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As a result of the digital transition, planned or imposed, it is important that companies introduce control tools that allow measuring, validating, and improving the operations performed automatically, by the various technologies involved in the processes. For SMEs the challenge is increased by the lack of available resources. The additional benefits that the introduction of these tools can bring are several and diversified, but there are also several challenges to their implementation. One of the main obstacles will be the time factor, which in this case covers several dimensions. We intend to demonstrate that in the first stage of the implementation process, the ChatGPT technology can be important in presenting these benefits and challenges to managers of SMEs as well as higher education institutions, increasing the training of future managers in business intelligence and data analysis platforms that are mostly open source and low/no code for cost reduction. We focus our attention on a small Portuguese company where the advances in the digital transition were imposed by the pandemic but now faces the challenges of uncertainty in the quality of its process data and has to make choices between the visible and invisible costs of implementing business intelligence tools.
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This study aims to investigate the mediating role of business intelligence in the relationship between critical success factors for business intelligence and strategic intelligence in the ear of the COVID-19 epidemic. The data acquired from a sample of 392 managerial positions from Jordanian commercial banks was examined using a multi-regression analysis in SPSS. This study's findings came in agreement with the notion that business intelligence boosts the link between CSF for BI and strategic intelligence. The study's findings have clues for both the current body of literature and decision-makers. Hence, businesses that have embraced BI understand the advantages of improving their strategic intelligence skills and decision-making procedures during the COVID-19 outbreak.
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The modern business environment in which the world economy operates brings increasing unpredictability, which makes it difficult to plan and implement business continuity management. Recent examples are the emergence and spread of the corona virus and the war in Ukraine. Market-oriented economies are characterized by a reduction in the life cycle of companies and competition in saturated industrial sectors. Integration processes, takeovers and mergers, represent one of the ways of implementing development strategies of organizations and most often take place in waves and in crisis periods due to economic shocks and the influence of internal and external factors on the organization. Mentioned strategy is used by companies in order to acquire the necessary capital and resources in an effort to establish their position on the market more quickly and efficiently and to carry out activities of greater volume and income for the purpose of survival and faster progress. Due to the trends of M&A and the growing inconsistency and uncertainty in business, the aim of this paper is to highlight the consequences that M&A brings when it comes to managing the business continuity of a "newly integrated" organization. The impact on employees, business processes and general functioning and management of business continuity during and especially after integration will be observed. In order to fulfill the objective of the paper, the empirical part of the paper uses the Delphi method, in which the source of data is based on statistical collection and then the interpretation of the answers to the set questionnaire from a number of experts in the field of business continuity management, the banking sector, auditing and several business units that have recently passed M&A activities in order to consolidate attitudes about the consequences that integration processes bring for business continuity management and the organization as a whole. The paper will analyze the consequences of mergers and acquisitions for the management of business continuity, identify its advantages and disadvantages, and present recommendations for future such processes in the concluding remarks.
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The COVID 19 black swan event has disrupted every aspect of life in unprecedented ways, causing organizations to scramble to effectively sense and respond to the tumultuous business environment. Business intelligence and analytics (BI&A) capability has gained attention as a key weapon in the arsenal needed to combat turbulent times and to adjust to the post-pandemic new normal. Post-pandemic BI&A trends point to changes in organizational priorities for BI&A infrastructure that influence the traditional view of BI&A architecture and its role within an organization. As a result, new challenges and opportunities are emerging. This paper identifies and examines twelve key post-pandemic BI&A trends from industry practice and six major research themes. It also proposes an initial set of research questions that could inspire future research in BI&A in the post-pandemic new normal. © 2022 IEEE Computer Society. All rights reserved.
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Healthcare is one of the largest and complex sectors in the stock market. It comprises a broad range of companies including hospitals, healthcare providers, selling of medical devices, drugs, and insurance. When the coronavirus unexpectedly comes into sight, the entire world economy has stagger. This has decreased the surgeries, outpatient department footfall, international patients, medical device pharmaceutical, and healthcare commodities. Medical device industry has worst affected the export of medical devices and the critical raw materials are disturbed due to the restriction on movement, social distancing, travel, and transport. Healthcare industry challenged a burden such as 100% of alertness for the protection in hospitals and further investment of manpower, equipment, consumables, etc. Health and wealth are the two main components of well-being in life. Almost everything requires money from food to education to health services. During this pandemic situation, we have to take care of both health and wealth. Healthcare industry is one of the world's major and fastest emergent enterprises. Nowadays, with the advancement of expertise like analytics, business intelligence (BI), and artificial intelligence (AI), the prediction of stock value has improved and benefits the investors to make the right decisions. This study aimed to predict the hospital and healthcare services stocks in the Indian Stock Market Index (Nifty). Companies like Apollo Hospitals Enterprise Limited, Cadila Healthcare Ltd., Dr. Reddy's Laboratories, Fortis Healthcare Limited, Max Healthcare InstituteLimited, Opto Circuits Limited, Panacea Biotec, Poly Medicure Ltd., Thyrocare Technologies Limited, and Zydus Wellness Ltd. were used in this study to predict healthcare stocks. Hospital and healthcare service stocks were predicted using linear regression (LR), autoregressive integrated moving average (ARIMA), and long short-term memory (LSTM). © 2022 Scrivener Publishing LLC.
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PurposeThis viewpoint paper aims to discuss sustainable digitalisation of facilities management (FM) through the implementation of the newly recognised International Organization for Standardization (ISO) standards within the ISO 41000 series.Design/methodology/approachThis viewpoint paper provides a review of the literature of the recent ISO documents and academic study. The content is also dependent on the authors' opinions and interpretation.FindingsFM is currently shifting emphasis towards a strategic focus through the adoption of the new recognised international ISO standards that consider sustainable digitalisation in business decisions. However, the FM sector is encountering potential risks to the implementation of the new recognised international ISO standards. Digitalisation is one kind of force that has shaped the management of the built environment and FM recently and rapidly, especially in the Covid-19 period. This is impacting the FM industry. As standardisation aims at establishing a constantly evolving baseline of proven practices, standardisation can be considered a part of sustainable FM. It is believed that standardised and strategic level support is crucial for the smooth adoption of sustainable FM practices and processes. Standards such as the ISO standards, applied to the global FM industry, help in objectively quantifying the added value of FM to the core business. Advanced technology and digitalisation can contribute to the sustainability of any profession and industry, but it also requires a community to tackle the problems.Originality/valueThis paper contributes to the FM industry by making recommendations for improvement in the use of digitalisation. In summary, the significant finding of this viewpoint paper is that digitalisation offers both possibilities and problems in the application of the new recognised international ISO standards within the FM industry.
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Purpose: While business intelligence and analytic (BIA) systems have been developed by large corporations around the world, micro-, small- and medium-sized enterprises (MSMEs) have recently paid attention and deployed BIA adoption, particularly during the Covid-19 pandemic disruption. This study sheds light on how MSMEs adopt the BIA systems and then proposes a framework for the BIA adoption process in the context of MSMEs. Design/methodology/approach: The multiple case research design and interpretivism approach are employed for expanding the theoretical boundary of the strategic management fields in BIA adoption by MSMEs. In total, 35 semi-structured interviews were conducted with senior managers and owners involved in BIA adoption from 17 participating MSMEs. Findings: The research study identifies three BIA adoption stages with specific technical and managerial features in the path of BIA adoption in each stage, corresponding to the level of BIA maturity of MSMEs. The authors also highlight other factors that directly influence the successful adoption and transformation from each stage to another. Research limitations/implications: The research study identifies three BIA adoption stages with specific technical and managerial features in the path of BIA adoption at each stage that corresponds to the level of BIA maturity of MSMEs. Besides, this study also extends the current literature on BIA adoption in an organisation during the Covid-19 pandemic by identifying several contextual barriers that directly influence the BIA adoption. Practical implications: Research findings can help business leaders and owners of MSMEs to determine the BIA maturity of their organisation. Furthermore, the authors' framework can also be used by consultancies and standard setters to develop detailed BIA adoption strategies and tactics that support MSMEs' digitalisation towards BIA adoption. Originality/value: The research study's results highlight that contextual factors, leadership competencies, motivations and barriers for BIA adoption can also be used to help MSMEs' leaders and owners to trigger, advance or eliminate challenges for the adoption of BIA initiatives in MSMEs. © 2023, Emerald Publishing Limited.
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In this modern and digital era, digital transformation is echoed as one of the organization's efforts to survive through Business Intelligence (BI). BI has become a buzzword even among business actors or organizations, not least for Small and Medium Enterprises (SMEs). SMEs are one of the sectors affected by the COVID-19 pandemic, namely the number of SME players who have lost their income and are finally forced to go out of business. BI is a combination of techniques and methods in terms of fulfilling access to information and a concise data management mechanism to be able to have a positive influence on SME business activities. It is because the strength of BI significantly impacts strategic decision-making using processing tools from Microsoft, namely SQL Server Integration Services (SSIS) and SQL Server Reporting Services (SSRS). This study aims to see the extent of BI as an alternative solution in decision-making by all SMEs in Indonesia. This research contributes to SMEs through the implementation of BI;SMEs get explicit knowledge about the factors that affect the performance of SMEs to help SMEs in making decisions. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Coronaviruses have been around for years, they are a large family of viruses that can create a variety of anomaly in humans and even in animals, the first symptoms are summed up by a simple cold with fever but it can spread to very serious respiratory problems. This disease has caused a global crisis on all levels;it's a very big challenge that we have lived it since the Second World War. The challenging problem of COVID-19 data science is considered in this paper, where we propose a new data warhouse, that best meets the needs of scientists. The proposed data warhouse as of February 24, 2020, is based on heterogeneous data provided by Our World in Data GitHub and Kaggle database, which are collected daily from Our World in Data COVID-19. Furthermore, this data warehouse is used to feed dashboards in real time that helps the decision-makers to strengthening of the coronavirus screening network, track the spread of the virus before and after vaccination around the world to fight against this dangerous disease.
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This article presents a multi-stage guided technical project coding Python scripts for utilizing Amazon Web Services (AWS) to work with a document-store database called DynamoDB. Students doing this project should have taken an introductory programming class (ideally in Python) and a database class to have experience with Python coding and database manipulation/querying in a relational environment. Students learn new data formats (Python dictionaries, JSON text data, keyvalue storage structures) and learn how to transform data from one format to another. They also gain experience with data visualization. The project was first carried out in a business intelligence (BI) course during Spring 2020 semester in the midst of COVID and included video tutorials. Since then, it has been refined and used each semester the BI course is taught.
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Digital transformation is an urgent strategy for enterprises to promote business activities forward the modern direction. The worldwide outbreak of the COVID-19 pandemic has spurred the digital transformation in many areas of society, especially business activities. This study aims to clarify the current reality of digital transformation in business during the COVID-19 pandemic in Vietnam. The study data is collected by an online survey including 82 enterprises from the Vietnamese economy. The statistical analysis shows that these enterprises have implemented digital transformation in their business operations to adapt to the strict social distancing measures caused by the pandemic. In general, digital transformation has helped businesses increase revenue as well as reduce operating costs, however, investment expenditure for the digital transformation of these enterprises has been still quite limited. Finally, business leaders confirm that their enterprises would accelerate in the digital transformation process in the near future. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Sentiment analysis is used to analyze data in text format such as tweets from Twitter social media users. Twitter is one of the most popular social media with more than half a billion users and can generate large volumes of data. It is difficult to operate large-scale data, so the data warehouse can be used as a data storage area that allows the operation of large-scale data. The final step of data warehousing is the application of business intelligence. This research uses dimensional model approach to build data warehouse from Twitter and uses lexicon-based approach to analyze public opinion of Twitter users toward covid-19 vaccine in Indonesia. The results show that the creation of a data warehouse and sentiment analysis have been successfully carried out based on the evaluation of the data warehouse and sentiment analysis. The evaluation carried out on the data warehouse is to examine the components of the dimensional model based on indicators and dimensional modeling rules by Kimball. While the evaluation carried out on sentiment analysis is the confusion matrix with the result of the accuracy of sentiment analysis is 74%. © 2022 IEEE.
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Purpose: The purpose of this study was to demonstrate a cloud business intelligence model for industrial SMEs. An initial model was developed to accomplish this, followed by validation and finalization of the cloud business intelligence model. Additionally, this research employs a mixed-techniques approach, including both qualitative and quantitative methods. This paper aims to achieve the following objectives: (1) Recognize the Cloud business intelligence concepts. (2) Identify the role of cloud BI in SMEs. (3) Identify the factors that affect the design and presenting a Cloud business intelligence model based on critical factors affecting SMEs during pandemic COVID-19. (4) Discuss the importance of Cloud BI in pandemic COVID-19 for SMEs. (5) Provide managerial implications for using Cloud BI effectively in Iran's SMEs. Design/methodology/approach: In the current study, an initial model was first proposed, and the cloud business intelligence model was then validated and finalized. Moreover, this study uses a mixed-methods design in which both qualitative and quantitative methods are used. The fuzzy Delphi Method has been applied for parameter validation purposes, and eventually, the Cloud business intelligence model has been presented through exploiting the interpretive structural modeling. The partial least squares method was also applied to validate the model. Data were also analyzed using the MAXQDA and Smart PLS software package. Findings: In this research, from the elimination of synonym and frequently repeated factors and classification of final factors, six main factors, 24 subfactors and 24 identifiers were discovered from the texts of the relevant papers and interviews conducted with 19 experts in the area of BI and Cloud computing. The main factors of our research include drivers, enablers, competencies, critical success factors, SME characteristics and adoption. The subfactors of included competitors pressure, decision-making time, data access, data analysis and calculations, budget, clear view, clear missions, BI tools, data infrastructure, information merging, business key sector, data owner, business process, data resource, data quality, IT skill, organizational preparedness, innovation orientation, SME characteristics, SME activity, SME structure, BI maturity, standardization, agility, balances between BI systems and business strategies. Then, the quantitative part continued with the fuzzy Delphi technique in which two factors, decision-making time and agility, were deleted in the first round, and the second round was conducted for the rest of the factors. In that step, 24 factors were assessed based on the opinions of 19 experts. In the second round, none of the factors were removed, and thus the Delphi analysis was concluded. Next, data analysis was carried out by building the structural self-interaction matrix to present the model. According to the results, adoptability is a first-level or dependent variable. Regarding the results of interpretive structural modeling (ISM), the variable of critical success factors is a second-level variable. Enablers, competencies and SME characteristics are the third-level and most effective variables of the model. Accordingly, the initial model of Cloud BI for SMEs is presented as follows: The results of ISM revealed the impact of SME characteristics on BI critical success factors and adoptability. Since this category was not an underlying category of BI;thus, it played the role of a moderating variable for the impact of critical success factors on adoptability in the final model. Research limitations/implications: Since this study is limited to about 100 SMEs in the north of Iran, results should be applied cautiously to SMEs in other countries. Generalizing the study's results to other industries and geographic regions should be done with care since management perceptions, and financial condition of a business vary significantly. Additionally, the topic of business intelligence in SMEs constrained the sample from the start since not all SMEs use business int lligence systems, and others are unaware of their advantages. BI tools enable the effective management of companies of all sizes by providing analytic data and critical performance indicators. In general, SMEs used fewer business intelligence technologies than big companies. According to studies, SMEs understand the value of simplifying their information resources to make critical business choices. Additionally, they are aware of the market's abundance of business intelligence products. However, many SMEs lack the technical knowledge necessary to choose the optimal tool combination. In light of the frequently significant investment required to implement BI approaches, a viable alternative for SMEs may be to adopt cloud computing solutions that enable organizations to strengthen their systems and information technologies on a pay-per-use basis while also providing access to cutting-edge BI technologies at a reasonable price. Practical implications: Before the implementation of Cloud BI in SMEs, condition of driver, competency and critical success factor of SMEs should also be considered. These will help to define the significant resources and skills that form the strategic edge and lead to the success of Cloud BI projects. Originality/value: Most of the previous studies have been focused on factors such as critical success factors in cloud business intelligence and cloud computing in small and medium-sized enterprises, cloud business intelligence adoption models, the services used in cloud business intelligence, the factors involved in acceptance of cloud business intelligence, the challenges and advantages of cloud business intelligence, and drivers and barriers to cloud business intelligence. None of the studied resources proposed any comprehensive model for designing and implementing cloud business intelligence in small and medium-sized enterprises;they only investigated some of the aspects of this issue. © 2021, Emerald Publishing Limited.
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The COVID-19 pandemic, first detected in December 2019, spread drastically globally within a short period. Such a pandemic has caused enormous loss of lives and health complications and adversely affected the world economies. Effective tracking of the spread of the disease around the globe and predicting when the next wave will occur has become critical in measures geared towards mitigating COVID-19. This paper explores different ways of utilizing analytics and business intelligence tools and solutions to understand the spread of COVID-19 around the globe and predict the number of new COVID-19 cases likely to be recorded. Microsoft Power BI is used to visualize COVID-19 data simply and intuitively in different ways for health decision-makers and concerned parties to easily understand the spread of COVID-19 through various visualizations and dashboards. We also utilize predictive analytics capabilities in Microsoft Power BI to predict the number of COVID-19 cases likely to be recorded in the next few months. The obtained results showed that COVID-19 cases increased over time, particularly in crowded countries. In addition, the results proved that the death rate is reducing with time even though the cases number is increasing. © 2022 IEEE.
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The major goal of this study is to investigate how business intelligence is used to develop business operations in SMEs, as well as the elements that influence business intelligence adoption. Following the sample verification procedure, 232 samples were collected. The SEM software was used to process all of the data acquired in the research investigation. The study's findings show that TOE has a significant effect on SMEs' adoption of business intelligence solutions. According to the study's results, the researcher believes that leaders and decision-makers in firms would use business intelligence systems to define all activities, responsibilities, and work procedures in order to increase organizational ambidexterity and performance. Copyright © 2022, Anka Publishers. All rights reserved.
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Big data technology and predictive analytics exhibit advanced potential for business intelligence (BI), especially for decision-making. This study aimed to explore current research studies, historic developing trends, and the future direction. A bibliographic study based on CiteSpace is implemented in this paper, 681 non-duplicate publications are retrieved from databases of Web of Science Core Collection (WoSCC) and Scopus from 2000 to 2021. The countries, institutions, cited authors, cited journals, and cited references with the most academic contributions were identified. Social networks and collaborations between countries, institutions, and scholars are explored. The cross degree of disciplinaries is measured. The hotspot distribution and burst keyword historic trend are explored, where research methods, BI-based applications, and challenges are separately discussed. Reasons for hotspots bursting in 2021 are explored. Finally, the research direction is predicted, and the advice is delivered to future researchers. Findings show that big data and AI-based methods for BI are one of the most popular research topics in the next few years, especially when it applies to topics of COVID-19, healthcare, hospitality, and 5G. Thus, this study contributes reference value for future research, especially for direct selection and method application.
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The COVID-19 pandemic has had an impact on many aspects of society in recent years. The ever-increasing number of daily cases and deaths makes people apprehensive about leaving their homes without a mask or going to crowded places for fear of becoming infected, especially when vaccination was not available. People were expected to respect confinement rules and have their public events cancelled as more restrictions were imposed. As a result of the pandemic's insecurity and instability, people became more at ease at home, increasing their desire to stay at home. The present research focuses on studying the impact of the COVID-19 pandemic on the desire to stay at home and which metrics have a greater influence on this topic, using Big Data tools. It was possible to understand how the number of new cases and deaths influenced the desire to stay at home, as well as how the increase in vaccinations influenced it. Moreover, investigated how gatherings and confinement restrictions affected people's desire to stay at home. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Due to the continuous and growing spread of the corona virus worldwide, it is important, especially in the business era, to develop accurate data driven decision-aided system to support business decision-makers in processing, managing large amounts of information in the recruitment process. In this context, e-Recruitment Recommender systems emerged as a decision support systems and aims to help stakeholders in finding items that match their preferences. However, existing solutions do not afford the recruiter to manage the whole process from different points of view. Thus, the main goal of this paper is to build an accurate and generic data driven system based on Business intelligence architecture. The strengths of our proposal lie in the fact that it allows decision makers to (1) consider multiple and heterogeneous data sources, access and manage data in order to generate strategic reports and recommendations at all times (2) combine many similarity's measure in the recommendation process (3) apply prescriptive analysis and machine learning algorithms to offer adapted and efficient recommendations. Copyright © 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.
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From December 2019, a major outbreak called novel corona virus is infecting people all over the world now. It is believed to be a beta corona virus of SARS-CoV and MERS-CoV. Infected people are unable to detect this disease as they feel normal till 10-12 days. After that, the virus infects the whole body and starts to find another body to infect, multiplying it day by day. As per the media news and other sources, epidemic is spreading globally, especially in countries like China, Italy where its effect is at peak, killing thousands of people. Based on the data of infected Covid-19 people in India, we systematically discuss the outbreak of epidemic corona virus in India. Defining the structure of active cases day by day, we predict the future of Covid-19 in India. We also suggest important measures to help prevent the spread of Covid-19 in India. © 2022 IEEE.
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The pressure on the speed of information processing ranks business intelligence technologies among the fastest growing decision support tools. The main goal of this article is, applying the UTAUT 2 (the unified theory of acceptance and use of technology), to verify the factors determining the implementation of business intelligence tools in business processes, especially decision-making, and their subsequent optimal use in business practice. The researched scheme was modified according to the specifics of business intelligence tools and was supplemented by user behaviour in decision-making. The verification was performed using a questionnaire survey based on UTAUT 2 theory and 152 respondents were included in the analysis. According to the results, the most important variable of influence on both the behavioural intention and the users' behaviour itself in decision-making was the factor of habit. And surprisingly, some previously recognised links were not confirmed, especially the factors influencing the intention of behaviour (effort expectancy, social influence, facilitating conditions). So, there is room after almost 10 years and experience gained during the Covid-19 pandemic to modify the latest version of a model.