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Globally, the transportation and logistics sector is facing economic disruptions owing to geopolitical tensions and post-COVID-19 global economic downturns. This disruption places more pressure on transportation companies to review their work methods and processes. Coupling data and model-driven approaches is essential for developing effective and efficient resilience strategies. To address this issue, this study provides an overview of the appearance of simulation in business analytics. However, a thorough review of the literature based on the PRISMA search process allowed us to identify that none of the previous studies could highlight the role or evaluate the hybridization between business analytics and simulation and their joint use in freight transportation. Moreover, this study proposes a collaborative framework based on the intuitionistic fuzzy analytic hierarchy process (IF-AHP) technique to select a business analytics-enabled simulation architecture. This study contributes to the freight transport sector by setting up an updated list of criteria and sub-criteria necessary for business analytics evaluation and enriches the literature by applying the IF-AHP technique to a concrete case of implementing data analytics and simulation. This study also suggests future directions to enrich the academic literature and offers insights to improve the framework for other use cases. © 2023 Elsevier Ltd
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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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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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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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This article proposes a new paradigm of asymmetric multifractality in financial time series, where the scaling feature varies over two adjacent intervals. The proposed approach first locates a change-point and then performs a multifractal detrended fluctuation analysis (MF-DFA) on each interval. The study investigates the impact of the COVID-19 pandemic on asymmetric multifractal scaling by analyzing financial indices of the G3+1 nations, including the world's four largest economies, from January 2018 to November 2021. The results show common periods of local scaling with increasing multifractality after a change-point at the beginning of 2020 for the US, Japanese, and Eurozone markets. The study also identifies a significant transition in the Chinese market from a turbulent multifractal state to a stable monofractal state. Overall, this new approach provides valuable insights into the characteristics of financial time series and their response to extreme events.
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As the number of people infected with COVID-19 continues to rise, a number of nations have implemented state wide quarantines. This has resulted in a global financial crisis that is having severe impacts on countries all around the world. As a direct consequence of the epidemic, unemployment rates have increased in a number of different regions, which has a substantial and detrimental effect on trade across the globe. In light of the current state of the economy, Artificial Intelligence (AI) is causing a shift in the manner in which businesses evaluate their bitcoin holdings. The application of AI in a commercial setting has the potential to produce a wide range of beneficial results. We are spared from completing as much manual labour as a direct result of the favourable effects that AI has had on technology. These consequences can be noticed in our day-to-day lives. In the event that there is a pandemic, having knowledge of AI and the various strategies it employs, such as the classifier model, could be beneficial. Humans will be better suited to make decisions if they have rapid access to the analyses and projections that are created by AI and big data. In order to be prepared for the arrival of the new world, the company is putting in more effort, in collaboration with small and medium-sized enterprises (SMEs) and start-ups, to improve the administration of virtual enterprises by having a presence on a variety of different e-trade systems. Artificial intelligence (AI) is currently being utilised in a variety of settings to assist with the process of identifying and implementing workable solutions to a variety of problems that can develop in the workplace. AI is being used to improve business operations in a wide variety of spheres, including marketing, fraud detection, algorithmic trading, customer assistance, portfolio management, and product recommendations based on customer preferences. These are just few of the sectors. These are just a few examples of the kinds of problems that artificial intelligence might be able to solve in the future. Given the present worth of cryptocurrencies, technological developments may also be made in order to improve the performance of the rules that have been provided and produce the most accurate conclusion that is possible. © 2022 IEEE.
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As the range of COVID-19 sufferers increased, many nations imposed a complete lockdown. As a result, it caused a devastating international financial disaster everywhere in the world. Technical and essential evaluation are two methods for determining future worth. Different strategies use statistics from outside the market, such as monetary conditions, hobby rates, and geopolitical events, to forecast future charge. We use technical evaluation forecasts potential charge using buying and selling statistics from the market, which includes charge and buying and selling volume, whereas other strategies use statistics from outside the market, such as monetary conditions, hobby rates, and geopolitical events. The objective of this project is to give technical and fundamental analysis using machine learning approaches. In business, AI is broadly used to remedy and optimize various problems, including marketing, credit score card fraud detection, algorithmic trading, patron service, portfolio management, and product advice primarily based totally on patron needs. Furthermore, the technology due used in this finished greater to optimize the proposed set of rules to attain the maximum correct result primarily based totally on the current valuation of cryptocurrency. This project is to use machine learning techniques to provide technical analysis. The incorporation of new technology into financial institutions has the potential to propel cryptocurrency values to time highs. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Companies face the challenge of managing customer relationships (CRM) in a context marked by a drastic digital transformation and unbridled evolution of consumer behavior, exacerbated by the COVID-19 pandemic. The customer is more demanding, has access to the global market and interacts with companies through multiple digital channels, such as email, social networks, mobile apps or instant messaging. In this situation, the success of a CRM implementation highly depends on information technology and the applications used. To harmonize this new business context with the development of information systems (IS), a suitable CRM ontology and enterprise architecture (EA) is needed. While an ontology-based conceptual model provides a unifying framework, aids sharing and reusing knowledge, and facilitates communication within a domain, an EA-based model unequivocally describes, analyzes, and visualizes how an organization should operate from the perspective of business, application, and technology. The purpose of the paper is the proposal of CURIE-O, a CRM OntoUML UFO-based ontology, together with CURIE-EA, a CRM ArchiMate-based EA to serve business managers and IS specialists an updated unifying framework of reference in the CRM domain as well as a highly efficient tool to support application development and maintenance in this changing and increasingly digital context. Modeling has proven to be an essential element to achieve high-performance information systems. In order to apply the ontology and the EA proposed here, the authors developed a CRM task management application prototype that was implemented as a case study in a consulting company. The methodology followed was design science research (DSR), in order to design and validate the artifacts. Within the DSR framework, other complementary research methods have been used, in particular literature research, interviews and focus groups carried out with several hotel chains in Tenerife (Canary Islands). The main existing CRM models in the scientific literature have also been analyzed together with the leading CRM market solutions.
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Machine learning makes the computer able to perform without explicit programming. So, machine learning is now applied to each and every field of our daily life. The broad range of applications of machine learning are disease detection, weather forecasting, gaming, political discussion, business analytics, acoustics, agriculture, energy forecasting, genomics, etc. The advances in artificial intelligence and machine learning is a combination of tools and techniques used together to solve cognitive problems. The concepts have been effectively executed through BERT and the GPT-2 architectures. Convolutional neural network which implements depthwise separable convolution and other neural networks are also used based on the requirement of the application. Machine learning strategies used for prediction and prognosis of the COVID-19 are partial derivative regression and nonlinear machine learning. Adaptive neural fuzzy inference system is used for wind power detection in power systems. Hence, it gives richer proposals and bits of knowledge for the ensuing decisions based on past information and activities with the extreme scope of production enhancement. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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The article solves the problem of ensuring effective corporate governance through the study of social effects and such a phenomenon as employee resistance to change. The main purpose of the study is to develop practical ways of increasing the effectiveness of corporate governance by assessing the intensity of employee resistance to change in turbulent external socio-economic conditions based on the business analytics platform. To achieve this, the authors compared theoretical thinking with empirical testing methods. This study was conducted using the methods of analysis and synthesis, surveys, expert evaluations, and the method of taxonomy. The results revealed: (1) Reliability and sustainability of survey tools;(2) Dependence of employee resistance to change on individual psychological and socio-psychological reasons and organizational barriers;(3) The effectiveness of assessing the level of intensity of employee resistance to organizational change in negative socio-economic conditions based on the calculation of the integrated coefficient;(4) The importance of effective work of HR managers that is aimed at overcoming employee resistance to change in order to achieve positive social effects. We have determined that achieving strategic goals of the company's development through the implementation of corporate governance is conditioned by positive organizational change, for the successful implementation of which it is necessary to assess the employee resistance to change in order to effectively overcome it. The study substantiates the main groups of reasons for persistent employee resistance to change. We offer practical recommendations on how to assess the intensity of employee resistance to change. In the course of additional research, proposals for successful planning of employee development programs were formulated. Positive social external effects of corporate governance effectiveness were determined.
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Grounded on resource-based view and dynamic capability perspectives, this research aims to explore linkages between the firm's big data management activities (BDMA), green manufacturing (GM) practices, and sustainable business performance (SBP). The research model was empirically evaluated using data collected from 248 pharmaceutical manufacturers in India during the COVID-19 pandemic. The analysis was performed using a covariance-based structural equation modeling using AMOS 20. The results indicate that GM activities impact SBP directly. Further results imply the mediating role of GM practices on the relationship between BDMA and SBP. The analysis reveals that senior management's resource commitment in pharmaceutical firms is a moderating mechanism in strengthening the association between BDMA and GM practices. This study is significant as it provides key theoretical and managerial implications for pharmaceutical sectors during emergent situations.
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Technology, market, and competitive dynamics are requiring firms in restaurant/food service supply chains to improve their analytics capabilities, which have tended to lag behind other comparable industries. The global COVID-19 pandemic has further encouraged industrial leaders to evaluate new challenges and opportunities. Our research provides insights into current applications of analytics technologies and organizationally integrates these insights for decision-makers in restaurant supply chains. The study applies decision theory as a framing perspective to this phenomenon, thereby advancing the academic literature on the interface between data management, analytical techniques, and computing. We combine data drawn from interviews of leading players in U.S. and Chinese-based restaurant chains with insights from trade publications and social media posts to identify best practices for analytics usage and supporting organizational changes. Our analysis provides examples of ways in which business leaders are applying analytics technologies to structured and unstructured data to address targeted objectives for demand/supply processes and to foster higher order organizational learning. In keeping with the stated objectives of this special issue of Production and Operations Management, this study provides an overview of both current state-of-the-art and next-generation capabilities for analytics in the restaurant industry. We further identify specific limitations of current processes, opportunities for development and theory-based research, and challenges to implementation.
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OBJECTIVES: To analyze the response in the management of both radiological emergencies and continuity of care in oncologic/fragile patients of a radiology department of Sant'Andrea Academic Hospital in Rome supported by a dedicated business analytics software during the COVID-19 pandemic. METHODS: Imaging volumes and workflows for 2019 and 2020 were analyzed. Information was collected from the hospital data warehouse and evaluated using a business analytics software, aggregated both per week and per quarter, stratified by patient service location (emergency department, inpatients, outpatients) and imaging modality. For emergency radiology subunit, radiologist workload, machine workload, and turnaround times (TATs) were also analyzed. RESULTS: Total imaging volume in 2020 decreased by 21.5% compared to that in 2019 (p < .001); CT in outpatients increased by 11.7% (p < .005). Median global TAT and median code-blue global TAT were not statistically significantly different between 2019 and 2020 and between the first and the second pandemic waves in 2020 (all p > .09). Radiologist workload decreased by 24.7% (p < .001) during the first pandemic wave in 2020 compared with the same weeks of 2019 and showed no statistically significant difference during the second pandemic wave, compared with the same weeks of 2019 (p = 0.19). CONCLUSIONS: Despite the reduction of total imaging volume due to the COVID-19 pandemic in 2020 compared to 2019, management decisions supported by a dedicated business analytics software allowed to increase the number of CT in fragile/oncologic outpatients without significantly affecting emergency radiology TATs, and emergency radiologist workload. KEY POINTS: ⢠During the COVID-19 pandemic, management decisions supported by business analytics software guaranteed efficiency of emergency and preservation of fragile/oncologic patient continuity of care. ⢠Real-time data monitoring using business analytics software is essential for appropriate management decisions in a department of radiology. ⢠Business analytics should be gradually introduced in all healthcare institutions to identify strong and weak points in workflow taking correct decisions.
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COVID-19 , Radiology Department, Hospital , Radiology , Emergency Service, Hospital , Humans , Pandemics , SoftwareABSTRACT
The use of data-driven business analytic models has had a significant impact on several sectors of the economy. In the UK, the tourism industry has contributed significantly to the economy. The contribution of tourism to the UK economy is estimated to be £145.9 billion (7.2%) of UK GDP. Regardless of its economic value, tourism is also one of the most vulnerable sectors, as it is susceptible to natural disasters, civil unrest, crisis, and pandemics, all of which can fully shut down the industry. Hence, an accurate and reliable tourism demand forecast is important. Apart from COVID-19, no other occurrence in modern history has had such a broad impact on the economy, industries, everyone and businesses in the world (Galvani et al., 2020). However, with the impact of COVID19 on the industry, it is imperative to reassess potential recovery plans for the UK economy, particularly for local tourism businesses. Macroeconomic data is collected over many source markets for the UK and a machine learning algorithm is tested to assess the future of the industry. © 2021 IEEE.
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The continuity strategy of business innovation to deal with the disruption condition, it is still many to be designed and explained. Referring to a sociotechnical systems (STSs) approach, this article conceptualizes and practically examines the business analytics, virtual business, and knowledge absorptive capacity that increase and produce the continuity capability of business innovation in uncertain business environment (e.g., COVID-19). We used SmartPLS to empirically evaluate the model based on 145 companies in a developing country of Indonesia that is currently under high disruption. Drawing a technical perspective, business analytics has successfully enhanced business continuity innovation and knowledge absorptive capacity. In a socio perspective, knowledge absorptive capacity significantly produces business continuity innovation. Business continuity innovation suffers if firms adopt virtual business without increasing their knowledge capabilities. Furthermore, mediating effects present higher support from management level to knowledge absorptive capacity strategy, a bigger impact for business analytics, and virtual business to improve and increase the goals opportunities of business continuity innovation. A novel contribution is the integration of STSs (business analytics, virtual business, and knowledge absorptive capacity) that drives business continuity innovation to deal and recover from uncertain environment. Our efforts also align several fields such as information technology-business alignment strategy, knowledge management, and knowledge-business services. Future research would be worthwhile and interesting whether our framework will suitable to different companies/respondents, business context, industry area, and crisis condition.