Smartphones nowadays have become the most globally demanded electronic devices. Each year, almost thousands of new smartphone models are launched by different brands having diverse specifications and variable price range. Due to the availability of large smartphone quantity in the market, sometimes the buyers get confused about the most appropriate model to buy at low price with respect to descent hardware specifications. This research article states such a type of decision problem and solves it using multi-criteria decision-making techniques that enable the mobile users to select the best smartphone model. The main objective of the study is to choose the best smartphone model among 10 selected models available in the market with the help of fuzzy analytical hierarchy process (FAHP) and preference ranking organization method for enrichment of evaluations (PROMETHEE) where FAHP based on trapezoidal fuzzy numbers is used to evaluate the criteria weights and PROMETHEE is used to propose the preference ranking order of the alternatives. Seven important specifications (i.e., criteria), namely price, storage capacity, RAM, battery, screen size, camera and clock speed, are considered based on which the selection procedure is carried out. PROMETHEE I provides the partial alternatives ranking and shows the mode of preference of each model over other models, whereas PROMETHEE II delivers the complete ranking of the alternatives which recommends Realme 5i and Samsung J6 Plus as the best and worst model, respectively, among the group.

Customer relationship management (CRM) is the tool to enhance customer relationship in any business. Due to the exponential growth of data volume, in any field, it is significant to develop new techniques to discover the customer knowledge, automation of the system and moreover customer satisfaction to win customer lifetime value. CRM with machine learning could bring a catalytic change in business. Several supervised and unsupervised machine learning techniques are utilized to improve the customer experience and profitability of business. This paper reviews the available literature on the CRM with machine learning techniques for customer identification, customer attraction, and customer retention and customer development. This study reveals that supervised learning techniques are 48.48% utilized, unsupervised learning techniques are utilized 15.15%, and 9.09% utilized other techniques in CRM. Paradigm is also shifted toward the deep learning from machine learning as 28.28% text has been reported to deep learning. Decision tree-based algorithm and support vector machine algorithms are most utilized algorithm of supervised learning. E-commerce and telecommunication sectors are the most important areas identified with the exponential growth of the users and hence need a suitable machine learning techniques for customer satisfaction and business profitability.

Gender-based segmentation is frequently used by marketers to cater to different needs and preferences of men and women. However, the literature in consumer behaviour has not explored enough the role of gender in decision-making. Specifically, there are hardly any studies that investigate the decision-making styles of Indian male and female consumers. Based on gender and sociocultural theories, this study develops and tests hypotheses on gender-based differences in decision-making styles, including hedonism, novelty consciousness, price value consciousness and brand loyalty orientation. Analysis of data obtained from 355 male and 203 female students through self-administered questionnaire indicates that women score higher than men on hedonism, novelty orientation and price value consciousness. No significant difference was found on the dimension of brand loyalty. The study contributes to the consumer behaviour research by highlighting the differences in decision-making styles of Indian millennial men and women. The results can help marketers to employ gender-based segmentation and target more effectively. The study provides insights that firms can use in framing product, pricing and communication strategies catering to the requirements of a specific gender.

Delay discounting behavior has proven useful in assessing impulsivity across a wide range of populations. As such, accurate estimation of the shape of each individual's temporal discounting profile is paramount when drawing conclusions about how impulsivity relates to clinical and health outcomes such as gambling, addiction, and obesity. Here, we identify an estimation problem with current methods of assessing temporal discounting behavior, and propose a simple solution. First, through a simulation study we identify types of temporal discounting profiles that cannot reliably be estimated. Second, we show how imposing constraints through hierarchical modeling ameliorates these recovery problems. Finally, we apply our solution to a large data set from a temporal discounting task, and illustrate the importance of reliable estimation within patient populations. We conclude with a brief discussion on how hierarchical Bayesian methods can aid in model estimation, compensate for small samples, and improve predictions of externalizing psychopathology.

Farmers in the coastal zone of West Bengal depend mainly on agriculture but constrained with low productivity and hence low income. Productivity constraints attributed to prolonged water logging during and after the wet (kharif) season, soil salinity and the scarcity of low saline irrigation water as the dry (rabi) season progresses. Level of cropping intensification is very low (123%). Experiments were conducted during 2016–2019 at farmers’ field (90 farmers) in this coastal saline zone for finding suitable cropping intensification options. Several new cropping systems have been found feasible that can increase the cropping intensity to 200–300% while providing better income (2–5 times as compared to baseline). Besides, for better utilisation of scarce freshwater resource the vegetable–vegetable–vegetable cropping system was evolved successfully through solar powered drip irrigation system and low-cost drip irrigation system. Such cropping systems provided higher profitability as compared to the existing farming practices. The paper also highlighted various determinants that may influence the adoption of these new cropping systems by the farmers and the possible strategic options therein.

The present study made an attempt to investigate the fiscal deficit–economic growth nexus in India during 1980–1981 to 2016–2017 in the presence of gross domestic capital formation (GDCF), exchange rate, and total revenue. For empirical investigation, the study has employed auto-regressive distributed lag (ARDL) model. The empirical findings of the ARDL model provide evidence that the linear model for India’s fiscal deficit, capital formation, and economic growth dynamics would be misspecification of the model. Therefore, this study has used nonlinear auto-regressive distributed lag (NARDL) bounds testing of cointegration proposed by Shin et al. (in: Festschrift in honor of Peter Schmidt, Springer, New York, 2014) to investigate the long-run and short-run interactions between the variables. The NARDL results found the evidence of asymmetric association between fiscal deficit and GDP and GDCF and GDP in the long-term and short-term time sphere. The obtained results indicate that fiscal deficit has adverse impact on GDP, whereas GDCF has positive influence on GDP in India. This finding supports neoclassical theory regarding the nexus between fiscal deficit and economic growth. The study has suggested that, as per the “golden rule” of public finance, the amount of the fiscal deficit should be spent on capital formation in order to have a long-term multiplier effect on growth.