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This paper aims to study the informational dynamics that take place between a firm and its stakeholders with respect to corporate environmental management.

The analysis is based on a case study contrasting environmental information reported by the case firm with environmental information about the firm disclosed by four stakeholder groups or their representatives (governments, the community, environmental non-governmental organizations and investors) over three years. The information flow of disclosure is also considered.

The results suggest that the informational dynamics are composed of multiple related patterns. The patterns range from correspondence between disclosures to stakeholders complementing or contradicting corporate disclosures. Different patterns are associated with different levels of interactions from stakeholders, who are most involved when they combine disclosure patterns around key environmental issues for the forest industry. Limited interactions are observed from the firm, suggesting a symbolic engagement within the dynamics and a strategic accountability approach.

Limitations are found in the focus on disclosure outlets without examination of their production and reception, and in the inherent nature of the documents collected to represent each perspective. Some stakeholder groups were excluded from the study due to data unavailability.

This paper offers an in-depth analysis of firm-stakeholders interactions with respect to environmental reporting and maps the information flow of their disclosure.

This paper aims to explore the limitations of the conformal finite difference time-domain method (C-FDTD or Dey–Mittra) when modeling perfect electric conducting (PEC) and lossless dielectric curved surfaces in coarse meshes. The C-FDTD is a widely known approach to reduce error of curved surfaces in the FDTD method. However, its performance limitations are not broadly described in the literature, which are explored as a novelty in this paper.

This paper explores the C-FDTD method applied on field scattering simulations of two curved surfaces, a dielectric and a PEC sphere, through the frequency range from 0.8 to 10 GHz. For each sphere, the mesh was progressively impoverished to evaluate the accuracy drop and performance limitations of the C-FDTD with the mesh impoverishment, along with the wideband frequency range described.

This paper shows and quantifies the C-FDTD method’s accuracy drops as the mesh is impoverished, reducing C-FDTD’s performance. It is also shown how the performance drops differently according to the frequency of interest.

With this study, coarse meshes, with smaller execution time and reduced memory usage, can be further explored reliably accounting the desired accuracy, enabling a better trade-off between accuracy and computational effort.

This paper quantifies the limitations of the C-FDTD in coarse meshes in a wideband manner, which brings a broader and newer insight upon C-FDTD’s limitations in coarse meshes or relatively small objects in electromagnetic simulation.

As a critical and intimate form of inquiry, ethnography remains close to lived realities and equips scholars with a unique methodological angle on social phenomena. This paper aims to explore the potential gains from an increased use of ethnography in social enterprise studies.

The authors develop the argument through a set of dualistic themes, namely, the socio-economic dichotomy and the discourse/practice divide as predominant critical lenses through which social enterprise is currently examined, and suggest shifts from visible leaders to invisible collectives and from case study-based monologues to dialogic ethnography.

Ethnography sheds new light on at least four neglected aspects. Studying social enterprises ethnographically complicates simple reductions to socio-economic tensions, by enriching the set of differences through which practitioners make sense of their work-world. Ethnography provides a tool for unravelling how practitioners engage with discourse(s) of power, thus marking the concrete results of intervention (to some degree at least) as unplannable, and yet effective. Ethnographic examples signal the merits of moving beyond leaders towards more collective representations and in-depth accounts of (self-)development. Reflexive ethnographies demonstrate the heuristic value of accepting the self as an inevitable part of research and exemplify insights won through a thoroughly bodily and emotional commitment to sharing the life world of others.

The present volume collects original ethnographic research of social enterprises. The editorial develops the first consistent account of the merits of studying social enterprises ethnographically.

To be sustainable and competitive in the current business environment, it is useful to understand users’ sentiment towards products and services. This critical task can be achieved via natural language processing and machine learning classifiers. This paper aims to propose a novel probabilistic committee selection classifier (PCC) to analyse and classify the sentiment polarities of movie reviews.

An Indian movie review corpus is assembled for this study. Another publicly available movie review polarity corpus is also involved with regard to validating the results. The greedy stepwise search method is used to extract the features/words of the reviews. The performance of the proposed classifier is measured using different metrics, such as F-measure, false positive rate, receiver operating characteristic (ROC) curve and training time. Further, the proposed classifier is compared with other popular machine-learning classifiers, such as Bayesian, Naïve Bayes, Decision Tree (J48), Support Vector Machine and Random Forest.

The results of this study show that the proposed classifier is good at predicting the positive or negative polarity of movie reviews. Its performance accuracy and the value of the ROC curve of the PCC is found to be the most suitable of all other classifiers tested in this study. This classifier is also found to be efficient at identifying positive sentiments of reviews, where it gives low false positive rates for both the Indian Movie Review and Review Polarity corpora used in this study. The training time of the proposed classifier is found to be slightly higher than that of Bayesian, Naïve Bayes and J48.

Only movie review sentiments written in English are considered. In addition, the proposed committee selection classifier is prepared only using the committee of probabilistic classifiers; however, other classifier committees can also be built, tested and compared with the present experiment scenario.

In this paper, a novel probabilistic approach is proposed and used for classifying movie reviews, and is found to be highly effective in comparison with other state-of-the-art classifiers. This classifier may be tested for different applications and may provide new insights for developers and researchers.

The proposed PCC may be used to classify different product reviews, and hence may be beneficial to organizations to justify users’ reviews about specific products or services. By using authentic positive and negative sentiments of users, the credibility of the specific product, service or event may be enhanced. PCC may also be applied to other applications, such as spam detection, blog mining, news mining and various other data-mining applications.

The constructed PCC is novel and was tested on Indian movie review data.

The purpose of this paper is to investigate the first three stochastic natural frequencies of skewed sandwich plates, considering uncertain system parameters. To conduct the sensitivity analysis for checking the criticality of input parameters.

The theoretical formulation is developed based on higher-order-zigzag theory in accordance with the radial basis function (RBF) and stochastic finite element (FE) model. A cubic function is considered for in-plane displacement over thickness while a quadratic function is considered for transverse displacement within the core and remains constant in the facesheet. RBF is used as a surrogate model to achieve computational efficiency and accuracy. In the present study, the individual and combined effect of ply-orientation angle, skew angle, number of lamina, core thickness and material properties are considered for natural frequency analysis of sandwich plates.

Results presented in this paper illustrates that the skewness in the sandwich plate significantly affects the global dynamic behaviour of the structure. RBF surrogate model coupled with stochastic FE approach significantly reduced the computational time (more than 1/18 times) compared to direct Monte Carlo simulation approach.

The stochastic results for dynamic stability of sandwich plates show that the inevitable source uncertainties present in the input parameters result in significant variation from the deterministic value demonstrates the need for inclusive design paradigm considering stochastic effects. The present paper comprehensively establishes a generalized new RBF-based FE approach for efficient stochastic analysis, which can be applicable to other complex structures too.

Email is a rapid and cheapest medium of sharing information, whereas unsolicited email (spam) is constant trouble in the email communication. The rapid growth of the spam creates a necessity to build a reliable and robust spam classifier. This paper aims to presents a study of evolutionary classifiers (genetic algorithm [GA] and genetic programming [GP]) without/with the help of an ensemble of classifiers method. In this research, the classifiers ensemble has been developed with adaptive boosting technique.

Text mining methods are applied for classifying spam emails and legitimate emails. Two data sets (Enron and SpamAssassin) are taken to test the concerned classifiers. Initially, pre-processing is performed to extract the features/words from email files. Informative feature subset is selected from greedy stepwise feature subset search method. With the help of informative features, a comparative study is performed initially within the evolutionary classifiers and then with other popular machine learning classifiers (Bayesian, naive Bayes and support vector machine).

This study reveals the fact that evolutionary algorithms are promising in classification and prediction applications where genetic programing with adaptive boosting is turned out not only an accurate classifier but also a sensitive classifier. Results show that initially GA performs better than GP but after an ensemble of classifiers (a large number of iterations), GP overshoots GA with significantly higher accuracy. Amongst all classifiers, boosted GP turns out to be not only good regarding classification accuracy but also low false positive (FP) rates, which is considered to be the important criteria in email spam classification. Also, greedy stepwise feature search is found to be an effective method for feature selection in this application domain.

The research implication of this research consists of the reduction in cost incurred because of spam/unsolicited bulk email. Email is a fundamental necessity to share information within a number of units of the organizations to be competitive with the business rivals. In addition, it is continually a hurdle for internet service providers to provide the best emailing services to their customers. Although, the organizations and the internet service providers are continuously adopting novel spam filtering approaches to reduce the number of unwanted emails, the desired effect could not be significantly seen because of the cost of installation, customizable ability and the threat of misclassification of important emails. This research deals with all the issues and challenges faced by internet service providers and organizations.

In this research, the proposed models have not only provided excellent performance accuracy, sensitivity with low FP rate, customizable capability but also worked on reducing the cost of spam. The same models may be used for other applications of text mining also such as sentiment analysis, blog mining, news mining or other text mining research.

A comparison between GP and GAs has been shown with/without ensemble in spam classification application domain.

Despite consumers' widespread use of social media platforms, there is scant research on the underlying factors that influence their willingness to share digital footprints on social media. The purpose of this study is to address this research gap by examining consumers' cognitive and affective attitudes simultaneously.

This research used quantitative method by using online survey administered to a sample of 733 social media users.

The findings indicate both cognitive and affective attitudes jointly influence consumers' behavioural intentions with trust as a key construct mediating the relationship between attitudinal antecedents and consumers' willingness to share digital footprints on social media.

This study contributes to the information systems (IS) literature by offering a comprehensive framework constituting the joint attitudinal components as antecedents to consumers' behavioural intention for sharing digital footprints while trust works as a mediator.

This paper has important managerial implications. It helps marketers and IS managers in profiling consumers, understanding consumption patterns, sharing of digital footprints, which are useful for effective market segmentation, product development and future design of social media platforms. It informs social media providers of the importance of not only focussing on functional aspects but also underscores the essence of paying attention to consumers' affect towards social media platforms, especially trust.

The paper presents an original framework that explains the influence of joint attitudinal components on behavioural intention, with trust as a mediator.

Sentiment analysis and opinion mining are emerging areas of research for analyzing Web data and capturing users’ sentiments. This research aims to present sentiment analysis of an Indian movie review corpus using natural language processing and various machine learning classifiers.

In this paper, a comparative study between three machine learning classifiers (Bayesian, naïve Bayesian and support vector machine [SVM]) was performed. All the classifiers were trained on the words/features of the corpus extracted, using five different feature selection algorithms (Chi-square, info-gain, gain ratio, one-R and relief-F [RF] attributes), and a comparative study was performed between them. The classifiers and feature selection approaches were evaluated using different metrics (F-value, false-positive [FP] rate and training time).

The results of this study show that, for the maximum number of features, the RF feature selection approach was found to be the best, with better F-values, a low FP rate and less time needed to train the classifiers, whereas for the least number of features, one-R was better than RF. When the evaluation was performed for machine learning classifiers, SVM was found to be superior, although the Bayesian classifier was comparable with SVM.

This is a novel research where Indian review data were collected and then a classification model for sentiment polarity (positive/negative) was constructed.

The purpose of this paper is to look more closely, in the context of a given case study, at the role of civil society’s counter-accounts in facilitating democratic change in society, as an essential goal of an emancipatory and radical social accounting project.

A case study of a Canadian company’s plans to open a gold mine in western Romania is here analysed. Civil society’s opposition to the mining project gave rise to an unprecedented social movement contesting the project’s utility for Romanian society. The role played by counter-accounts produced by civil society groups is investigated.

Counter-accounts produced by civil society played multiple roles in the case study analysed. First, counter-accounts indicated the failure of corporate reports to present the gold mining project in a balanced manner. Second, counter-accounts were successful in problematizing the corporate approach to addressing the social, cultural and environmental impacts of the project, while also nurturing societal debate on these issues. Third, counter-accounts exposed the ideological inclinations of state institutions to favour economic interests over the social, cultural and environmental ones. As a result of these contributions, even if the counter-accounts were subjective, this study claims that they form a good basis for the development of emancipatory accounting.

Limitations associated with an interpretative approach and case study research apply.

The paper illustrates the potential of civil society’s counter accounts to enable societal debates, as means towards democratic, transformative change.

The purpose of this paper is to present the application of logical framework analysis (LFA) for implementing continuous quality improvement (CQI) across multiple settings in a tertiary care hospital.

This study adopts a multiple case study approach. LFA is implemented within three diverse settings, namely, intensive care unit, surgical ward, and acute in-patient psychiatric ward. First, problem trees are developed in order to determine the root causes of quality issues, specific to the three settings. Second, objective trees are formed suggesting solutions to the quality issues. Third, project plan template using logical framework (LOGFRAME) is created for each setting.

This study shows substantial improvement in quality across the three settings. LFA proved to be effective to analyse quality issues and suggest improvement measures objectively.

This paper applies LFA in specific, albeit, diverse settings in one hospital. For validation purposes, it would be ideal to analyse in other settings within the same hospital, as well as in several hospitals. It also adopts a bottom-up approach when this can be triangulated with other sources of data.

LFA enables top management to obtain an integrated view of performance. It also provides a basis for further quantitative research on quality management through the identification of key performance indicators and facilitates the development of a business case for improvement.

LFA is a novel approach for the implementation of CQI programs. Although LFA has been used extensively for project development to source funds from development banks, its application in quality improvement within healthcare projects is scant.