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I examine if current diversity, equity and inclusion (DEI) initiatives can actually accomplish what they aim and claim to do. I argue that perforce they cannot, as they remain instruments of capitalist corporations and other similar structures.

I draw on a variety of literature, from poetry to theories and to empirical findings.

DEI work so far does not live up to its hyped-up claims. It is time for scholars and practitioners to question the DEI industrial complex and its influence on organizational dynamics. It is not clear that justice can ever be achieved in a capitalist neoliberal economy.

The paper is not an empirical paper.

DEI work needs to be re-conceived so that it addresses power imbalances, rather serving as a tool to keep organizations comfortable in seeming to change.

DEI practitioners will need to draw deeply on their courage so that they do not reinforce the existing systems of capitalist oppression through their well-intentioned work.

The paper argues that DEI work can accomplish little without a radical reconceptualization of its nature as a genuine tool for change, rather than simply window dressing.

The aim of this paper is to develop a suitable artificial neural network (ANN) model that fits best in predicting the experimental flow stress values to the closet proximity for mechanically alloyed Al6063/0.75Al2O3/0.75Y2O3 hybrid nanocomposite.

The ANN model is implemented on neural network toolbox of MATLAB^® using feed‐forward back propagation network and logsig functions. A set of 80 training data and 20 testing data were used in the ANN model. The layout of the network is arranged with three input parameters that include temperature, strain and strain rate, one hidden layer with 22 neurons and one output parameter consisting of flow stress. Flow stress was also predicted using Arrhenius constitutive model.

Based on the comparison of the predicted results using ANN model and Arrhenius constitutive model, it was observed that the ANN model has higher accuracy and could be used to estimate the flow stress values during hot deformation of Al6063/0.75Al2O3/0.75Y2O3 hybrid nanocomposite.

The ANN trained with feed forward back propagation algorithm developed, presents the excellent performance of flow stress prediction of Al6063/0.75Al2O3/0.75Y2O3 hybrid nanocomposite with minimum error rates.

The purpose of this paper is to highlight conceptual and technical difficulties in mapping “social enterprise” and “social purpose business” organizations.

This paper reflects on the design and administration of a social enterprise population survey in Ontario, Canada.

Numerous approaches used to frame social enterprise organizations were seriously flawed and fundamentally problematic, and criteria to distinguish social enterprise from other organizations were seemingly arbitrary, unstable, or unworkable.

This paper both contributes to those attempting to empirically research social enterprise organizations, and to the broader discussion concerning whether social enterprise is usefully approached as a distinctive organizational form.

In the last few decades, there has been growing interest in forecasting with computer intelligence, and both fuzzy time series (FTS) and artificial neural networks (ANNs) have gained particular popularity, among others. Rather than the conventional methods (e.g., econometrics), FTS and ANN are usually thought to be immune to fundamental concepts such as stationarity, theoretical causality, post-sample control, among others. On the other hand, a number of studies significantly indicated that these fundamental controls are required in terms of the theory of forecasting, and even application of such essential procedures substantially improves the forecasting accuracy. The aim of this paper is to fill the existing gap on modeling and forecasting in the FTS and ANN methods and figure out the fundamental concepts in a comprehensive work through merits and common failures in the literature. In addition to these merits, this paper may also be a guideline for eliminating unethical empirical settings in the forecasting studies.

The purpose of this paper is to present a novel framework for strategic decision making using Big Data Analytics (BDA) methodology.

In this study, two different machine learning algorithms, Random Forest (RF) and Artificial Neural Networks (ANN) are employed to forecast export volumes using an extensive amount of open trade data. The forecasted values are included in the Boston Consulting Group (BCG) Matrix to conduct strategic market analysis.

The proposed methodology is validated using a hypothetical case study of a Chinese company exporting refrigerators and freezers. The results show that the proposed methodology makes accurate trade forecasts and helps to conduct strategic market analysis effectively. Also, the RF performs better than the ANN in terms of forecast accuracy.

This study presents only one case study to test the proposed methodology. In future studies, the validity of the proposed method can be further generalized in different product groups and countries.

In today’s highly competitive business environment, an effective strategic market analysis requires importers or exporters to make better predictions and strategic decisions. Using the proposed BDA based methodology, companies can effectively identify new business opportunities and adjust their strategic decisions accordingly.

This is the first study to present a holistic methodology for strategic market analysis using BDA. The proposed methodology accurately forecasts international trade volumes and facilitates the strategic decision-making process by providing future insights into global markets.

Although Iran is one of the largest producers and exporters of saffron in the world, the organic saffron market in Iran is still in its early stages, and there is scarce empirical evidence in this regard. Therefore, the study's primary purpose is to segment the organic saffron market in Mashhad, Iran using neobehavioristic theory and machine learning methods.

Considering the neobehavioristic theory of consumer behavior, the organic saffron market was segmented using crisp and fuzzy clustering algorithms. Also, to assess the relative importance of the factors affecting the intention to buy organic saffron in each market segment, a sensitivity analysis was performed on the output of the artificial neural network (ANN). A total of 400 questionnaires were collected in Mashhad, Iran in January and February 2020.

In contrast to the belief that psychological factors are more important in market segmentation than demographic characteristics, findings showed that the demographic characteristics of consumers, especially education and income, are the dominant variables in the segmentation of the organic food market. Among the 4 A’s marketing mix elements, the results showed that a low level of awareness and accessibility are obstacles to organic saffron market development. Advertising, distribution channel improvement, package downsizing and online business development are suggested strategies for expanding the organic saffron market in Iran.

The results of the present study will help policymakers and suppliers of organic saffron to identify their target markets and design short- and long-term marketing strategies to develop the organic saffron market.

Machine learning methods and the neobehavioristic theory of consumer behavior were used to segment the organic food market.

It is widely believed that the construction industry is more volatile than other sectors of the economy. Accurate predictions of the level of aggregate demand for construction are of vital importance to all sectors of this industry (e.g. developers, builders and consultants). Empirical studies have shown that accuracy performance varies according to the type of forecasting technique and the variable to be forecast. Hence, there is a need to gain useful insights into how different techniques perform, in terms of accuracy, in the prediction of demand for construction. In Singapore, the residential sector has often been regarded as one of the most important owing to its large percentage share in the total value of construction contracts awarded per year. In view of this, there is an increasing need to objectively identify a forecasting technique which can produce accurate demand forecasts for this vital sector of the economy. The three techniques examined in the present study are the univariate Box‐Jenkins approach, the multiple loglinear regression and artificial neural networks. A comparison of the accuracy of the demand models developed shows that the artificial neural network model performs best overall. The univariate Box‐Jenkins model is the next best, while the multiple loglinear regression model is the least accurate. Relative measures of forecasting accuracy dealing with percentage errors are used to compare the forecasting accuracy of the three different techniques.