Analysis of corporate environmental reports using statistical techniques and data mining

Measuring the effectiveness of corporate environmental reports, it being highly qualitative and less regulated, is often considered as a daunting task. The task becomes more complex if comparisons are to be performed. This study is undertaken to overcome the physical verification problems by implementing data mining technique. It further explores on the effectiveness by performing exploratory analysis and structural equation model to bring out the significant linkages between the selected 10 variables. Samples of five hundred and thirty nine reports across various countries are used from an international directory to perform the statistical analysis like: One way ANOVA (Analysis of Variance), MDA (Multivariate Discriminant Analysis) and SEM (Structural Equation Modeling). The results indicate the significant differences among the various types of industries in their environmental reporting, and the exploratory factors like stakeholder, organization strategy and industrial oriented factors, proved significant. The major accomplishment is that the findings correlate with the conceptual frame work of GRI

Evaluation of corporate environmental reports using data mining approach

Growing importance in corporate environmental reporting has undermined the metrics for its examination. Any such metrics are useful in measuring the effectiveness of reports. The main aim of the study in this paper is to examine and analyze the corporate environmental reports (N=539) using data mining approach. The assessment technique implemented in this study is in line with Global Reporting Initiative (GRI) disclosures. Our findings show that the data mining approach is a reliable and robust measurement. Further findings suggest that reports addressing the criteria differ from the nature of business. The study reveals the growing importance of addressing the various stakeholder groups, industrial base approach and organizational strategies in sustainable reporting

Analysis of corporate environmental reports using statistical techniques and data mining

Measuring the effectiveness of corporate environmental reports, it being highly qualitative and less regulated, is often considered as a daunting task. The task becomes more complex if comparisons are to be performed. This study is undertaken to overcome the physical verification problems by implementing data mining technique. It further explores on the effectiveness by performing exploratory analysis and structural equation model to bring out the significant linkages between the selected 10 variables. Samples of 539 reports across various countries are used from an international directory to perform the statistical analysis like - One way ANOVA (Analysis of Variance), MDA (Multivariate Discriminant Analysis) and SEM (Structural Equation Modeling). The results indicate the significant differences among the various types of industries in their environmental reporting, and the exploratory factors like stakeholder, organization strategy and industrial oriented factors, proved significant. The major accomplishment is that the findings correlate with the conceptual frame work of GRI

Analysis of corporate environmental reports using statistical techniques and data mining

Measuring the effectiveness of corporate environmental reports, it being highly qualitative and less regulated, is often considered as a daunting task. The task becomes more complex if comparisons are to be performed. This study is undertaken to overcome the physical verification problems by implementing data mining technique. It further explores on the effectiveness by performing exploratory analysis and structural equation model to bring out the significant linkages between the selected 10 variables. Samples of 539 reports across various countries are used from an international directory to perform the statistical analysis like - One way ANOVA (Analysis of Variance), MDA (Multivariate Discriminant Analysis) and SEM (Structural Equation Modeling). The results indicate the significant differences among the various types of industries in their environmental reporting, and the exploratory factors like stakeholder, organization strategy and industrial oriented factors, proved significant. The major accomplishment is that the findings correlate with the conceptual frame work of GRI

Analysis of corporate environmental reports using statistical techniques and data mining

Measuring the effectiveness of corporate environmental reports, it being highly qualitative and less regulated, is often considered as a daunting task. The task becomes more complex if comparisons are to be performed. This study is undertaken to overcome the physical verification problems by implementing data mining technique. It further explores on the effectiveness by performing exploratory analysis and structural equation model to bring out the significant linkages between the selected 10 variables. Samples of 539 reports across various countries are used from an international directory to perform the statistical analysis like - One way ANOVA (Analysis of Variance), MDA (Multivariate Discriminant Analysis) and SEM (Structural Equation Modeling). The results indicate the significant differences among the various types of industries in their environmental reporting, and the exploratory factors like stakeholder, organization strategy and industrial oriented factors, proved significant. The major accomplishment is that the findings correlate with the conceptual frame work of GRI

Reliability assessment of an intelligent approach to corporate sustainability report analysis

This paper describes our efforts in developing intelligent corporate sustainability report analysis software based on machine learning approach to text categorization and illustrates the results of executing it on real-world reports to determine the reliability of applying such approach. The document ultimately aims at proving that given sufficient training and tuning, intelligent report analysis could at last replace manual methods to bring about drastic improvements in efficiency, effectiveness and capacity

Determinants of e-commerce adoption among the SMEs in a developing economy: Malaysia

This paper details the results of an empirical study conducted to examine the factors influencing SMEs’ decision to adopt e-commerce in Malaysia. The methodology and results of this study may be applicable to several other states of developing in nature. A one-stage normative model was used to examine how internal variables of firms and external factors of government support affect the adoption of e-commerce. The results showed that changing trend in organizational characteristics, managerial profiles and government support as significant. Though some of the findings are contradictory to previous studies, it has a greater significance on the importance of policy implications of a developing economy

Determinants of e-commerce adoption among small and medium-sized enterprises in Malaysia

With a major proportion of research on Electronic Commerce (EC) undertaken on large corporations, and focused primarily on developed countries, little is known about the determinants of EC in Small and Medium-sized Enterprises (SMEs) of developing nations. This chapter explores the extent of EC use by SMEs, and provides some empirical evidence of how internal factors of firm and owner are influencing EC adoption among smaller businesses in Malaysia. The methodology and results of this study may be applicable to other developing countries. Findings confirm the low level of participation in EC by SMEs. The age of enterprise, as well as the owner’s gender and education were found to be significant in determining the level of EC adoption. Though some of the results contradict those of previous studies, they may have a greater implication for government authorities in drawing up guidelines, approaches, and formulating more effective frameworks to promote EC use among SMEs in developing countries

Enhanced intelligent text categorization using concise keyword analysis

Supervised learning is a popular approach to text classification among the research community as well as within software development industry. It enables intelligent systems to solve various text analysis problems such as document organization, spam detection and report scoring. However, the extremely difficult and time intensive process of creating a training corpus makes it inapplicable to many text classification problems. In this research, we explored the opportunities of addressing this pitfall by studying the ontological characteristics of document categories and grouping them under virtual super-categories to narrow down the search for a suitable category. Applying this method showed that classifier performance has greatly improved despite the relatively small size of the training corpus