4, no. 3 (2008): 79–94. http://dx.doi.org/10.4018/jeis.2008070106.

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38

Tešendić, Danijela, and Danijela Boberić Krstićev. "Business Intelligence in the Service of Libraries." Information Technology and Libraries 38, no. 4 (2019): 98–113. http://dx.doi.org/10.6017/ital.v38i4.10599.

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This paper describes implementation of business intelligence tools in the libraries. A complete procedure for building a data warehouse is described on the case study of the BISIS library management system. During development of a data warehouse model, user requirements about reporting are detected and structure of already existing transactional databases in the BISIS system is analysed. Based on this analysis, three data warehouse models have been proposed that would satisfy the requirements for analytical processing of data. The paper presents the usage of one OLAP tool, but the proposed data warehouse model is independent of the choice of OLAP tools and any other tool can be integrated with the proposed data warehouse.

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Gallinucci, Enrico, Matteo Golfarelli, and Stefano Rizzi. "Advanced topic modeling for social business intelligence." Information Systems 53 (October 2015): 87–106. http://dx.doi.org/10.1016/j.is.2015.04.005.

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Drushku, Krista, Julien Aligon, Nicolas Labroche, Patrick Marcel, and Verónika Peralta. "Interest-based recommendations for business intelligence users." Information Systems 86 (December 2019): 79–93. http://dx.doi.org/10.1016/j.is.2018.08.004.

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41

Hall, H. "Online information source: tools of business intelligence?" Journal of Information Science 26, no. 3 (2000): 139–44. http://dx.doi.org/10.1177/0165551004233131.

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Hall, Hazel. "Online information sources: tools of business intelligence?" Journal of Information Science 26, no. 3 (2000): 139–43. http://dx.doi.org/10.1177/016555150002600303.

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43

Bergeron, Pierrette. "Regional business intelligence: the view from Canada." Journal of Information Science 26, no. 3 (2000): 153–60. http://dx.doi.org/10.1177/016555150002600305.

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44

Herring, Jan P. "Senior Management Must Champion Business Intelligence Programs." Journal of Business Strategy 12, no. 5 (1991): 48–52. http://dx.doi.org/10.1108/eb039444.

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45

Maté, Alejandro, Juan Trujillo, Félix García, Manuel Serrano, and Mario Piattini. "Empowering global software development with business intelligence." Information and Software Technology 76 (August 2016): 81–91. http://dx.doi.org/10.1016/j.infsof.2016.04.011.

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46

Ramirez-Angulo, Pedro Julian, and Rosa Alexandra Chaparro Guevara. "Business Intelligence Readiness Assessment for a Shopping Mall." Journal of Cases on Information Technology 22, no. 2 (2020): 18–33. http://dx.doi.org/10.4018/jcit.2020040102.

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This case takes place at SCC, a recognized shopping mall located in Cali, Colombia. In March 2018, Javier Ortiz, CEO of SCC, contacted a group of researchers to identify the key success factors before the implementation of business intelligence practices. For this, the researcher's group conducted a set of deep semi-structured interviews to establish the current state of processes, people and technologies around business information. With this information, a business intelligence maturity stage model is presented to identify a low level of business intelligence practices, that represents a long list of challenges to be faced by the SCC with a limited budget. This case focuses on how to analyze the business intelligence readiness assessment using exploratory analysis, and seeks to promote the skills and competencies oriented to prioritize actions based on research to improve decision making before a business intelligence implementation project.

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47

Grabova, Oksana, Jerome Darmont, Jean-Hugues Chauchat, and Iryna Zolotaryova. "Business intelligence for small and middle-sized entreprises." ACM SIGMOD Record 39, no. 2 (2010): 39–50. http://dx.doi.org/10.1145/1893173.1893180.

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48

Goethals, Frank G. "Data warehousing and business intelligence for e-commerce." ACM SIGMOD Record 31, no. 2 (2002): 71–72. http://dx.doi.org/10.1145/565117.565132.

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49

Veingerl Čič, Živa, Matjaž Mulej, and Simona Šarotar Žižek. "Different intelligences’ role in overcoming the differences in employee value system." Kybernetes 47, no. 2 (2018): 343–58. http://dx.doi.org/10.1108/k-06-2017-0200.

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Purpose This paper aims to present the findings of the research about the role of different intelligences in overcoming the differences in employee value system as a source of success. Design/methodology/approach Based on their previous research, the authors used desk and informal field research, the Dialectical Systems Theory and its Law of Requisite Holism. Findings The integration of one’s personal development with one’s individual intelligence influences human value systems. Knowledge and developing of various types of intelligence matter: it lets individuals develop faster, in the long run. The higher one’s level of intelligence is, the easier one finds it to face problems or experience. Thus, one is becoming a mature personality, who can overcome extreme alternatives to the briefed human values. This process can also receive meaningful support from the exercise of social responsibility, which is one’s responsibility for one’s impacts on society, i.e. people and nature. Success of the process depends on “personal requisite holism”. The top managers need significantly more emotional and social competences than the others. Research limitations/implications The topic is researched with qualitative analysis in desk and informal field research. Quantitative methodological approach took place in the authors’ cited previous publications. Practical implications Work distribution makes the leaders and subordinates differ in prevailing values, too. Mastering of these differences will support business success, survival of jobs included and well-being of coworkers from both groups. Application of the cognitive, emotional and spiritual intelligences might help the organization meet this need. The fourth – physical intelligence – supports ensuring the psychological well-being at work; from this, other mentioned intelligences have been developed. Mastering of these differences can also receive support from methods of creative cooperation, social responsibility and personal requisite holism; the authors have reported about these elsewhere, and only point to these in this study. Social implications The more holistic intelligences system generates a more socially responsible society. Originality/value No similar concept is offered in the available literature.

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Bekmirzaev, Obidjon, Kumushbibi Gulomova, and Sanjar Mukhamadiev. "Research on New Trends and Development Prospects of Enterprise Resource Planning (ERP) Systems." European International Journal of Multidisciplinary Research and Management Studies 5, no. 3 (2025): 50–54. https://doi.org/10.55640/eijmrms-05-03-12.

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This article analyzes modern trends and development prospects of ERP systems. The importance of ERP systems in automating business processes and increasing efficiency is highlighted, and their integration with cloud technologies, artificial intelligence, IoT and mobile applications is discussed. Also, the development prospects of ERP systems are considered as artificial intelligence-based automation, increased cybersecurity measures, flexibility and the creation of user-friendly interfaces. Continuous improvement of ERP systems serves to increase business efficiency and ensure competitiveness.

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