Search results

The study aims to investigate the usage of diversity and inclusion (D&I) signals in communications for employer branding through digital channels made by European companies.

A quali-quantitative content analysis approach was employed to detect the usage of D&I signals of the top 43 European companies ranked in the 2021 Refinitiv Diversity and Inclusion index. These signals were organized according to Plummer's Big 8 diversity's dimensions. A correlation analysis was conducted to verify a relationship between D&I initiatives and digital communication for employer branding on corporate websites and LinkedIn. Descriptive statistics were used to analyze the D&I dimensions' pervasiveness in digital communications and relevance on LinkedIn.

The results show that the correlation exists only between D&I initiatives and communication on the corporate website, while LinkedIn is still underused in this field. The most pervasive and relevant D&I dimensions for European companies are “Gender” and “Sexual Orientation”.

This paper enriches employer branding research by providing original insights into the use of D&I dimensions in digital communications.

Joint space multidimensional scaling (MDS) maps are often utilized for positioning analyses and are estimated with survey data of consumer preferences, choices, considerations, intentions, etc. so as to provide a parsimonious spatial depiction of the competitive landscape. However, little attention has been given to the possibility that consumers may display heterogeneity in their information usage (Bettman et al., 1998) and the possible impact this may have on the corresponding estimated joint space maps. This paper aims to address this important issue and proposes a new Bayesian multidimensional unfolding model for the analysis of two or three-way dominance (e.g. preference) data. The authors’ new MDS model explicitly accommodates dimension selection and preference heterogeneity simultaneously in a unified framework.

This manuscript introduces a new Bayesian hierarchical spatial MDS model with accompanying Markov chain Monte Carlo algorithm for estimation that explicitly places constraints on a set of scale parameters in such a way as to model a consumer using or not using each latent dimension in forming his/her preferences while at the same time permitting consumers to differentially weigh each utilized latent dimension. In this manner, both preference heterogeneity and dimensionality selection heterogeneity are modeled simultaneously.

The superiority of this model over existing spatial models is demonstrated in both the case of simulated data, where the structure of the data is known in advance, as well as in an empirical application/illustration relating to the positioning of digital cameras. In the empirical application/illustration, the policy implications of accounting for the presence of dimensionality selection heterogeneity is shown to be derived from the Bayesian spatial analyses conducted. The results demonstrate that a model that incorporates dimensionality selection heterogeneity outperforms models that cannot recognize that consumers may be selective in the product information that they choose to process. Such results also show that a marketing manager may encounter biased parameter estimates and distorted market structures if he/she ignores such dimensionality selection heterogeneity.

The proposed Bayesian spatial model provides information regarding how individual consumers utilize each dimension and how the relationship with behavioral variables can help marketers understand the underlying reasons for selective dimensional usage. Further, the proposed approach helps a marketing manager to identify major dimension(s) that could maximize the effect of a change of brand positioning, and thus identify potential opportunities/threats that existing MDS methods cannot provides.

To date, no existent spatial model utilized for brand positioning can accommodate the various forms of heterogeneity exhibited by real consumers mentioned above. The end result can be very inaccurate and biased portrayals of competitive market structure whose strategy implications may be wrong and non-optimal. Given the role of such spatial models in the classical segmentation-targeting-positioning paradigm which forms the basis of all marketing strategy, the value of such research can be dramatic in many marketing applications, as illustrated in the manuscript via analyses of both synthetic and actual data.

This paper aims to design a vulnerability assessment model considering the multidimensional and systematic approach to disaster risk and vulnerability. This model serves to both risk mitigation and disaster preparedness phases of humanitarian logistics.

A survey of 27,218 households in Pueblo Rico and Dosquebradas was conducted to obtain information about disaster risk for landslides, floods and collapses. We adopted a cross entropy-based approach for the measure of disaster vulnerability (Kullback–Leibler divergence), and a maximum-entropy estimation for the reconstruction of risk a priori categorization (logistic regression). The capabilities approach of Sen supported theoretically our multidimensional assessment of disaster vulnerability.

Disaster vulnerability is shaped by economic, such as physical attributes of households, and health indicators, which are in specific morbidity indicators that seem to affect vulnerability outputs. Vulnerability is heterogeneous between communities/districts according to formal comparisons of Kullback–Leibler divergence. Nor social dimension, neither chronic illness indicators seem to shape vulnerability, at least for Pueblo Rico and Dosquebradas.

The results need a qualitative or case study validation at the community/district level.

We discuss how risk mitigation policies and disaster preparedness strategies can be driven by empirical results. For example, the type of stock to preposition can vary according to the disaster or the kind of alternative policies that can be formulated on the basis of the strong relationship between morbidity and disaster risk.

Entropy-based metrics are not widely used in humanitarian logistics literature, as well as empirical data-driven techniques.

The purpose of this paper is to investigate which cross-country characteristics influence the going-public decisions and how the cultural values of the countries affect initial public offering (IPO) firms’ profitability and risk of financial distress.

Using a sample of privately held and firms that went public on the European and Asian Stock Exchanges between 2007 and 2011, this paper applies probit model and ordinary least squares regression to examine which cross-country characteristics could affect the decision to go public and how cultural values affect the profitability and risk of IPO firms.[AQ1] In addition, to overcome multicollinearity concerns caused by the use of Global Leadership and Organizational Behavioural Effectiveness culture dimensions, this paper factor analyses the dimensions using principal component analysis.

The results are as follows. First, this paper finds that firms in tradition-oriented countries are less likely to go public, while firms in result-oriented countries are more likely to hold an IPO. Second, this paper finds that country characteristics (i.e. financial deepening and taxation) affect the going-public decision. Third, this paper documents that IPO firms in traditionally and result-oriented countries have positive profitability and less risk of financial distress.

This study is intended for all those European and Asian policymakers and managers who want to improve their knowledge about what different indicators can establish the decision of firms that going-public facing different stages of their lifecycle. Specifically, policymakers wishing to promote IPO-activity in their countries may find it useful to strengthen the set of formal-institutions both to reduce corporate-taxation and to reduce the uncertainty associated with first-time share issuance and investment in such initiatives. This study is also intended for managers of companies that are not yet publicly-traded on their national stock-markets to be helpful to their decision-making processes.

This paper aims to extend the growing literature on the effects of cross-country factors on economic decision-making in finance and particularly adds to research that investigates the influence of these factors on the IPO decision of European and Asian firms.

The purpose of this study is to measure the availability, accessibility and usability of financial products and services in both rural and urban India from 1991 to 2014.

This paper uses principal component analysis (PCA) method to construct financial inclusion index that serves as a proxy variable for indicating the inclusiveness of financial products and services among the rural and urban people. To fulfill this objective, the study proposes separate indexes of financial inclusion for both rural and urban India from 1991 to 2014. The paper uses annual time series data from 1991 to 2014 to construct the rural-urban financial inclusion index. The used data have been collected from the basic statistical returns of Reserve Bank of India and Economic Political Weekly research foundation.

The study inferences that though there is a remarkable increase in financial inclusion in India from 1991 onwards, it does not result in sizeable growth of financial access to rural masses in comparison to urban masses. The rural India does not substantiate an equivalent growth to that of urban India, contrasting a perceptible increase in financial inclusion. The finding of this study will help the researchers and policymakers to understand the status of financial inclusion in the context of both rural and urban India. Furthermore, policymakers can take appropriate policy initiatives to fulfill the financial inclusion gap that exists between rural and urban people. Additionally, the proposed index is easy to compute and can be used to make comparison across countries for further studies.

The present paper attempts to include all possible dimensions (and indicators within a dimension) that have been considered so far by various authors. Therefore, the authors hope that this index will be more indicative and accurate than previous index. Again, the authors propose to use PCA for the first time to assign the weight of factors in the financial inclusion index for rural and urban India separately.

Recent accounting research has connected the coercive and enabling types of formalisation (C/E) (Adler and Borys, 1996) with the distinction between diagnostic and interactive controls (D/I) proposed by Simons (1995, 2000) to tackle research questions on complex control situations involving both the degree of employee autonomy and patterns of management attention. The diverse conceptual approaches used for connecting C/E and D/I have led to fragmentation in the literature and raise concerns about their conceptual clarity. In this paper, we assess the conceptual clarity of various forms of connection between C/E and D/I. Firstly, we conduct an in-depth content analysis of 59 recent papers, and inductively identify three points of conceptual ambiguity and divergence in the literature (namely, the perspective from which a phenomenon is studied; whether categories capture choices driven by design or by style-of-use; and the properties of control systems). We also observe that the literature proposes various forms of connection (i.e. coexistence, inclusion, and combination approaches). Secondly, we use the three detected points of ambiguity and divergence as assessment criteria, and evaluate the extent to which conceptual clarity is at risk under each form of connection. Based on this assessment, we provide guidelines to enhance the conceptual clarity of the connections between C/E and D/I, propose several research models, and indicate opportunities for future research in this area.

With the emergence of the basis of intellectual capital, competitive advantage was considered as the focus of competitive strategies, and the knowledge resulting from this approach became the basis for the development and strategic directions of companies in various fields of the company such as finance and accounting. The purpose of this study is sustainable intellectual capital reporting framework and evaluation of key examples in the context of capital market companies.

The methodology of this study was exploratory from the point of view of the developmental result and based on the type of objective and qualitative and quantitative basis was used to collect the data. The statistical population in the qualitative part was university experts and in the quantitative part financial managers of capital market companies. Data collection tools were interviews in the qualitative part and fuzzy scales and language comparison checklists in the quantitative part. Therefore, first through three stages of coding, the dimensions of the model were identified, and based on the fuzzy Delphi analysis, the reliability level was determined through the average between the first round and the second round of Delphi. Finally, through the default tests, the appropriate fuzzy model was first determined, and then hierarchical fuzzy analysis based on TODIM's approach was used to determine the most favorable axis of sustainable intellectual capital reporting.

The results in the qualitative part indicate the existence of 3 categories and 6 components and 39 conceptual themes in the form of a six-dimensional model. In the quantitative part, the results showed that by confirming the dimensions identified through fuzzy Delphi analysis, the most desirable axis of intellectual capital reporting is the component of technological capital reporting, which can play a more effective role in sustainable reporting.

This study, relying on the importance of the consequences of sustainable intellectual capital reporting, tries to evaluate the consequences of this field of financial reporting due to the lack of a coherent theoretical framework about capital market companies. In addition, the framework presented in this study promotes integrated thinking for firms to it would provide some level of incentive to those charged with governance concerning the voluntary compliance with the sustainable intellectual capital reporting framework.

The purpose of this study is to detect these reviews’ complex emotions, visualize and analyze them. Movie reviewers’ moviescores and reviews can be analyzed with respect to their emotion content, aggregated and projected onto a movie, resulting in an emotion map for a movie. It is then possible for a moviegoer to choose a movie, not only on the basis of movie scores and reviews, but also on the basis of aggregated emotional outcome of a movie as reflected by its emotion map displaying certain emotion map patterns desirable for the moviegoer.

The authors use the hourglass of emotion model to find the emotional scores of words of a review, then they use singular value decomposition to reduce the data dimension into singular scores. Once, they have the emotional scores of reviews, the authors cluster them by using k-means algorithm to find similar emotional levels of movies. Finally, the authors use heat maps to visualize four dimensions in a figure.

The authors are able to find the emotional levels of movie reviews, represent them in single scores and visualize them. The authors look the similarities and dissimilarities of movies based on their genre, ranking and emotional statuses. They also find the closest emotion levels of movies to a given movie.

The authors detect complex emotions from the text and simply visualize them.

This book is a policy proposal aimed at the democratic left. It is concerned with gradual but radical reform of the socio‐economic system. An integrated policy of industrial and economic democracy, which centres around the establishment of a new sector of employee‐controlled enterprises, is presented. The proposal would retain the mix‐ed economy, but transform it into a much better “mixture”, with increased employee‐power in all sectors. While there is much of enduring value in our liberal western way of life, gross inequalities of wealth and power persist in our society.

Reaching at the true development state is one of the important policy agenda of any country or its provinces under it. The true development state can further be ensured if the country or province does inclusive development. Financial inclusion is one of the important agenda through which a country or a province's inclusive growth and development can be ensured. The present study aims to compute the magnitudes of financial inclusion and its associated income link across the 18 districts of the state of West Bengal in India, with the help of the four banking indicators or dimensions – number of branches, number of accounts, amounts of deposits, and amounts of credit of the scheduled commercial banks, for the period 1997–2018. It finds that except Kolkata, all the other districts have low IFI values. Kolkata is at the top with near 100 percent financial inclusion. Mostly the districts from the North Bengal region are having very low magnitudes whereas the districts from the South Bengal region are having relative high magnitude of financial inclusion. The ultimate effects of these levels of financial inclusion have led to very low level of net domestic products of the districts.