Conceptual Model of Business Value of Business Intelligence Systems

With advances in the business intelligence area, there is an increasing interest for the introduction of business intelligence systems into organizations. Although the opinion about business intelligence and its creation of business value is generally accepted, economic justification of investments into business intelligence systems is not always clear. Measuring the business value of business intelligence in practice is often not carried out due to the lack of measurement methods and resources. Even though the perceived benefits from business intelligence systems, in terms of better information quality or achievement of information quality improvement goals, are far from being neglected, these are only indirect business benefits or the business value of such systems. The true business value of business intelligence systems hides in improved business processes and thus in improved business performance. The aim of the paper is to propose a conceptual model to assess business value of business intelligence systems that was developed on extensive literature review, in-depth interviews, and case study analysis for researching business intelligence systems’ absorbability capabilities or key factors facilitating usage of quality information provided by such systems respectively

Methods of Teaching Social Intelligence and its Impact on Service Quality

Current literature on social intelligence was reviewed and analyzed and most was focused on the general understanding of social intelligence and methods of measurement. There is little literature that connects social intelligence to the business world and none that focuses on the hospitality industry. It is important to fill the gap in research because social intelligence could have a significant impact on service quality, which in turn affects revenues. The current literature provides a good foundation, but more research is needed in the hospitality industry to determine the impact social intelligence actually has on service quality. Experimentation utilizing several measurement techniques from the literature could help add to the research and bridge the gap between social intelligence and service quality in the hospitality industry

From Measurement to Management: the Influence of IT on Service Operations

The state of service management practice and the developments in IT-efficiency research prompt the call for managerial relevance, normative theory building and the conceptualization and measurement of the impact of Information Technology (IT) on service efficiency. Drawing on theoretical insights from economic and behavioral literature, this article deduces a work system centered model of the service outlet and proposes a measurement methodology (ITIMPACT) geared towards the development of a business intelligence tool. The measurement follows a two-step methodology that first assesses compared-to-best efficiency, using Data Envelopment Analysis (DEA), and subsequently explains efficiency differences using a regression framework. An inter-disciplinary approach bases the first step on econometric logic, while the second takes its foundation in behavioral sciences, and information system research.Service industry;

Overview of Business Intelligence Maturity Models

Today, companies wish to evaluate and justify their investments into Business Intelligence systems, which requires measurement of their business value and comparison with similar systems in other companies. Maturity models offer an adequate baseline for comparison. Maturity models define levels of definition, efficiency, manageability and measurement of the monitored environment. This paper briefly describes and analyzes six different maturity models that can be used for the maturity of BI systems assessment. An overview and analysis will show that most of the models do not cover the whole area of Business Intelligence, but they rather focus on a specific point of view and/or area of the problem domain. Results show that by using maturity models, only a short period of time is needed in order for one to discover the areas within the company or institution that need special, more intensive attention and work. Namely, results of the analysis often expose problematic areas that could be easily overlooked

Cognitive absorption and the behavioral intention to use business intelligence: Determinants and influence of cognitive absorption

Introduction: In the last decade Business intelligence became increasingly important for organizations to adopt in order to compete in highly changing industries. The main purpose of the business intelligence system is to enable users to make and execute decisions better and faster. However, the success of business intelligence is not guaranteed. Users are defined as one of the main sources of the potential issues, since they are the ones who should incorporate the new system into their work environment. Users interact with the system via business intelligence application, and for that reason, it is important to investigate users' behavioral intentions towards the application. In order to investigate users intentions, several theoretical constructs have been developed, mostly from the technology acceptance research. The main one being explored in this study is cognitive absorption. Purpose: The purpose of this study is to test the theory presented in Agarwal and Karahanna (2000) in the new setting. The authors’ theory proved the influence of the cognitive absorption in the context of the World Wide Web. We will use their theory in order to investigate the determinants and the influence of the cognitive absorption in the business intelligence context. Method: This study is explanatory in nature. We used survey methodology - questionnaire for the hypotheses testing. We tested seven hypotheses based on the theoretical model adapted from the literature. Several statistical analyses have been conducted for measurement and hypotheses testing, and they have been performed in SPSS and SmartPLS software packages. Conclusion: The results of the analysis identified playfulness as the significant determinant of the cognitive absorption with the business intelligence application. Furthermore, cognitive absorption proved to have the significant direct as well as indirect influence on the behavioral intention to use the business intelligence application

A New Concept of Marketing: The Emotional Marketing

Nowadays, in the marketing area, a new concept of marketing is emerging: the emotional marketing. The emotional marketing studies how to arouse emotions in people to induce them to buy that particular produc/service. Recent studies shown how purchasing choices and decisions are the result of a careful analysis of rational and emotional aspects. Psychological literature recognizes that the emotional conditions influence every stage of decision-making in purchasing process. Emotions play a key role in any kind of social or business decision. The emotions are manifested in verbal, facial and textual expressions. People when speak, interact and write, convey emotions.emotions, emotional marketing, emotional brand, emotional intelligence,emotions measurement.

Searching for Emotional Intelligence Measurement in Indonesia Context with Innovative Approach

AbstractEmotional Intelligence is widely accepted as one of the dominant factors contributing to superior educational and professional performance. Previous research has determined the contribution of Emotional Intelligence and some factors that influence Emotional Intelligence. Researchers have introduced measurement tools for Emotional Intelligence. Those that have been accepted internationally; Multifactor Emotional Intelligence Scale (MSCEIT) of Mayer, Caruso, and Salovey (2000); Emotional Quotient Inventory (EQ-i) of Bar-on (2002); and Emotional and Social Competence Inventory (ESCI-360) of Goleman, Boyatzis and HayGroup (1999). These models are generally accepted but constructed for Western empirical setting and written in the English language.Moon (2011) suggest that EI cannot be fully or meaningfully understood without consideration of people's cultural values and beliefs due to the existence of cross-cultural differences in EI. Instead of adopting a standard for Emotional Intelligence according to a western approach, it is important to develop an innovative Emotional Intelligence measurement tool according to the context of Indonesia because Indonesia is a multi-cultural country in South East Asia with too strong different characteristic when compared with Western nations. Wang (2009) argues that Eastern and Western cultures have numerous differences because Eastern and Western people live in different environment and are educated in distinctively different way. As a result, the characteristics of each culture are shown in its people behaviour, their attitude toward life and love, and their personalities (Wang, 2009).This research is done by innovatively analyse the current research on Emotional Intelligence in Indonesia and further research recommendation on innovative Emotional Intelligence measurement for Indonesia context. The purpose of this paper is to learn about and to compile some research on Emotional Intelligence specific to Indonesia's context and its impact on determining success. The various research components such as literature review, exploratory research and descriptive research have been implemented. Self Assessment Questionnaires were designed for Indonesian Business School graduates (undergraduates and master program), leaders of multi-sectors companies, and government employees. At the end, structured equation modelling was utilized to create a path model.The findings indicate that in Indonesia, Emotional Intelligence and Competencies have an important role in determining success and influencing performance. At last, the findings will be a powerful insight to develop innovative Emotional Intelligence measurement tools in Indonesian version

Improving Green Computing in Business Intelligence by Measuring Performance of Reverse Supply Chains

Increasing attention has been given to greencomputing in Business Intelligence. This paper specificallyconsiders the measurement of performance in the reversesupply chain. That is because of the increasing value ofproducts and technology at the end of general direct supplychains as well as the impact of new green legislation. Unlikeforward supply chains, design strategies for reverse supplychains are relatively unexplored and underdeveloped.Meanwhile measuring supply chain performance is becomingimportant as the need for data in business intelligence systemsincreases and the understanding, collaboration and integrationincreases between supply chain members. It also helpscompanies to target the most profitable market segments oridentify a suitable service definition. This paper describes asynthesis of known theory concerning measuring performanceand assesses the state of the art. Strengths and gaps areidentified. Some initial results are presented for measuringsupply performance in reverse supply chains (using robustmethods) and are outlined future research needs

Does the Demographic Factor Impact Enterprise Business Intelligence Maturity Initiaves in Companies in Malaysia?

This chapter proposes an Enterprise Business Intelligence Maturity Model that involves thirteen key process areas (Strategic Management, Performance Measurement, Balanced Scorecard, Information Quality, Data Warehouse, Master Data Management, Metadata Management, Analytical, Infrastructure, Knowledge Management, People, Organization Culture and Change Management). This key objective of this chapter was to investigate impact on demographic factors such as age of BI initiave, organizational size, number of IT/BI employees, type of industry and revenue of the company towards the Enterprise Business Intelligence Initiave. A survey was conducted around 132 companies in this study. Results shows that age of BI initiatives, organizational size and number of IT/BI employees have relationship on BI maturity level while BI maturity level has strong relationship on the revenue of the company. Results above also show that the type of industry has no relationship on the BI maturity level