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This chapter measures total factor productivity growth (TFPG) using Malmquist productivity index (MPI) and the growth of MPI of Indian Textile Industry employing nonparametric data envelopment analysis (DEA), during 1995–2016, exploring company (firm) level Center for Monitoring of Indian Economy (CMIE) Prowess data; examines whether TFPG has improved after the withdrawal of multifiber trade agreement (MFA) since 2005; decomposes TFPG into technical change (TC), technical efficiency change (TEC), and scale efficiency change (SEC); and explains the factors behind the movement of TFPG, considering the effect of R&D (RD), exports (EX), marketing expenditures (MKTs) advertisement expenditures (ADVs), imports (IMP), using second-stage panel regression. Empirical evidence supports fluctuating pattern of TFPG during 1995–2016, with a marginal declining tendency. TFPG has increased in 1999–2000, 2000–01, 2009–10, and 2012–13. After dismantling MFA, MPI level has significantly declined, with an increase in its growth rate, but the increase is not statistically significant. The effect of EX, RD, ADV are nonlinear, U-shaped, and IMP and MKT are inverted U-shaped, implying that the sign effect of any variable depends on its size. There are joint interaction effects of (a) RD and EX; RD and MKT which are positive, (b) ADV and MKT as represented by the ratio (ADV/MKT), having nonlinear inverted U-shaped relation. The joint interaction effect supports that the impact of one variable depends on the magnitude of other. The marginal effect of EX, IMP, and ADV are positive; increase in these variables promotes TFPG. The greater role of ADV over MKT is evident. The marginal effect of RD is negative; the average level of RD is too low to generate positive effects, and, thus, there is an urgency of increasing RD. The promising part of the decomposition analysis is that highest contribution to growth rate of TFPG is the growth rate of TEC followed by growth rate of TC, and thus by increasing TEC and TC, higher growth rate of TFPG is achieved and may be beneficial in the long run and may lead to absorption of economic shocks for an economy facing recession in its output growth. Some policy suggestions are made for boosting up TFPG.

Conventional economic contexts and value creation exert on the extensive use of intangible resources whose value is much greater than the tangible assets. In particular, intellectual capital (IC) is recognized as an important source of value creation for firms. However, the field of IC is majorly dominated by large firms, and little has been done in exploring IC in small and medium enterprises (SMEs). Within this context, the purpose of this article is to contribute to the body of literature on IC in the SMEs context by investigating the different dynamics of IC and understanding its impact on their organizational performance and processes.

The study has contextualized an integrative review of literature collected from Web of Science (WoS) and further analyzed integrating the bibliometric and manual review in a systematic approach.

The paper summarizes the key findings highlighting how SMEs can grasp IC in their core competencies and operational processes to achieve sustainable business performance. The study provides theoretical propositions highlighting the conceptual underpinnings of the literature on IC in SMEs and proposed methods outlining the methodological issues arising out of the diverse empirical/quantitative approaches adopted in the previous literature. Furthermore, empirical findings from the literature show that IC management affects a broad range of financial performance metrics in SMEs, however, sometimes with unexpected and mixed results. Hence, more research to replicate prior studies and corroborate extant research in both similar and different contexts would be desirable.

The study adopts an integrative review to understand the context of IC in SMEs; however, it does not study the synergy between varied IC components individually and their role in SMEs performance. Furthermore, the review relates IC to SMEs and does not cover the role of IC in large corporations.

The originality of this paper lies in its contribution to the body of knowledge in the field of IC and SMEs by exploring IC's impact on SME performance, especially the market performance, knowledge management (KM), strategic business models, sustainability performance (corporate social responsibility [CSR]), innovation and their intercollaborations (varied stakeholders).

Rapid advancement of data science has disrupted both business and employees in organizations. However, extant literature primarily focuses on the organizational level phenomena, and has almost ignored the employee/individual perspective. This study thereby intends to capture the experiences of mid-level managers about these disruptions vis a vis their corresponding actions.

In a small-sample qualitative research design, Interpretative Phenomenological Analysis (IPA) was adopted to capture this individual-level phenomenon. Twelve mid-level managers from large-scale Indian organizations that have extensively adopted data science tools and techniques participated in a semi-structured and in-depth interview process.

Our findings unfolded several perspectives gained from their experiences, leading thereby to two emergent person-job (mis)fit process models. (1) Managers, who perceived demands-abilities misfit (D-A misfit) as a growth-alignment opportunity vis a vis their corresponding actions, which effectively trapped them into a vicious cycle; and (2) the managers, who considered D-A misfit as a psychological strain vis a vis their corresponding actions, which engaged them into a benevolent cycle.

The present paper has major theoretical and managerial implications in the field of human resource management and business analytics.

The findings advise managers that the focus should be on developing an organizational learning eco-system, which would enable mid-level managers to gain their confidence and control over their job and work environment in the context of data science disruptions. Importantly, organizations should facilitate integrated workplace learning (both formal and informal) with an appropriate ecosystem to help mid-level managers to adapt to the data-science disruptions.

The present study offers two emergent cyclic models to the existing person–job fit literature in the context of data science disruptions. A scant attention of the earlier researchers on how individual employees actually experience disruption, and the corresponding IPA method used in the present study may add significant value to the extant literature. Further, it opens a timely and relevant future research avenues in the context of data science disruptions.

Depression is a mental health problem characterized by a persistent sense of sadness and loss of interest. EEG signals are regarded as the most appropriate instruments for diagnosing depression because they reflect the operating status of the human brain. The purpose of this study is the early detection of depression among people using EEG signals.

(i) Artifacts are removed by filtering and linear and non-linear features are extracted; (ii) feature scaling is done using a standard scalar while principal component analysis (PCA) is used for feature reduction; (iii) the linear, non-linear and combination of both (only for those whose accuracy is highest) are taken for further analysis where some ML and DL classifiers are applied for the classification of depression; and (iv) in this study, total 15 distinct ML and DL methods, including KNN, SVM, bagging SVM, RF, GB, Extreme Gradient Boosting, MNB, Adaboost, Bagging RF, BootAgg, Gaussian NB, RNN, 1DCNN, RBFNN and LSTM, that have been effectively utilized as classifiers to handle a variety of real-world issues.

1. Among all, alpha, alpha asymmetry, gamma and gamma asymmetry give the best results in linear features, while RWE, DFA, CD and AE give the best results in non-linear feature. 2. In the linear features, gamma and alpha asymmetry have given 99.98% accuracy for Bagging RF, while gamma asymmetry has given 99.98% accuracy for BootAgg. 3. For non-linear features, it has been shown 99.84% of accuracy for RWE and DFA in RF, 99.97% accuracy for DFA in XGBoost and 99.94% accuracy for RWE in BootAgg. 4. By using DL, in linear features, gamma asymmetry has given more than 96% accuracy in RNN and 91% accuracy in LSTM and for non-linear features, 89% accuracy has been achieved for CD and AE in LSTM. 5. By combining linear and non-linear features, the highest accuracy was achieved in Bagging RF (98.50%) gamma asymmetry + RWE. In DL, Alpha + RWE, Gamma asymmetry + CD and gamma asymmetry + RWE have achieved 98% accuracy in LSTM.

A novel dataset was collected from the Central Institute of Psychiatry (CIP), Ranchi which was recorded using a 128-channels whereas major previous studies used fewer channels; the details of the study participants are summarized and a model is developed for statistical analysis using N-way ANOVA; artifacts are removed by high and low pass filtering of epoch data followed by re-referencing and independent component analysis for noise removal; linear features, namely, band power and interhemispheric asymmetry and non-linear features, namely, relative wavelet energy, wavelet entropy, Approximate entropy, sample entropy, detrended fluctuation analysis and correlation dimension are extracted; this model utilizes Epoch (213,072) for 5 s EEG data, which allows the model to train for longer, thereby increasing the efficiency of classifiers. Features scaling is done using a standard scalar rather than normalization because it helps increase the accuracy of the models (especially for deep learning algorithms) while PCA is used for feature reduction; the linear, non-linear and combination of both features are taken for extensive analysis in conjunction with ML and DL classifiers for the classification of depression. The combination of linear and non-linear features (only for those whose accuracy is highest) is used for the best detection results.

The present paper explores and analyses various aspects of entrepreneurial marketing in the different regions (developed or developing) and attempts to consolidate the extant literature in the field of entrepreneurial marketing and suggests future directions for research.

The research questions developed by the paper deal with (1) comparison of entrepreneurial marketing in the developed and developing world; (2) methodological approaches used in entrepreneurial marketing; (3) the constructs or theories used in literature; (4) the existing research gaps and potential future directions in research of entrepreneurial marketing. To answer the same, we conduct a systematic literature review of the 82 research papers extracted from the Web of Science (WoS) and ScienceDirect databases.

The findings are presented in the form of descriptive and results. The descriptive findings show that more studies are needed in developing nations, introducing or developing entrepreneurial marketing conceptually, using mixed research designs, having objective measurements of constructs and contributing to comparative studies. The results discuss the constructs and theories employed in the extant literature and suggest that theories like human capital, creation, causation or trust are fundamental to study entrepreneurial marketing.

The paper adopts the existing entrepreneurial, marketing, innovation, and customer orientation (EMICO) framework and further develops an organizing framework to discover several gaps in the existing literature that can further be explored and promote the development of research in entrepreneurial marketing.

The majority of poor women in Africa live in rural areas, and investigating their empowerment status and factors influencing their empowerment is therefore a tool for overcoming poverty. This paper investigated the dimensions and determinants of women's empowerment in rural Nigeria.

This study used data from the 2013 Nigeria Demographic and Health Survey (NDHS). Information on women's agencies, resource, income, leadership and time/workload was used to construct women empowerment index (WEI). Data were analyzed using descriptive statistics and logit regression model.

Most of the decisions were made by the women's spouses, while decisions on how to spend her earnings were jointly made with her spouse. A majority of the women did not justify beating nor owned businesses. A larger percentage of rural women were disempowered than men; agency had the highest relative contribution to women's disempowerment; and women in the northern zones of Nigeria were less empowered than their southern counterparts. Husband's education and her age were inversely related to women's empowerments while her education, household size and being the household head were directly related to it.

There is a dearth of empirical studies on multidimensional women's empowerment in rural Nigeria. This study therefore provides a clear understanding of drivers of women's empowerment in rural Nigeria, and its findings are to serve as guiding documents for policymakers in designing gender-responsive interventions programs and implementation of a genuine gender mainstreaming in rural development policy in Nigeria. Further, the findings would contribute to the growing body of knowledge, especially empirical studies, on women's empowerment in Nigeria and the developing world.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-07-2019-0455

Today, as we hurtle towards imminent planetary destruction in the age of the Anthropocene, we believe it may be instructive to try and understand if the ancient science of spirituality can prove useful in humankind's ability to change course, even at this late hour. We argue that such a paradigm shift is critically essential for human survival and that without the inner transformation proposed by this science, it may prove impossible to build a society based on the principles of liberty, equality and fraternity. This chapter draws from foundational texts and authoritative sources across multiple religious traditions, based upon which it outlines a brief sketch of the ancient science of spirituality. We begin with an account of the differentia specifica of this science, where we delve into what kind of science this is. Since it is centrally concerned with inner transformation, we briefly outline the theory of change embedded in this science and the kind of rejuvenation it enables, which makes it possible for us to clearly perceive the key elements and the structure of reality. We then spell out the impact this has on the nature of human action, continually teasing out implications for policy and practice in our time. We provide a few concrete illustrations of the same. Inter alia, we also show how many of these insights can be found even within modern scientific and philosophical traditions, thereby indicating possibilities of convergence and synthesis between ancient and modern science, following thereby the guidance of genuine spirituality.