Abstract

Several studies have explored firm performance in the post-Covid-19 pandemic era. However, there is not much research to find reports divulging the complex relationship dynamics between business intelligence, organizational and network learning, customer value anticipation, and creative economy-based small-medium enterprises (SMEs) performance in developing countries. This study aims to uncover the complexity of those relationships. The quantitative data were collected from 313 creative economy-based SMEs in East Java, Indonesia. Using PLS-SEM, this study disclosed that business intelligence practices could not directly impact SMEs’ performance. Business intelligence will be crucial to SMEs’ performance with the support of organizational learning as a mediator. The finding also confirmed the presence of serial mediation of organizational learning and innovation in the relationship between business intelligence and SMEs’ performance. However, the role of network learning and innovation is also important, considering their relatively large direct impact on SMEs’ performance. The theoretical implications of this research broke the boundaries of strategic management theory in resource-based view and knowledge-based view in the latest era, where creative economy-based SMEs have been able to mobilize resources to carry out business intelligence to realize innovation and high performance. Further research is suggested to explore the role of business intelligence in promoting specific performance areas, such as marketing performance, financial performance, and human resource management. In addition, it is advisable to choose more specific research subjects, including those in the culinary subsector, and pay attention to other areas, e.g., the demographics of respondents in the model as a control variable.

Introduction

The performance of small-medium enterprises (SMEs) in the post-pandemic era of Covid-19 has been in the spotlight of many scientists, e.g. Ref. [1] highlighted the declining performance of 273 SMEs at the company level operating in commercial, manufacturing, service, or construction industries in certain geographical areas in Finland due to a competitive mindset contaminated by high-performance growth aspirations [2], examined the impact of identity-based entrepreneurial passion on the performance of 193 established SMEs in Ghana [3], identified the mediating role of green product innovation between green entrepreneurial orientation, green transformational leadership, and the performance of 384 manufacturing SMEs operating in Amman, Jordan [4], discussed the export performance of 329 SMEs in Vietnam and how they are developing their internal skills to face external challenges arising from the digitalization process and the COVID-19 crisis [5], analyzed the role of learning in an organization while measuring and managing the sustainable performance of 710 manufacturing sector SMEs operating in Laos [6], examined the impact of international resources on the sustainable performance of 380 Pakistani SMEs through the mediating role of green entrepreneurial orientation [7], tested the influence of organizational creativity and open innovation on the performance of 206 SMEs spread across several regions in Indonesia., and [8] explored how 247 Vietnamese SMEs can leverage information technology (IT) to overcome crises, exploit innovative opportunities, adapt to changing market conditions, and drive new competitive initiatives. This is due to the prominent role of creative economy-based SMEs as the drivers of economic growth in Indonesia and other countries [9]. East Java is no exception; one of the regions contributing 14.92% of Indonesia’s national gross domestic product (GDP) [10]. This province has much potential in the creative economy sector, which is supported by 113 startups [11,12] to face the turbulent business environment as a result of accelerating digital transformation [13] during the Covid-19 pandemic. The number of creative economy-based SMEs will continue to grow, even from the activities of homemakers who are familiar with digital literacy and the ease of doing business today [14].

Conclusion

This research has attempted to investigate the link between business intelligence, organizational learning, network learning, customer value anticipation (CVA), innovation, and creative economy-based SMEs performance in East Java of Indonesia. The findings indicate that business intelligence does not directly impact SMEs’ performance because the data generated from business intelligence practices needs to be integrated into knowledge that must be learned through organizational learning. If this is offset by CVA, it will give birth to innovations that ultimately impact SMEs’ performance. Hence, even though the impact of serial mediation network learning and innovation presence on the relationship between business intelligence and performance is not supported by field data in this study, network learning still need attention to improve SMEs’ performance. The theoretical implications of this research break the boundaries of strategic management theory resource-based view (RBV) and knowledge-based view (KBV) in the latest era, where creative economy-based SMEs in developing countries have been able to mobilize resources to carry out business intelligence, even though in a simple way, to realize Innovation and high performance.

The limitation of this research is that it has yet to explore the causes of the insignificant effect of network learning on innovation. Thus, future research can conduct qualitative studies to obtain more in-depth information regarding the relationship between network learning with Innovation. In addition, this research still uses economy creative-based SMEs as samples, while future research is recommended to involve specific samples, for example, in the culinary sector only, the sector with the highest number in East Java. Researchers can also pay attention to other provinces, such as West Java, which has the largest creative economy exports in Indonesia. Further scholars are also expected to explore the role of business intelligence in more specific performance areas, such as marketing performance, financial performance, and human resource management, and include the demographics of respondents in the model as a control variable. The data distribution in PLS-SEM analysis is only possible to assess statistical significance using the Bootstrapping technique.

Abstract

As big data becomes an apparent challenge to handle when building a business intelligence (BI) system, there is a motivation to handle this challenging issue in higher education institutions (HEIs). Monitoring quality in HEIs encompasses handling huge amounts of data coming from different sources. This paper reviews big data and analyses the cases from the literature regarding quality assurance (QA) in HEIs. It also outlines a framework that can address the big data challenge in HEIs to handle QA monitoring using BI dashboards and a prototype dashboard is presented in this paper. The dashboard was developed using a utilisation tool to monitor QA in HEIs to provide visual representations of big data. The prototype dashboard enables stakeholders to monitor compliance with QA standards while addressing the big data challenge associated with the substantial volume of data managed by HEIs’ QA systems. This paper also outlines how the developed system integrates big data from social media into the monitoring dashboard.

Introduction

Quality assurance (QA) in higher education (HE) is emerging as the higher education institutions (HEIs) find it difficult to monitor their levels of quality of services. Governments impose national QA standards in order to achieve the minimum level of quality of services provided by these institutions. In the Kingdom of Saudi Arabia (KSA), Education and Training Evaluation Commission (ETEC) imposes, through its National Centre for Academic Accreditation and Assessment (NCAAA), the QA standards that all HEIs in the KSA are required to comply with for accreditation [1]. To measure compliance with these standards, in 2018, NCAAA developed 23 key performance indicators (KPIs). In 2022, NCAAA introduced a revised version of KPIs. This revised version comprises a total of 17 KPIs, and all HEIs are mandated to monitor and report on compliance. This evaluation process of compliance with these KPIs should take place during the forthcoming accreditation cycle for HEIs [2].

In addition, the increasing amount of data generated from traditional sources and through social media plays negative influence on business intelligence (BI). There is a desire to build low-cost data platforms that will be able to handle this increasing amount of data. This is sometimes known as the big data challenge [3,4].

In this paper, different BI architectures are presented. These BI architectures encompass different BI technologies that HEIs may adopt, taking into consideration their capabilities and specific requirements of HEIs. A proposed architecture is also presented to show how the big data challenge can be addressed to deal with huge amounts of data that the QA system might require particularly if the data is being linked to social media. The recent trend of measuring students’ satisfaction is through students’ comments posted on social media.

Conclusion

This paper showed how the HF-HEQ-BI framework can be applied to develop a BI prototype dashboard for monitoring quality in HEIs and a dashboard has been developed from the framework. The paper has also outlined different BI architectures that HEIs may adopt to handle the big data challenge while monitoring QA. These architectures show different configurations of BI systems that HEIs may adopt based on their requirements and available resources. The development process of the BI dashboard was discussed to show how HF-HEQ-BI FUT may be used to determine the requirements to be presented in the dashboard. HF-HEQ-BI FUT assists in determining the required visualisation for each KPI to be displayed in the dashboard. FUT is customisable and can be adapted by users to reflect the requirements of individual HEI. FUT shows how decision-makers in HEIs may select various visualisation dashboards while monitoring QA compliance regarding NCCAA standards.

A prototype dashboard was developed to illustrate the application of the framework and also to evaluate the HF-HEQ-BI framework. The developed dashboard shows a monitoring display of the mandatory NCAAA KPIs, institutional specific KPIs, additional KPIs, and social media analytics. The social media analytics presented in the dashboard included the sentiment analysis of data drawn from Twitter through Twitter API. Additionally, the proposed dashboard allows the dashboard users to represent additional KPIs related to performance monitoring in the institutions.

The developed dashboard based on the HF-HEQ-BI framework shows how data retrieved from social media is presented in summarised diagrams to handle the big data challenge. The prototype dashboard is dynamic and KPIs can be added or modified easily based on the changes in regulations or institutional directions of the strategic plans. Our future work will focus on the evaluation of the proposed dashboard to meet the big data challenge.

Abstract

The rise of information technology has transformed the business landscape, with organizations increasingly relying on information systems to collect and store vast amounts of data. To stay competitive, businesses must harness this data to make informed decisions that optimize their actions in response to the market. Business intelligence (BI) is an approach that enables organizations to leverage data-driven insights for better decision-making, but implementing BI comes with its own set of challenges. Accordingly, understanding the key factors that contribute to successful implementation is crucial.

This study examines the factors affecting the implementation of BI projects by analyzing the interactions between these factors using system dynamics modeling. The research draws on interviews with five BI experts and a review of the background literature to identify effective implementation strategies. Specifically, the study compares traditional and self-service implementation approaches and simulates their respective impacts on organizational acceptance of BI. The results show that the two approaches were equally effective in generating organizational acceptance until the twenty-fifth month of implementation, after which the self-service strategy generated significantly higher levels of acceptance than the traditional strategy. In fact, after 60 months, the self-service approach was associated with a 30% increase in organizational acceptance over the traditional approach. The paper also provides recommendations for increasing the acceptance of BI in both implementation strategies. Overall, this study underscores the importance of identifying and addressing key factors that impact BI implementation success, offering practical guidance to organizations seeking to leverage the power of BI in today’s competitive business environment.

Introduction

The speed of data generation and accumulation has increased with the increasing use of information technology solutions by organizations, which are now using digital tools to store and analyze vast amounts of data in real-time [28,[37], [63]]. Companies and organizations can gain a comparative advantage and overtake their competitors through the analysis and continuous and effective use of data and information [12,30,68].

Business intelligence is one of the popular and welcomed solutions for organizations to use data analysis for decision-making and data-oriented business [67,74]. For example, a retail company can use business intelligence to monitor customer behavior and preferences and adjust its marketing strategy accordingly [48]. Likewise, a manufacturing company can use BI to optimize its supply chain, reduce production costs, and improve product quality [27]. This solution offers managers and experts of organizations the possibility of making smart and updating analyzes and decisions. A business intelligence system is usually defined as a set of technological solutions [12] that facilitate organizations to collect, integrate, and analyze large data stores in order to understand their opportunities, strengths, and weaknesses [23].

Conclusion and outlook of research

The purpose of this research was to investigate the impact of business intelligence system implementation strategies in IT organizations on organizational acceptance. The study identified effective factors in the implementation of business intelligence systems through expert consultation and created a dynamic model to simulate the self-service and traditional approaches. The results showed that the self-service strategy led to higher levels of organizational acceptance compared to the traditional approach, with a difference of 30%.

To validate the initial results, three scenarios were proposed by experts: acquiring domain knowledge, increasing cooperation with the requesting team, and developing soft skills in the development team. These scenarios showed that the traditional approach can become more competitive and gain more organizational acceptance by addressing the lack of domain knowledge, improving cooperation with the requesting team, and developing soft skills in the development team. Each scenario resulted in an increase in organizational acceptance by 17–25% compared to the initial state of the traditional strategy.

Abstract

     This study aims at investigating the mediating impact of business intelligence, innovation speed and innovation quality on the relationship between the different components of intellectual capital and competitive advantage in the context of Jordanian companies. To achieve this, the study developed a theoretical framework that depicts the mediation through data that were collected randomly, from the pharmaceutical and medical supplies companies, with a total sample of 569 participants. Data were analyzed using structural equation modeling, SEM (CB-SEM), for model fit and Process software for path analysis to estimate the different indirect effects. The findings provide evidence that all mediators serve differently as important mediating mechanisms between the different components of intellectual capital and competitive advantage. Overall, the results of this study show and clarify the mediating mechanism of business intelligence, innovation speed and innovation quality, as mediators through which human capital, structural capital and relational capital improve competitive advantage in the context of Jordanian companies.

Introduction

     In the field of business, knowledge has become very important in the success of organizations and their contribution to transformation into a knowledge economy, where most of knowledge is implicit and is available in the minds of individuals, and depends on their intuition, intellectual experience and skills. It is also available in the form of meaningful information about the market, the beneficiary, communications and technology. In fact, knowledge can be measured and plays a crucial role in achieving competitive advantage [1]. Competitive advantage is defined as a distinct skill, technology or resource that enables the organization to provide a service, commodity or benefits distinct from what competitors provide within the same sector, which confirms the distinction and difference of the organization from its competitors from the point of view of customers [2]. However, Dynamic Capabilities, sustainable competitive advantage, innovations and market responsiveness are all sources of competitive advantage. Dynamic Capabilities are the organizational and strategic routines by which firms achieve new resource configurations as markets emerge, collide, split, evolve and die [3]. An innovation is defined as an idea, a product or process, or a system that is perceived to be new to an individual [4]. Sustainable competitive advantage is defined as a strategy that cannot be easily imitated by the competitors of the resources owned and used by a company and thus creates value [5]. Market responsiveness describes how timely the organization is in responding to market changes [6]. In light of this, the methods of developing performance in organizations have changed, after they were focused on the organizational or material side of the administrative process, which was losing its spirit or human nature, and leading to a feeling of frustration for workers and beneficiaries. These methods became focused on the human aspect of the administrative process in its practical sense, including the elements of effective leadership, empowering workers and attention to intellectual capital [7].

Discussion, Conclusions and Recommendations

     Based on the results obtained, the study found that the impact of structural capital on competitive advantage via the mediation of business intelligence is insignificant, and the impact of relational capital on competitive advantage via the mediation of business intelligence is negatively significant, while the impact of human capital on competitive advantage via the mediation of business intelligence is positively significant. These findings together prove the existence of the mediation effect of business intelligence. This role of business intelligence is supported by [25], who showed that the various types of business intelligence are used for competitive advantage. In addition, business intelligence is involved in various other relations linked to competitive advantage as shown by [6]. However, in the current study, this is only clearly shown between human capital and competitive advantage. This result clarifies the mediating mechanism of business intelligence through which human capital improves competitive advantage in the pharmaceutical and medical supply companies in the Jordanian context. This means that pharmaceutical and medical supply companies in Jordan are effectively taking advantage of business intelligence by which human capital improves competitive advantage

     Having said this, the insignificant impact of structural capital and the negative impact of relational capital on competitive advantage through business intelligence could be attributed to either a low level of both structural and relational capital or the absence of such effect at all. These findings indicate that merely emphasizing structural capital and relational capital may not guarantee the expected competitive advantage. However, as none of the researchers have examined such mediation of the business intelligence relationship, especially at the individual level of the different components of intellectual capital, our findings can be partially supported by [6,25,33,42–44] only in the impact of Business intelligence on competitive advantage.

Abstract

     The application of Business Intelligence systems can be seen as a business strategy and development, which integrates a comprehensive set of services to provide relevant corporate information in strategic and operational decision-making, and to increase the corporation’s competitiveness. For the successful implementation of a Business Intelligence System in an organization, it is necessary to use well-defined processes and business rules. The purpose of this article is to present and explore a set of tools and practices for extracting and exploiting Big Data sources. The result of this research approach aims to define a set of indicators and dashboards to improve the organization’s business management and intelligence.

Introduction

     rtually available to everyone, anytime and from anywhere. Thus, with globalization contributing to increased competitiveness, organizations are forced to constantly change, to manage a large amount of data generated daily and which are essential to support decision-making. There is also a need to consider the competitiveness between organizations, where the awareness that data represent an essential source for the production of necessary information is becoming ever greater [19][6][7]. Computer applications have allowed

     Computer applications have allowed organizations to have quality control over data, generate relevant indicators to be provided to managers about the organizations business, preparing them to design forecast scenarios more effectively and efficiently. Consequently, organizations are becoming increasingly dependent on the use of Business Intelligence (BI) applications to extract, process and organize the necessary data.

Discussion and Conclusion

     After characterizing the various phases of the work, some conclusions and results can be considered. The first coincides with the importance of knowledge of data for organizations. Data stored in different sources must be extracted and explored for a platform such as Data Warehouses and associated with BI systems, to suit users and the organization, to improve the quality of information, and obtain the knowledge necessary for decision making.

     In this sense, the importance of reviewing the literature must be recognized, allowing the study and characterization of platforms, applications and systems, for the process of extracting and exploring data knowledge for BI.

     The research methodology phase contextualizes the development structure of the aligned work, with the objective to be achieved, through the creation of the artifact, from the extraction and exploration of relevant data that serve the BI system. The DSR methodology allows finding an acceptable solution to the problem. After analysing the problem and in accordance with this methodology, the solution found was the creation of a cataloguing typology, which makes it possible to identify how and which data extraction and exploration tools are best suited to the users’ requirements.

Abstract

     This study aims to examine the factors that influence business intelligence and analytics (BIA) usage in the banking sector. Based on a comprehensive literature review, a theoretical model was developed to explore the impact of three key factors on business intelligence and analytics adoption and usage in the banking sector, namely technological, organizational, and environmental factors. The study used the Statistical Package for the Social Sciences (SPSS) to analyze data collected from 120 employees of Jordan Arab bank. The results revealed the critical impact of not only the existence of data and technology infrastructure but also the importance and availability of management and human resources support and capabilities. This study suggests that, more importantly, successful planning for business intelligence and analytics should go beyond the technology aspects to gain the full benefits of such technology, especially in the banking sector. Yet, we argue that more research needs to be conducted, especially in the context of developing countries, to fully understand how banking sectors can successfully implement and utilize business intelligence and analytics.

Introduction

     Business intelligence and analytics (BIA) is considered one of the most critical technologies, systems, practices, and applications that help organizations develop a deeper understanding of business data and gain a competitive advantage while improving operations and product development and strengthening relationships with customers [1,2]. BIA has an even more important role in the banking sector by enabling experts and managers to make better, accurate, timely, and relevant decisions so as to increase the productivity and profitability of the bank and be able to comply with the different regulatory and environmental dimensions of this sector [3].

     BIA, nowadays, is a trendy issue and a compulsory prerequisite for creating an outstanding corporate image, which goes in line with implementing a successful plan regarding using technology extensively. Thus, this supports business decisions and gains a competitive advantage in today’s dynamic environment, which requires outstanding efforts for dedicating massive budgets to research and development (R&D). Data are a focal point and are considered the fuel of the future since they can be processed efficiently and used effectively in supporting risky occurrences and decisions that can be heavily reflected in the performance of corporates [4,5].

     Business intelligence (BI) is an umbrella term that includes structures, tools, databases, applications, and methodologies to analyze data by converting raw data into meaningful and helpful information to support business managers’ decisions [6]. Banking areas, such as branch performance, sales, risk assessment, electronic banking, customer segmentation, and retention, are generally excellent for applying various business concepts and analytics, technologies, and tools, including data mining (DM), data warehousing, and decision support systems (DSS). Therefore, top management must constantly focus on solving challenging problems and seizing opportunities for the banking sector to succeed and excel in today’s business environment. This requires computerized support for managerial decision making, thereby implying a need for decision support, business intelligence, and analytics systems [7]. Business intelligence systems (BIS) emerged from technical solutions that provide data integration, analytical capabilities, and data mining to provide stakeholders at various levels with valuable information to make their decisions effectively and successfully.

Conclusions

     Business intelligence and analytics is a key tool that deals with large amounts of diverse data, whether structured or unstructured [2], thus providing banks with the huge value of achieving better operations and great competitive advantages. With the dynamic environment we are facing nowadays, every bank should emphasize the importance of implementing a massive business-intelligence system to keep up with the fast pace of technology. In this study, we studied the factors that influence the use of business intelligence and analytics in the banking sector. Different factors have been examined, including technical, organizational, and environmental dimensions. The results of the analysis confirmed not only the importance of these factors on business intelligence and analytics adoption and use but also on the huge value for the banking sector that can be achieved by such technology. In fact, it is extremely important to understand the dynamics of these factors, not only to achieve better usage of business intelligence, but also to better plan and implement such technologies in the banking sector.

     The results presented in this study reveal that to achieve better fact-based decision making, business-intelligence technologies require the presence of quality-level data-related infrastructure capabilities, great senior-management support and better “market environment” awareness. Yet, the researchers believe that more factors with more comprehensive and larger populations need to be studied in order to fully understand the critical success factor of BIA usage and utilization. That is, further research can test the proposed model in different contexts, such as the Jordanian public-shareholding firms as they invest heavily in BIA systems, and, in turn, more generalizable results can be obtained. In addition, future research could apply the model to developed countries to compare the results with those of developing counties. Finally, the researchers examined the impact of BIA on firm performance and innovation capabilities, but more research is needed to examine the BIA adoption on organizational effectiveness in a holistic view. To conclude, the results of this study are of supreme implication to the literature in this research area and can motivate additional crucial studies to shed more light on their standings.

Abstract

1 Background

2 COVID 19 and Businesses in the UAE

3 Literature Review

4 Hypotheses Development

5 Method and Measurements

6 Results

7 Conclusions

References

Abstract

     The current study aims at empirically investigating the impact of the business intelligence on the new start-up performance of UAE during Covid-19. The study also examines the mediating role of innovativeness in the relationship between business intelligence and new start-up performance in UAE. The sample size included distributing the questionnaires to 250 respondents to get the required information for further analyses. 210 questionnaires out of 250 were received, so the response rate of the study was 84%. The data analysis involved the path modeling technique because of the explorative nature of the study. The results indicated that all the paths are significant at a p-value of less than 0.05. The findings of the study will be helpful for policymakers and researchers in formulating the policy concerning the business intelligence, innovation, and start-up performance in UAE

Background

     No term has been widely accepted by scholars to refer to the external and internal intelligence, which are deemed as essential in business decision-making. The Business Intelligence (BI) is considered an umbrella term that covers processes and technologies required for handling information that may enhance the business decisionmaking [1]. Therefore, business intelligence is a product as well as a process, which comprises those methods that are used by organizations for intelligence and to generate useful information that may help firms thrive and survive in the marketplace. As a product, it serves as information that enables firms to anticipate the behavior of firms’ suppliers, customers, competitors, services, products, business environment. and technologies with greater certainty.

     The Business intelligence (BI) has gained considerable attention due to its increased accessibility to the information through electronic means. In this process, information is acquired, processed, and presented in a useful form and also forms the basis for organizations’ intelligence practices. In addition, the information usage is required to be improved, particularly in the context of the great social and political change taking place around the globe, the rapid technological change, and the increase in the cut-throat competition [2]. However, the increased uncertainty among firms has given rise to the information processing activities [3, 4], which may otherwise jeopardize the firms’ survival in the market. Generally, the startups strive to attain a certain position in the market, which is also essential for them to grow and survive. It is noteworthy that small firms are not exactly a smaller version of large firms since both have different structures, management practices, ran an source availability, the competition level and the environmental response in the market they compete [5]. Thus under a highly dynamic, volatile, and competitive business environment, the information must be gathered by firms for improved decision-making. Although it may seem challenging, particularly for the startup firms who are struggling to survive in the market, this information gathering process facilitates the firms’ managers to effectively align with their environment and improve the performance of their organization [6, 7]. In addition, firms can use business intelligence (BI) for information gathering and simultaneously contribute to expanding the available pool of knowledge for the managers. Thus, BI can be deemed as important assets for the organizations [8].

Conclusions

     The results of the study are in line with the Resource-Based View (RBV). According to the RBV Theory, organizations must utilize their human, organizational, and physical assets (intangible & tangible) to develop and sustain a competitive advantage. This theory postulates that firms that possess rare and valuable resources are capable of developing a competitive advantage [20], particularly if their resources cannot be easily substituted or imitated. Correspondingly, in terms of knowledge-based view (KBV), knowledge is viewed as a company’s most valuable resource [38]. The RBV theory provides a theoretical foundation for the Knowledge-based view (KBV) by asserting that a firm’s primary factor of production is its knowledge, which helps firms in gaining a competitive advantage. This study was based on the sample size of 250 respondents, whereby only 210 questionnaires out of 250 were received, so the response rate of this study was 84%. For the data analysis, the path modeling technique was required because of the explorative nature of the study. The results indicate that all the paths are significant at a p-value of less than 0.05.

With the aim of bridging the gap between the firm’s internationalization speed research and the emerging study of business intelligence (BI), this study draws on knowledge transformation as the theoretical lens for exploring how business intelligence leverages organizational agility to promote the speed of internationalization. By analyzing data collected from 258 Chinese firms in the Yangtze River Delta area, we conclude that: (1) Business intelligence has a significant influence on the speed of internationalization, and the organizational agility positively mediates such causal relationship. (2) Cultural distance negatively moderates the relation between organizational agility and speed of internationalization. The managerial implications of these findings and future research directions regarding the firm’s internationalization speed are discussed.

Introduction

With the intensified international competition, the accelerating internationalization has been becoming an increasingly important route to competitive advantage for international companies (Hilmersson & Johanson, 2016; Lin & Si, 2019; Tan & Mathews, 2015). firms eagerly seeking to participate in international business are on the rise (Johanson & Kalinic, 2016), with the expectation that a faster internationalization strategy will facilitate them to seize fresh opportunities, enter into potentially global niches, and build first-mover advantages (Acedo & Jones, 2007; Jiang, Beamish, & Makino, 2014; Vermeulen & Barkema, 2002). The firm’s speed of internationalization, a key aspect of international strategic decision-making, has become an important issue for expanding international markets (Chetty, Johanson, & Martín, 2014; Coviello & Cox, 2006; Prashantham & Young, 2011). It is particularly relevant for Chinese companies, which usually have strong motivations to catch up with competitors from developed markets as quickly as possible (Cheng & Yang, 2017; Deng, 2012). Recently, the internationalization speed literature has increasingly shifted focus onto the relationship between a firm’s internationalization speed and its international performance (García-García, García-Canal, & Guillén, 2017; Jain, Celo, & Kumar, 2019; Sea-Jin & Jay Hyuk, 2011). These emerging studies confirm the importance of experiential knowledge that promotes the firm’s speed of internationalization by extracting such knowledge from embodied experience, such as operational experience and trust-building (Johanson & Vahlne, 1977, 2009).

1- Introduction

2- Literature review

3- Research context and the methodology

4- Findings

5- Discussion

References

Abstract

Firms continuously report increased competitive value gains from the use of business intelligence and analytics (BI&A), however, little is known about how insights from BI&A are transformed to added value to date. We have conducted fourteen in-depth, semi-structured interviews with a sample of informants in CEO positions, IT managers, CIO, Heads of R&D, as well as Market Managers from nine medium or large-sized European firms. Applying the absorptive capacity’s theoretical lens, we have provided evidence that absorptive capacity’s capabilities are an underlying foundation in the process of transforming BI&A triggered insights into valuable knowledge. Moreover, this process is supported by technological, human, and relationship assets.

Introduction

The amount of data and information generated on a daily basis continuously increases, forcing firms to increasingly rely on external knowledge and information to enhance firm innovation and performance (Benner & Tushman, 2015; Ireland, Hitt, & Vaidyanath, 2002). With the quick development of computer intelligence as well as the appearance of “big data” concept, business intelligence and analytics has become an increasingly important concept for researchers and practitioners (Chen, Chiang, & Storey, 2012). Although BI&A were initially used for decision-making support activities, they have been increasingly considered for organizational learning and adjustments, improving operational efficiency, and strengthening organizational intelligence (Trieu, 2017). A survey conducted by IBM Institute for Business Value and MIT Sloan Management Review reported that firms are increasingly gaining competitive advantage from analytics (58% of the more than 4500 respondents reported competitive value gains from analytics) (Kiron & Shockley, 2011). Not surprisingly, Gartner’s survey on IT Spending found BI&A to be a top priority for most of the analyzed firms and predicted that BI&A would remain one of the top foci for the leading firms (Gartner, 2013). On the other hand, firms have acknowledged the potential of BI&A to generate insights and knowledge from both external and internal sources of knowledge (Shehzad, Khan, & Naeem, 2013; Wang, 2014; Wixom, Watson, & Werner, 2011; Yeoh & Koronios, 2010). As the complexity of the data is increasing, humans have difficulties interpreting the external information due to limited mental capacities (Jansen, Van Den Bosch, & Volberda, 2005; Sammut & Sartawi, 2012). More information is not necessarily beneficial for the organization since its information and knowledge processing capacity is limited as well (Simsek, 2009). As a result, organizations develop information filters and routines to cope with bounded rationality (March, 1978; Nelson & Winter, 1982). BI&A have found it possible to expand the human mental capacity as well as the firm’s absorptive capacity by increasing the ability of individuals and firms to receive, store, analyze and transfer information with fewer errors (Brynjolfsson & Hitt, 2000; Elbashir, Collier, Sutton, Davern, & Leech, 2013; Simon, 1991). While various streams of studies have provided research on the BI&A potential, there has been little attention given to the improvement of understanding the role of BI&A in the process of knowledge generation from external data and with it, the underlying mechanisms that facilitate this process.

I- Introduction

II- Related Work

III- Methodology for Data Warehouse Design

IV- Telecommunication DW Case Study

V- Implementation

VI- Discussion

References

Abstract

In a cloud based data warehouse (DW), business users can access and query data from multiple sources and geographically distributed places. Business analysts and decision makers are counting on DWs especially for data analysis and reporting. Temporal and spatial data are two factors that affect seriously decision-making and marketing strategies and many applications require modelling and special treatment of these kinds of data since they cannot be treated efficiently within a conventional multidimensional database. One main application domain of spatiotemporal data warehousing is telecommunication industry, which is rapidly dominated by massive volume of data. In this paper, a DW schema modelling approach is proposed which integrate in a unified manner temporal and spatial data in a general data warehousing framework. Temporal and spatial data integration becomes more important as the volume and sharing of data grows. The aim of this research work is to facilitate the understanding, querying and management of spatiotemporal data for on-line analytical processing (OLAP). The proposed new spatiotemporal DW schema extends OLAP queries for supporting spatial and temporal queries. A case study is developed and implemented for the telecommunication industry.

INTRODUCTION

An increasing number of Cloud Computing (CC) platforms provide facilities for big Data Warehouse (DW) storage and manipulation. Having all the DW functionalities over the Internet simplifies the access on it and storage is no longer an issue since clouds offer almost limitless storage capacity. The Apache Hive Data Warehouse [1] manages large distributed data sets using SQL, while Microsoft with Azure SQL Data Warehouse [2] can fully manage a cloud DW providing a single holistic DW solution. Amazon offers also cloud DW capabilities over Amazon Redshift Cluster using standard SQL [3]. Google isn’t out of this with Google BiqQuerry to antagonize the other big vendors [4]. Nowadays, almost all big and smaller cloud providers like IMB [5], Oracle [6], Teradata [7], CoolaData [8] etc. have already include DW services in their cloud environments. Business intelligence (BI) is a technology-driven process for the collection, integration, analysis, and presentation of business information. It includes a wide variety of tools, applications and methodologies that permit organizations to collect data from internal and external sources for analysis and decision-making. A component of BI is online analytical processing (OLAP). OLAP creates a multidimensional view of data for the user to do the analysis. The approach for OLAP is classified into three categories, MOLAP, ROLAP and HOLAP. In MOLAP (multi-dimensional online analytical processing) the data used for analysis is stored in specialized multidimensional databases. ROLAP works directly with relational databases. HOLAP approach is a hybrid OLAP approach which combines MOLAP with ROLAP by allowing the designer to decide which portion of the data will be stored in MOLAP and which portion in ROLAP. BI data is typically stored in a DW. The purpose of data warehousing is to construct a huge repository of integrated data, which is optimized for analysis purposes. Nowadays, the big data challenge moves the tradition DWs to cloud based DWs with limitless storage resources and internet based secure access from anywhere to everywhere.

Business intelligence and analytics (BI&A) technologies facilitate data collection, analysis and information delivery and are designed to support decision-making. Given that management accounting (MA) is a decision-supporting activity, there is an obvious link between BI&A and management accounting (AICPA, 2013; Cokins, 2009; Maisel and Cokins, 2014). This link is important, as BI& A is high on the corporate agenda of many organizations (Colombus, 2014; Colombus, 2015; Research and Markets, 2015; PwC, 2016), and executives believe that better data analysis and decision support create value for their companies (Elbashir et al., 2013; Kiron et al., 2014). Thus, management accounting has much to gain from successfully integrating BI&A techniques into managerial accounting tasks. This interest is also reflected in the professional accounting bodies’ agendas, so much so that in 2016, the Chartered Institute of Management Accountants (CIMA) called for research on ‘analytics’ by offering grants to undertake studies in this area (CIMA, 2016b). Given the current business focus on BI&A and its perceived management importance, this solution domain should be an area of focus of AIS research. However, commentators have noted that although “there is potential for studying business intelligence solutions in general and their implications for decision making and control, our current understanding of these developments in the accounting academia is very limited” (Granlund, 2011, p. 10). To assess whether these reservations are valid, an extensive literature search is carried out in over sixty accounting and information system journals. Literature reviews are essential, as they save time and effort for future researchers and lead to the efficient advancement of new knowledge and of the discipline (Webster and Watson, 2002). The specific aims of this review are twofold: 1. Critically review and evaluate the volume and content of literature that has focused on the relationship between BI&A and management accounting. 2. Based on the review, highlight research gaps and opportunities for future research. This paper is structured as follows. Section 2 conceptualizes the link between MA and BI&A. This is necessary, as there are numerous definitions of BI&A in use and there is some confusion regarding how this technology is linked to other emerging technologies such as big data, machine learning and the Internet-of-things. Section 3 explains the two-staged literature review, initially providing a review of the recent themes in the IS literature and then giving an overview of the articles selected for the second review on the link between management accounting and BI&A. Section 4 contains the review and the findings, organized into 5 themes identified during the analysis of the articles and a summary of research questions to be explored in the future. Section 5 concludes the paper.

Expert Systems (ES) are a core element of human decision making. Initially, in the 1970s and 1980s, ES were focused on extracting explicit knowledge from human experts. With the availability of big data, after the 2000s, ES incorporated data-driven models, thus being associated with business intelligence, big data, data science, and machine learning systems (Cortez & Santos, 2017). The importance of data-driven models in the ES area is confirmed by the recent Wiley’s Expert Systems (EXSY) literature survey that analyzed all journal research articles published from 2000 to 2016 (Cortez et al., 2018). The survey revealed data-driven as the most prevalent ES method type, corresponding to around 35% of all recently published EXSY papers. The first “Knowledge Discovery and Business Intelligence” (KDBI) track was held at the EPIA conference onArtificial Intelligence in 2009, with the goal of strengthening the interaction between Knowledge Discovery (KD) and Business Intelligence (BI). Both are important data related topics. KD is the subfield of Artificial Intelligence that is focused on the extraction of human interesting knowledge from raw data (Fayyad et al., 1996). BI is an umbrella term that encompasses methodologies and technologies (e.g., data warehousing and dashboards) to support managerial decision making (Delen et al., 2014). Following the success of its first 2009 edition, the KDBI workshop become a regular track of the biannual EPIA conference, with its fifth edition taking place at the 18th EPIA conference, held at Porto, Portugal, in September 2017. Since 2011, the track had a dedicated special EXSY issue (Cortez & Santos, 2013, Cortez & Santos, 2015, Cortez & Santos, 2017). This is the fourth special issue on Knowledge Discovery and Business Intelligence, and it includes extended versions of the best papers presented at the 5th KDBI thematic track of EPIA 2017, which received 20 paper submissions. The EXSY special issue included two rounds of reviews, which involved reviewers from the 5th KDBI track of EPIA2017 and also the EXSY journal. After the revision stage, a total of five papers were accepted to be published in this issue, which corresponds to an acceptance rate of 25%. While in the last decade there have been remarkable developments in the KD and BI areas, there are still challenges and opportunities. For instance, most KDBI research has been focused on processing structured data but little has been devoted to Mining Software Repositories (MSR), which is quite valuable for the software engineering industry. Also, feature selection is a challenging task in the context of large genomics datasets. Some domain applications, such as food truck recommendation, produce difficult multi-label classification tasks. Another challenge comes from the development of online recommendation systems, which are relevant for high velocity data and big data streams. Moreover, imbalanced domains are important in several real-world applications, such as medicine and finance, often prejudicing the predictive performance of the data-driven models. These challenges are approached in the five accepted papers published here. In the next section, we summarize the main contributions of these papers.