• The Journal of Asian Finance, Economics and Business
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• v.10 no.2
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• pp.15-27
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• 2023

The purpose of this research is to investigate the impact of Business Intelligence (BI) on the relation between Big Data Analytics (BDA) and Financial Performance (FP), at the beginning we reviewed the academic accounting and finance literature to develop the theoretical framework of business intelligence, big data and financial performance in terms of definition, motivations and theories, then we conduct an empirical analysis based on questionnaire-base survey data collected. The researchers identified the study population in the joint-stock companies listed on the Egyptian Stock Exchange and operating in the sectors and activities related to modern technologies in information systems, big data analytics, and business intelligence, in addition to the auditing offices that review the financial reports of these companies, and The sector closest to the research objective is the communications, media, and information technology sector, where the survey list was distributed among the sample companies with (15) lists for each company, and (15) lists for each audit office, so that the total sample becomes (120) individuals (with a response rate 83.3%), The results show, First, Big data analytics significantly affect organizations' financial performance, second, Business intelligence mediates (partial) the relationship between big data analytics and financial performance.

• Educational Technology International
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• v.17 no.2
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• pp.175-202
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• 2016

Most of the learning analytics research has investigated how quantitative data can affect learning. The information that is provided to learners has been determined by teachers and researchers based on reviews of the previous literature. However, there have been few studies on standard learning activities that are performed in e-learning environments independent of the teaching methods or on learning behavior data that are obtained through learning analytics. This study aims to explore the general learning activities and learning behaviors that can be used in the analysis of learning data. Learning activities and learning behavior are defined in conjunction with the concept of learning analytics to identify the differences between teachers' and learners' learning activities. Learning activities and learning behavior were verified by an expert panel review in an e-learning environment. The differences between instructors and learners in their usage were analyzed using a survey method. As results, 8 learning activities and 29 learning behaviors were validated. The Research has shown that instructors' degree of utilization is higher than that of the learners.

• Journal of Distribution Science
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• v.21 no.1
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• pp.53-63
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• 2023

Purpose: Big Data analytics (BDA) has been recognized to improve firm performance because it can efficiently manage and process large-scale, wide variety, and complex data structures. This study examines the determinants of Big Data analytics adoption toward marketing and financial performance of global logistic companies in Thailand. The research framework is adopted from the technology-organization-environment (TOE) model, including technological factors (relative advantages), organizational factors (technological infrastructure and absorptive capability), environmental factors (industry competition and government support), Big Data analytics adoption, marketing performance, and financial performance. Research design, data, and methodology: A quantitative method is applied by distributing the survey to 450 employees at the manager's level and above. The sampling methods include judgmental, stratified random, and convenience sampling. The data were analyzed by Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM). Results: The results showed that all factors significantly influence Big Data analytics adoption, except technological infrastructure. In addition, Big Data analytics adoption significantly influences marketing and financial performance. Conversely, marketing performance has no significant influence on financial performance. Conclusions: The findings of this study can contribute to the strategic improvement of firm performance through Big Data analytics adoption in the logistics, distribution, and supply chain industries.

• Journal of Information Technology Applications and Management
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• v.22 no.1
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• pp.37-60
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• 2015

As the dependence on information systems in enterprises has grown dramatically, the importance of implementing information systems in audit has been increased as well. However, there is a lact of about utilization of information system for audit process. Thus, this study is to investigate the factors that effect auditor's adopting Data Analytics to audit work. Through literature research and focus group interview, we added two factors that affect the behavioral intention to UTAUT model. We have selected performance expectancy, effort expectancy, social influence, facilitating conditions, anxiety, task fit, behavioral intention as variables and verified hypotheses based on survey questionnaires from auditors. As a result, it was found that performance expectations, social influence, task fit influenced the behavior intention. In Addition, we analyzed adding two variables, IT-related work experience and type of auditor as moderate variable. This study has an implication for companies to motivate implementation as well as activation of Data Analytics technique.

• The Journal of Industrial Distribution & Business
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• v.13 no.11
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• pp.1-10
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• 2022

Purpose: This study measures the influence of facilitating conditions on employees' attitudes towards the adoption of big data analytics by selected medical aid organizations in Durban. In the health care sector, there are various sources of big data such as patients' medical records, medical examination results, and pharmacy prescriptions. Several organizations take the benefits of big data to improve their performance and productivity. Research design, data, and methodology: A survey research strategy was conducted on some selected medical aid organizations. A non-probability sampling and the purposive sampling technique were adopted in this study. The collected data was analysed using version 23 of Statistical Package for Social Science (SPSS) Results: the results show that the "facilitating conditions" have a positive influence on employees' attitudes in the adoption of big data analytics Conclusions: The findings of this study provide empirical and scientific contributions of the facilitating conditions issues regarding employee attitudes toward big data analytics adoption. The findings of this study will add to the body of knowledge in this field and raise awareness, which will spur further research, particularly in developing countries.

• Asia pacific journal of information systems
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• v.32 no.1
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• pp.70-91
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• 2022

Organizations today depend and thrive on timely, accurate and strategically relevant information. Business analytics (BA) holds the key to many of these issues. This paper validates a model on how the usage of BA leads to operational efficiency. We identified the factors of basic analytical usage from the Business Capacity Maturity Model (BCMM). The scope of the study is restricted to the Information Technology Infrastructure and Application management domain. A survey was conducted among the managers of the IT companies in Bengaluru, India. The results showed a significant influence of data-oriented culture and BA tools and infrastructure on BA usage. We found a significant influence of BA usage and pervasive use on operational efficiency. The speed to insight is still not practised in organizations. The awareness level of analytical skills in organizations is very low.

• Journal of Information Technology Applications and Management
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• v.24 no.4
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• pp.197-214
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• 2017

This study sheds light on the source data quality in big data systems. Previous studies about big data success have called for future research and further examination of the quality factors and the importance of source data. This study extracted the quality factors of source data from the user's viewpoint and empirically tested the effects of source data quality on the usefulness and utilization of big data analytics results. Based on the previous researches and focus group evaluation, four quality factors have been established such as accuracy, completeness, timeliness and consistency. After setting up 11 hypotheses on how the quality of the source data contributes to the usefulness, utilization, and ongoing use of the big data analytics results, e-mail survey was conducted at a level of independent department using big data in domestic firms. The results of the hypothetical review identified the characteristics and impact of the source data quality in the big data systems and drew some meaningful findings about big data characteristics.

• Journal of Digital Contents Society
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• v.13 no.2
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• pp.205-211
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• 2012

This paper presents research result of digital marketing model for improving enterprise marketing campaign performance. Recently, the enterprises which had completed projects such as ERP, CRM, and SCM for business value chain process transformation are working to improve enterprise marketing process. It is the trend for enterprises to use digital marketing tactics to overcome the limit of existing traditional marketing tactics. Especially, enterprises try to adopt digital marketing for marketing campaign performance. In this paper, digital marketing research model and hypothesis were established and statistically analyzed by marketing expert survey research. The research finding is that Web Analytics, Social Analytics, Personalized CRM, Campaign execution automation, Real-Time campaign management can be core influencers for marketing campaign performance improvement.

• Corrosion Science and Technology
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• v.7 no.4
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• pp.208-211
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• 2008

Safety operation of main pipelines is primarily provided by anticorrosive monitoring. Anticorrosive monitoring of oil pipeline transportation objects is based on results of complex corrosion inspections, analysis of basic data including design data, definition of a corrosion residual rate and diagnostic of general equipment's technical condition. All the abovementioned arrangements are regulated by normative documents. For diagnostics of corrosion-technical condition of oil pipeline transportation objects one presently uses different methods such as in-line inspection using devices with ultrasonic, magnetic or another detector, acoustic-emission diagnostics, electrometric survey, general external corrosion diagnostics and cameral processing of obtained data. Results of a complex of diagnostics give a possibility: $\cdot$ to arrange a pipeline's sectors according to a degree of corrosion danger; $\cdot$ to check up true condition of pipeline's metal; $\cdot$ to estimate technical condition and working ability of a system of anticorrosive protection. However such a control of corrosion technical condition of a main pipeline creates the appearance of estimation of a true degree of protection of an object if values of protective potential with resistive component are taken into consideration only. So in addition to corrosive technical diagnostics one must define a true residual corrosion rate taking into account protective action of electrochemical protection and true protection of a pipeline one must at times. Realized anticorrosive monitoring enables to take a reasonable decision about further operation of objects according to objects' residual life, variation of operation parameters, repair and dismantlement of objects.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.87-99
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• 2013

Massive increases in data available to an organization are creating a new opportunity for competitive advantage. In this era of big data, developing analytics capabilities, therefore, becomes critical to take advantage of internal and external data and gain insights for data-driven decision making. However, the use of data in education is in its infancy, in comparison with business and government, and the potential for data analytics to impact education services is growing. In this paper, I survey how universities are currently using education data to improve students' performance and administrative efficiency, and propose new ways of extending the current use. In addition, with the so-called data scientist shortage, universities should be able to train professionals with data analytics skills. This paper discusses which skills are valuable to data scientists and introduces various training and certification programs offered by universities and industry. I finally conclude the paper by exploring new curriculums where students, by themselves, can learn how to find and use relevant data even in any courses.