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The paper aims to investigate the influence of the angle of inclination on mixed convection heat transfer from rectangular plated shrouded fin array computationally. This study has got applications in the various thermal field such as cooling, solar thermal and so on.

A computational study is made to evaluate the thermal performance in an inclined channel.

Increase in clearance from 0.01 to 0.25 results in an increase of local Nusselt number by is as high as 15% near the exit. At a higher value of Gr with an increase in C* from 0.10, Nu is found to increase by 5.5%. Increase in Gr by 1.37 times results in enhancement of Nu by a maximum of 25-30%. Around 10% increase in overall Nu value is observed with an increase in inclination (i.e. from 30° to 60°).

This study has got applications in the various thermal field such as cooling, solar thermal and so on.

Entry region mixed convection in a shrouded inclined finned channel is performed.

The purpose of this paper is to determine the triggers of positive electronic word of mouth (eWOM) using real-time Big Data obtained from online retail sites/dedicated review sites.

In this study, real-time Big Data has been used and analysed through support vector machine, to segregate positive and negative eWOM. Thereafter, using natural language processing algorithms, this study has classified the triggers of positive eWOM based on their relative importance across six product categories.

The most important triggers of positive eWOM (like product experience, product type, product characteristics) were similar across different product categories. The second-level antecedents of positive eWOM included the person(s) for whom the product is purchased, the price and the source of the product, packaging and eagerness in patronising a brand.

The findings of this study indicate that the marketers who are active in the digital forum should encourage and incentivise their satisfied consumers to disseminate positive eWOM. Consumers with special interest for any product type (mothers or doctors for baby food) may be incentivised to write positive eWOM about the product’s ingredients/characteristics. Companies can launch the sequels of existing television or online advertisements addressing “for whom the product is purchased”.

This study identified the triggers of the positive eWOM using real-time Big Data extracted from online purchase platforms. This study also contributes to the literature by identifying the levels of triggers that are most, more and moderately important to the customers for writing positive reviews online.

Humanoids have become the center of attraction for many researchers dealing with robotics investigations by their ability to replace human efforts in critical interventions. As a result, navigation and path planning has emerged as one of the most promising area of research for humanoid models. In this paper, a fuzzy logic controller hybridized with genetic algorithm (GA) has been proposed for path planning of a humanoid robot to avoid obstacles present in a cluttered environment and reach the target location successfully. The paper aims to discuss these issues.

Here, sensor outputs for nearest obstacle distances and bearing angle of the humanoid are first fed as inputs to the fuzzy logic controller, and first turning angle (TA) is obtained as an intermediate output. In the second step, the first TA derived from the fuzzy logic controller is again supplied to the GA controller along with other inputs and second TA is obtained as the final output. The developed hybrid controller has been tested in a V-REP simulation platform, and the simulation results are verified in an experimental setup.

By implementation of the proposed hybrid controller, the humanoid has reached its defined target position successfully by avoiding the obstacles present in the arena both in simulation and experimental platforms. The results obtained from simulation and experimental platforms are compared in terms of path length and time taken with each other, and close agreements have been observed with minimal percentage of errors.

Humanoids are considered more efficient than their wheeled robotic forms by their ability to mimic human behavior. The current research deals with the development of a novel hybrid controller considering fuzzy logic and GA for navigational analysis of a humanoid robot. The developed control scheme has been tested in both simulation and real-time environments and proper agreements have been found between the results obtained from them. The proposed approach can also be applied to other humanoid forms and the technique can serve as a pioneer art in humanoid navigation.

To navigate humanoid robots in complex arenas, a significant level of intelligence is required which needs proper integration of computational intelligence with the robot's controller. This paper describes the use of a combination of genetic algorithm and neural network for navigational control of a humanoid robot in given cluttered environments.

The experimental work involved in the current study has been done by a NAO humanoid robot in laboratory conditions and simulation work has been done by the help of V-REP software. Here, a genetic algorithm controller is first used to generate an initial turning angle for the robot and then the genetic algorithm controller is hybridized with a neural network controller to generate the final turning angle.

From the simulation and experimental results, satisfactory agreements have been observed in terms of navigational parameters with minimal error limits that justify the proper working of the proposed hybrid controller.

With a lack of sufficient literature on humanoid navigation, the proposed hybrid controller is supposed to act as a guiding way towards the design and development of more robust controllers in the near future.

This study aims to investigate the impact of conspicuous consumption on the subjective well-being (SWB) of Indian bottom of the pyramid (BOP) consumers.

This study followed a qualitative phenomenological approach. Focus group discussions were conducted with 72 Indian BOP consumers. Thematic analysis resulted in 21 categories and 9 themes.

This study revealed three distinct categories of consequences related to the effects of conspicuous consumption on SWB at the BOP. Conspicuous consumption can either enhance or diminish the BOP consumers’ SWB and sometimes even simultaneously have constructive and adverse effects (a double-edged sword). This study presents a conceptual model examining the consequences of conspicuous consumption at the BOP through the lenses of social comparison and compensatory consumption theories.

The literature has classified the consequences of conspicuous consumption at the BOP as constructive or adverse effects. This study reveals that conspicuous consumption at the BOP can be a double-edged sword. This study indicates that social comparison drives compensatory consumption at the BOP, which impacts the SWB of BOP consumers. Such an amalgamation of the theories of conspicuous consumption, social comparison and compensatory consumption is the unique contribution of this research. The implications for practice and policy are discussed.

The purpose of this paper is to explore the impact of retailer personality on consumer‐based retailer equity.

The authors developed a retailer personality scale and find its impact on consumer‐based retailer equity by adopting the scale developed by Pappu and Quester. A mall‐intercept survey was undertaken using a systematic sampling of department store shoppers of age 18 years and above in a metropolitan city, Kolkata, India. The questionnaire was used to collect data from seven department retail brands. The impact of each retailer personality dimension on each consumer‐based retailer equity dimension was explored, using structural equation modeling.

The study proposed a five‐dimensional scale to measure department store personality. Results indicated that the three dimensions of store personality, namely sophistication, dependability and empathy, have significant positive impact on each consumer‐based retailer equity dimension except one (empathy→retailer loyalty). The remaining two dimensions of retailer personality, namely authenticity and vibrancy, have no impact on each consumer‐based retailer dimension.

The paper is the first to propose a scale for measuring department store personality and to explore the link between retailer personality and consumer‐based retailer equity.

Product returns are a major challenge for e-businesses as they involve huge logistical and operational costs. Therefore, it becomes crucial to predict returns in advance. This study aims to evaluate different genres of classifiers for product return chance prediction, and further optimizes the best performing model.

An e-commerce data set having categorical type attributes has been used for this study. Feature selection based on chi-square provides a selective features-set which is used as inputs for model building. Predictive models are attempted using individual classifiers, ensemble models and deep neural networks. For performance evaluation, 75:25 train/test split and 10-fold cross-validation strategies are used. To improve the predictability of the best performing classifier, hyperparameter tuning is performed using different optimization methods such as, random search, grid search, Bayesian approach and evolutionary models (genetic algorithm, differential evolution and particle swarm optimization).

A comparison of F1-scores revealed that the Bayesian approach outperformed all other optimization approaches in terms of accuracy. The predictability of the Bayesian-optimized model is further compared with that of other classifiers using experimental analysis. The Bayesian-optimized XGBoost model possessed superior performance, with accuracies of 77.80% and 70.35% for holdout and 10-fold cross-validation methods, respectively.

Given the anonymized data, the effects of individual attributes on outcomes could not be investigated in detail. The Bayesian-optimized predictive model may be used in decision support systems, enabling real-time prediction of returns and the implementation of preventive measures.

There are very few reported studies on predicting the chance of order return in e-businesses. To the best of the authors’ knowledge, this study is the first to compare different optimization methods and classifiers, demonstrating the superiority of the Bayesian-optimized XGBoost classification model for returns prediction.

The purpose of this paper is to examine the factors affecting decision-making at the BoP and propose a framework of the consumer decision-making process at the base of the pyramid (BoP).

The qualitative research design was adopted to collect primary data. Data collected through the in-depth semi-structured interviews of the 48 BoP consumers in Kolkata and Kharagpur (India) were subjected to grounded theory analysis for theory development.

This study reveals that consumer vulnerability affects the decision-making process and the transaction cost at BoP level. It was observed that the consumers at the BoP make constrained choices due to their vulnerability and try to minimise transaction cost while selecting the retails. The constrained retail choice leads to a limited selection of products and brands.

This study, for the first time, has investigated the decision-making process for BoP consumers in detail. As a pioneering attempt, it sheds light on some new factors including consumer vulnerability and transaction cost that have an impact on the consumer decision-making process.

The purpose of this paper is to examine the relationship between financial development and economic growth for five major emerging economies: Brazil, Russia, India, China and South (BRICS) during 1993 to 2014 using banking sector and stock market development indicators.

To begin with, the study first examined some of the principal indicators of financial development and macroeconomic variables of the selected economies. Next, using generalized method of moment system estimation (SYS-GMM), the relationship between financial development and growth is investigated. The banking sector development indicators used in the study include size of the financial intermediaries, credit to deposit ratio (CDR) and domestic credit to private sector (CPS), whereas the stock market development indicators are value of shares traded and turnover ratio. Also, some macroeconomic control variables such as inflation, exports and the enrolment in secondary education were used.

The examination of the principal indicators of financial development and macroeconomic variables have shown considerable differences between the selected economies. Results from the dynamic one-step SYS-GMM estimates confirm that in presence of turnover ratio, all the selected banking development indicators such as size of financial intermediaries, CDR and CPS are positively significantly determining economic growth. Similarly, in presence of all the selected banking sector development indicators, value of shares traded is found to be positively significantly associated with economic growth. However, the same is not true when turnover ratio is regressed in presence of banking sector variables. Overall, the evidence suggests that banking sector development and stock market development indicators are complementary to each other in stimulating economic growth.

A positive association between financial development and growth indicates that the policymakers should take necessary measures toward simultaneous development of both banking sector as well as stock market for inducing growth.

The present paper attempts to examine the relationship between financial development and growth using both banking sector and stock market development indicators which has not been attempted before for BRICS. Also, most of the existing studies are found in case of developed economies. This paper tries to fill this void by studying five major emerging economies.

With enhanced use of humanoids in demanding sectors of industrial automation and smart manufacturing, navigation and path planning of humanoid forms have become the centre of attraction for robotics practitioners. This paper aims to focus on the development and implementation of a hybrid intelligent methodology to generate an optimal path for humanoid robots using regression analysis, adaptive particle swarm optimization and adaptive ant colony optimization techniques.

Sensory information regarding obstacle distances are fed to the regression controller, and an interim turning angle is obtained as the initial output. Adaptive particle swarm optimization technique is used to tune the governing parameter of adaptive ant colony optimization technique. The final output is generated by using the initial output of regression controller and tuned parameter from adaptive particle swarm optimization as inputs to the adaptive ant colony optimization technique along with other regular inputs. The final turning angle calculated from the hybrid controller is subsequently used by the humanoids to negotiate with obstacles present in the environment.

As the current investigation deals with the navigational analysis of single as well as multiple humanoids, a Petri-Net model has been combined with the proposed hybrid controller to avoid inter-collision that may happen in navigation of multiple humanoids. The hybridized controller is tested in simulation and experimental platforms with comparison of navigational parameters. The results obtained from both the platforms are found to be in coherence with each other. Finally, an assessment of the current technique with other existing navigational model reveals a performance improvement.

The proposed hybrid controller provides satisfactory results for navigational analysis of single as well as multiple humanoids. However, the developed hybrid scheme can also be attempted with use of other smart algorithms.

Humanoid navigation is the present talk of the town, as its use is widespread to multiple sectors such as industrial automation, medical assistance, manufacturing sectors and entertainment. It can also be used in space and defence applications.

This approach towards path planning can be very much helpful for navigating multiple forms of humanoids to assist in daily life needs of older adults and can also be a friendly tool for children.

Humanoid navigation has always been tricky and challenging. In the current work, a novel hybrid methodology of navigational analysis has been proposed for single and multiple humanoid robots, which is rarely reported in the existing literature. The developed navigational plan is verified through testing in simulation and experimental platforms. The results obtained from both the platforms are assessed against each other in terms of selected navigational parameters with observation of minimal error limits and close agreement. Finally, the proposed hybrid scheme is also evaluated against other existing navigational models, and significant performance improvements have been observed.