Relationship between accounting benefits and ERP user satisfaction in the context of the fourth industrial revolution

The importance of corporate social responsibility is shaping investment decisions and entrepreneurial actions in diverse perspectives. The rapid growth of SMEs has tremendous impacts on the environment. Nonetheless, the economic emergence plan of Cameroon has prompted government support of SMEs through diverse projects. This saw economic growth increased to 3.8% and unemployment dropped to 4.3% caused by the expansion of private sector investments. The dilemma that necessitated this study is the response strategy of SMEs operators towards environmental sustainability. This study, thus seeks to examine the effects of entrepreneurial intentions and actions on environmental sustainability. The research is a conclusive case study design supported by the philosophical underpins of objectivism ontology and positivism epistemology. Data was sourced from four hundred (400) SMEs operators purposively sampled from the Centre and Littoral regions of Cameroon using structured questionnaire. Data was analysed using the Structural Equation Modelling technique with the aid of statistical packages including: SPSS 24 and AMOS 23. The study revealed that entrepreneurial action has weak positive statistical significant impacts on environmental sustainability; whereas entrepreneurial intention has strong positive statistical significant effects on environmental sustainability. Entrepreneurial intention comprised of self-efficacy and perceived control whereas, entrepreneurial actions involved entrepreneurial alertness and uncertainty. This study concludes that entrepreneurs in Cameroon have sustainable intentions to protect the environment but; the current actions taken are inadequate. This research recommends that entrepreneurs should enhance efforts toward attaining the state of genuine sustainabilit

A unified view of data-intensive flows in business intelligence systems : a survey

Data-intensive flows are central processes in today’s business intelligence (BI) systems, deploying different technologies to deliver data, from a multitude of data sources, in user-preferred and analysis-ready formats. To meet complex requirements of next generation BI systems, we often need an effective combination of the traditionally batched extract-transform-load (ETL) processes that populate a data warehouse (DW) from integrated data sources, and more real-time and operational data flows that integrate source data at runtime. Both academia and industry thus must have a clear understanding of the foundations of data-intensive flows and the challenges of moving towards next generation BI environments. In this paper we present a survey of today’s research on data-intensive flows and the related fundamental fields of database theory. The study is based on a proposed set of dimensions describing the important challenges of data-intensive flows in the next generation BI setting. As a result of this survey, we envision an architecture of a system for managing the lifecycle of data-intensive flows. The results further provide a comprehensive understanding of data-intensive flows, recognizing challenges that still are to be addressed, and how the current solutions can be applied for addressing these challenges.Peer ReviewedPostprint (author's final draft

Building a Data Warehouse step by step

Data warehouses have been developed to answer the increasing demands of quality information required by the top managers and economic analysts of organizations. Their importance in now a day business area is unanimous recognized, being the foundation for developing business intelligence systems. Data warehouses offer support for decision-making process, allowing complex analyses which cannot be properly achieved from operational systems. This paper presents the ways in which a data warehouse may be developed and the stages of building it.data warehouse, data mart, data integration, database management system, OLAP, data mining

Creating business value from big data and business analytics : organizational, managerial and human resource implications

This paper reports on a research project, funded by the EPSRC’s NEMODE (New Economic Models in the Digital Economy, Network+) programme, explores how organizations create value from their increasingly Big Data and the challenges they face in doing so. Three case studies are reported of large organizations with a formal business analytics group and data volumes that can be considered to be ‘big’. The case organizations are MobCo, a mobile telecoms operator, MediaCo, a television broadcaster, and CityTrans, a provider of transport services to a major city. Analysis of the cases is structured around a framework in which data and value creation are mediated by the organization’s business analytics capability. This capability is then studied through a sociotechnical lens of organization/management, process, people, and technology. From the cases twenty key findings are identified. In the area of data and value creation these are: 1. Ensure data quality, 2. Build trust and permissions platforms, 3. Provide adequate anonymization, 4. Share value with data originators, 5. Create value through data partnerships, 6. Create public as well as private value, 7. Monitor and plan for changes in legislation and regulation. In organization and management: 8. Build a corporate analytics strategy, 9. Plan for organizational and cultural change, 10. Build deep domain knowledge, 11. Structure the analytics team carefully, 12. Partner with academic institutions, 13. Create an ethics approval process, 14. Make analytics projects agile, 15. Explore and exploit in analytics projects. In technology: 16. Use visualization as story-telling, 17. Be agnostic about technology while the landscape is uncertain (i.e., maintain a focus on value). In people and tools: 18. Data scientist personal attributes (curious, problem focused), 19. Data scientist as ‘bricoleur’, 20. Data scientist acquisition and retention through challenging work. With regards to what organizations should do if they want to create value from their data the paper further proposes: a model of the analytics eco-system that places the business analytics function in a broad organizational context; and a process model for analytics implementation together with a six-stage maturity model

Modeling innovation: determinants of innovativeness and the impact of innovation on firm performance

The objective of this paper is to report on the method, analysis, and conclusions concerning two research questions formulated as: What are the determinants of innovation at firm level?” and “what is the impact of innovation on firm performance?” The results are based on an empirical study covering 184 manufacturing firms in the Northern Marmara region within Turkey. A comprehensive and integrated innovation model is presented composed of two sub-models proposed in line with the two research questions posed. Results and conclusions are presented

A Few Implementation Solutions for Business Intelligence

To succeed in the context of a global and dynamic economic environment, the companies must use all the information they have, as efficiently as possible, in order to gain competitive advantages and to consolidate their position on the market. They have to respond quickly to the changes in the business environment and to adapt themselves to the market’s requirements. To achieve these goals, the companies must use modern informatics technologies for data acquiring, storing, accessing and analyzing. These technologies are to be integrated into innovative solutions, such as Business Intelligence systems, which can help managers to better control the business practices and processes, to improve the company’s performance and to conserve it’s competitive advantages.Business Intelligence, competitive advantage, OLAP, data mining, key performance indicators.

Overcoming Barriers in Supply Chain Analytics—Investigating Measures in LSCM Organizations

While supply chain analytics shows promise regarding value, benefits, and increase in performance for logistics and supply chain management (LSCM) organizations, those organizations are often either reluctant to invest or unable to achieve the returns they aspire to. This article systematically explores the barriers LSCM organizations experience in employing supply chain analytics that contribute to such reluctance and unachieved returns and measures to overcome these barriers. This article therefore aims to systemize the barriers and measures and allocate measures to barriers in order to provide organizations with directions on how to cope with their individual barriers. By using Grounded Theory through 12 in-depth interviews and Q-Methodology to synthesize the intended results, this article derives core categories for the barriers and measures, and their impacts and relationships are mapped based on empirical evidence from various actors along the supply chain. Resultingly, the article presents the core categories of barriers and measures, including their effect on different phases of the analytics solutions life cycle, the explanation of these effects, and accompanying examples. Finally, to address the intended aim of providing directions to organizations, the article provides recommendations for overcoming the identified barriers in organizations