Kalpak K. Kulkarni, Arti D. Kalro and Dinesh Sharma
This study aims to investigate the influence of Big Five Personality traits (i.e. openness to experience, conscientiousness, extraversion, agreeableness and neuroticism) on young…
Abstract
Purpose
This study aims to investigate the influence of Big Five Personality traits (i.e. openness to experience, conscientiousness, extraversion, agreeableness and neuroticism) on young consumers’ intentions to share branded viral video advertisements. Further, this study also demonstrates that the advertising appeal (informational versus emotional) used in the viral advertisement moderates the effects of specific personality traits on the sharing of viral ads.
Design/methodology/approach
A conceptual framework is proposed based on the Five-Factor Model of Personality (McCrae and John, 1992) and advertising effectiveness literature. Using experiments, responses from young consumers were collected and hypotheses were tested using hierarchical regression and ANOVA.
Findings
Results reveal that the two personality traits, extraversion and openness to experiences, are positively associated with consumers’ viral ad sharing intentions, whereas conscientiousness, agreeableness and neuroticism are not. Moreover, individuals scoring high on openness and extraversion prefer sharing branded viral ads containing informational appeal vis-ã-vis those containing emotional appeals.
Originality/value
Studies decoding the factors behind the success of viral advertisements have more often focussed on the ad content rather than on personality dimensions of the ad sharers. This study bridges this gap by investigating the influence of Big Five Personality traits on young consumers’ intention to forward viral ads, in interaction with ad appeal. Young consumers represent key audience segments consuming and sharing viral content online, and hence, it is important to have a deeper understanding of this market segment.
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Prashant Sharma, Dinesh Kumar Sharma and Prashant Gupta
Option pricing theory enables computation of the price of an option using different variables associated with the underlying security and options contract. The purpose of this…
Abstract
Purpose
Option pricing theory enables computation of the price of an option using different variables associated with the underlying security and options contract. The purpose of this study is to assess research trends that emerged in the field of option pricing. This study reviews existing literature of the option pricing domain, both qualitatively and quantitatively, and identifies potential themes for future research.
Design/methodology/approach
This study adopts bibliometric analysis method to explore literature published in the option pricing domain. As part of bibliometric analysis, this study considers both descriptive and network analysis to assess publication trends. For descriptive analysis, the “bibliometrix” package proposed by Aria and Cuccurullo (2017) is used and for network analysis, VOS viewer (Van Eck and Waltman, 2017) and Gephi (Bastian et al., 2009) are used.
Findings
This study identifies research trends, top researchers, articles, journals and contributions from institutions and countries in the option pricing domain. It identifies four clusters that show different directions and also focuses on past studies on the same subject. It explores research gaps by performing an in-depth analysis of existing literature on option pricing and suggests the way forward for research in this area.
Originality/value
To the best of the authors’ knowledge, no previous studies have attempted to analyze the literature published in the option pricing domain. This study fulfils this research gap by conducting a comprehensive analysis of studies in the option pricing area. This study identifies quality research work published in the domain, research trends, contribution by most relevant researchers, contributions across geographies and institutions and the connections among these aspects. This study also identifies important themes and provides directions for future research.
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Sunny Vijay Arora, Arti D. Kalro and Dinesh Sharma
Managers prefer semantic imbeds in brand names, but extant literature has primarily studied fictitious names for their sound-symbolic perceptions. This paper aims to explore…
Abstract
Purpose
Managers prefer semantic imbeds in brand names, but extant literature has primarily studied fictitious names for their sound-symbolic perceptions. This paper aims to explore sound-symbolic perceptions of products with blended brand names (BBNs), formed with at least one semantic and one nonsemantic component. Unlike most extant literature, this study not only estimates the effect of vowels and consonants individually on product perceptions but also of their combinations. The boundary condition for this effect is examined by classifying products by their categorization and attributes by their abstractness.
Design/methodology/approach
Through a within-subject experiment, this paper tested perceptions of products with BBNs having high-/low-frequency sounds. A mixed-design experiment followed with sound frequency, product-level categorization and attributes’ abstractness as predictor variables.
Findings
For BBNs, vowel sounds convey brand meaning better than the combinations of vowel and consonant sounds – and these convey brand meaning better than consonant sounds. Differences in consumers’ perceptions of products with BBNs occur when the degree of attributes’ abstractness matches product-level categorization, such as when concrete attributes match subordinate-level categorization.
Practical implications
Brand managers/strategists can communicate product positioning (attribute-based) through BBNs created specifically for product categories and product types.
Originality/value
This research presents a comparative analysis across vowels, consonants and their combinations on consumers’ perceptions of products with BBNs. Manipulation of names’ length and position of the sound-symbolic imbed in the BBN proffered additional contributions. Another novelty is the interaction effect of product categorization levels and attributes’ abstractness on sound-symbolic perception.
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Preeti Virdi, Arti D. Kalro and Dinesh Sharma
Decision aids (DAs) in online retail stores ease consumers' information processing. However, online consumers do not use all decision aids in purchase decision-making. While the…
Abstract
Purpose
Decision aids (DAs) in online retail stores ease consumers' information processing. However, online consumers do not use all decision aids in purchase decision-making. While the literature has documented the effects of individual decision aids or two decision aids at a time, no study has compared the efficacy of multiple decision aids simultaneously. Also, very few studies have looked at the use of decision aids for consumers with maximizing and satisficing tendencies. Hence, this study aims to understand the preferences of maximizers and satisficers towards online decision aids during the choice-making process.
Design/methodology/approach
This is an observational study with 60 individuals who were asked to purchase either a search-based or an experience-based product online. Participants' browsing actions and verbalizations during online shopping, were recorded and analysed using NVivo, and later the use of decision aids was mapped along their choice process.
Findings
Consumer's preference of decision aids varies across the two stages of the choice process (that is, consideration set formation and evaluation & choice). In their choice formation, maximizers use different decision aids in both stages, that is, filter tool and in-website search tool for search products, and collaborative filtering-based recommender systems and eWOM for experience products. Satisficers used more decision aids as compared to maximizers across the two stages for both product types.
Originality/value
This study is an exploratory attempt to understand how consumers use multiple decision aids present on e-commerce websites.
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Rabin K. Jana, Dinesh K. Sharma and Subrata Kumar Mitra
The purpose of this paper is to offer improvement in routing and collection load decisions for a green logistics system that delivers lunch boxes.
Abstract
Purpose
The purpose of this paper is to offer improvement in routing and collection load decisions for a green logistics system that delivers lunch boxes.
Design/methodology/approach
A mathematical model is introduced into the literature for the 130 years old logistics systems whose delivery accuracy is better than the Six Sigma standard without using sophisticated tools. A simulated annealing (SA) approach is then used to find the routing and collection load decisions for the lunch box career.
Findings
The findings establish that we can improve the world-class lunch box delivery (LBD) system. The suggested improvement in terms of reduction in distance travel is nearly 6%. This could be a huge relief for thousands of lunch box careers. The uniformity in collection load decisions suggested by the proposed approach can be more effective for the elderly lunch box carriers.
Research limitations/implications
The research provides a mathematical framework to study an important logistics system that is running with a supreme level of service accuracy. Collecting primary data was challenging as there is no scope for recording and maintaining data in the present logistics system. The replicability of the system for some other city in the world is a challenging question to answer.
Practical implications
Better routing and collection load decisions can help many lunch box careers save time and bring homogeneity in workload into the system.
Social implications
An efficient routing decision can help provide smoother traffic movements, and uniformity in collection load can help avoid unwanted injuries to about 5,000 lunch box careers.
Originality/value
The originality of this paper lies in the proposed mathematical model and finding the routing and collection load decisions using a nature-inspired probabilistic search technique. The LBD system of Mumbai was never studied mathematically. The study is the first of its kind.
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Vartika Chaudhary, Dinesh Sharma, Anish Nagpal and Arti D. Kalro
This paper aims to examine the effect of three types of health-related claims (health, nutrition and ingredient) and product healthiness on situational skepticism toward the…
Abstract
Purpose
This paper aims to examine the effect of three types of health-related claims (health, nutrition and ingredient) and product healthiness on situational skepticism toward the claims that appear on the front-of-package of food products. The effect of situational skepticism on the purchase intention of the product is further examined.
Design/methodology/approach
Two experimental studies were conducted with a 3 (health-related claims: health claim vs nutrition claim vs ingredient claim) × 2 (product healthiness: healthy vs unhealthy) between-subjects factorial design. Study 1 investigates the effects within a single product category (Biscuits) and Study 2 the effects across product categories (Salad and Pizza).
Findings
The results demonstrate that situational skepticism is the highest for health claims, followed by nutrition claims and the least for ingredient claims. In addition, situational skepticism is higher for claims appearing on unhealthy products vis-à-vis healthy ones. Finally, situational skepticism mediates the relationship between claim type, product healthiness and product purchase intention.
Research limitations/implications
This study contributes to the field of nutrition labeling by advancing research on information processing of nutrition labels through the lens of the persuasion knowledge model (Friestad and Wright, 1994). Specifically, this study contributes to a nuanced understanding of claim formats on how the language properties of the claim – its vagueness, specificity and verifiability – can affect consumer perception. This study finds that higher specificity, verifiability and lower vagueness of ingredient claims lead to lower skepticism and hence higher purchase intention.
Practical implications
Furthermore, this study incrementally contributes to the ongoing discussion about the claim–carrier combination by showing that health-related claims are better perceived on healthy compared to unhealthy products. Hence, managers should avoid health washing, as this can backfire and cause harm to the reputation of the firm.
Social implications
From a public policy point of view, this study makes a case for strong monitoring and regulations of ingredient claims, as consumers believe these claims easily and hence can be misled by false ingredient claims made by unethical marketers.
Originality/value
The scope of research on skepticism has largely been limited to examining a general individual tendency of being suspicious (i.e. dispositional skepticism) in health-related claims as well as other areas of marketing. In this research, the authors extend the scope by examining how specific types of claims (health vs nutrition vs ingredient) and product healthiness jointly impact consumer skepticism, i.e. situational skepticism.
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Preeti Virdi, Arti D. Kalro and Dinesh Sharma
Collaborative filtering based recommender systems (CF–RS) are widely used to recommend products based on consumers' preference similarity. Recommendations by CF–RS merely provide…
Abstract
Purpose
Collaborative filtering based recommender systems (CF–RS) are widely used to recommend products based on consumers' preference similarity. Recommendations by CF–RS merely provide suggestions as “people who bought this also bought this” while, consumers are unaware about the source of these recommendations. By amalgamating CF–RS with consumers' social network information, e-commerce sites can offer recommendation from social networks of consumers. These social network embedded systems are known as social recommender systems (SRS). The extant literature has researched on the algorithms and implementation of these systems; however, SRS have not been understood from consumers' psychological perspective. This study aims to qualitatively explore consumers' motives to accept SRS in e-commerce websites.
Design/methodology/approach
This qualitative study is based on in-depth interviews of frequent online shoppers. SRS are currently not very widespread in the Indian e-commerce space; hence, a vignette was shown to respondents before they responded to the questions. Inductive qualitative content analysis method was used to analyse these interviews.
Findings
Three main themes (social-gratification, self-gratification and information-gratification) emerged from the analysis. Out of these, social-gratification acts as an enabler, while self-gratification along with some elements of information-gratification act as inhibitors towards acceptance of social recommendations. Based on these gratifications, we present a conceptual model on consumer's acceptance of social recommendations.
Originality/value
This study is an initial attempt to qualitatively understand consumers' attitudes and acceptance of social recommendations on e-commerce websites, which in itself is a fairly new phenomenon.
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Indranil Ghosh, Rabin K. Jana and Dinesh K. Sharma
Owing to highly volatile and chaotic external events, predicting future movements of cryptocurrencies is a challenging task. This paper advances a granular hybrid predictive…
Abstract
Purpose
Owing to highly volatile and chaotic external events, predicting future movements of cryptocurrencies is a challenging task. This paper advances a granular hybrid predictive modeling framework for predicting the future figures of Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Stellar (XLM) and Tether (USDT) during normal and pandemic regimes.
Design/methodology/approach
Initially, the major temporal characteristics of the price series are examined. In the second stage, ensemble empirical mode decomposition (EEMD) and maximal overlap discrete wavelet transformation (MODWT) are used to decompose the original time series into two distinct sets of granular subseries. In the third stage, long- and short-term memory network (LSTM) and extreme gradient boosting (XGB) are applied to the decomposed subseries to estimate the initial forecasts. Lastly, sequential quadratic programming (SQP) is used to fetch the forecast by combining the initial forecasts.
Findings
Rigorous performance assessment and the outcome of the Diebold-Mariano’s pairwise statistical test demonstrate the efficacy of the suggested predictive framework. The framework yields commendable predictive performance during the COVID-19 pandemic timeline explicitly as well. Future trends of BTC and ETH are found to be relatively easier to predict, while USDT is relatively difficult to predict.
Originality/value
The robustness of the proposed framework can be leveraged for practical trading and managing investment in crypto market. Empirical properties of the temporal dynamics of chosen cryptocurrencies provide deeper insights.
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S.P. Sharma, Dinesh Kumar and Komal
The purpose of this paper is to present a hybridized technique for analyzing the stochastic behavior of an industrial system. The feeding system of a paper mill situated in North…
Abstract
Purpose
The purpose of this paper is to present a hybridized technique for analyzing the stochastic behavior of an industrial system. The feeding system of a paper mill situated in North India producing 200 tons of paper per day has been considered for analysis and efforts have been made to incorporate vague, ambiguous, imprecise and conflicting information quantified by fuzzy numbers.
Design/methodology/approach
In this paper, three important tools namely, fuzzy analysis, neural network and genetic algorithms (GAs), are used to built a hybridized and more realistic technique herein named as, neural network and GAs‐based Lambda‐Tau (NGABLT). The technique will facilitate the maintenance personnel in making a better decision. This technique has been demonstrated by computing some of the reliability indices of the considered system.
Findings
The results indicate that NGABLT technique reduces the gap between crisp and existing Lambda‐Tau results, i.e. it may be a more useful tool to assess the current system condition and suggests to improve the system reliability and availability.
Originality/value
The authors have suggested a hybridized technique for analyzing the stochastic behavior of the feeding system in a paper mill by computing fuzzy reliability indices.
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Dinesh Sharma, B.S. Sahay and Amit Sachan
Previous research in the area of distributor performance proposed different scales, mostly in western, developed country context. These studies also lacked the consideration of…
Abstract
Previous research in the area of distributor performance proposed different scales, mostly in western, developed country context. These studies also lacked the consideration of dynamic interaction between variables, which determine the distributor’s performance. This paper proposes a composite Distributor Performance Index (DPI) to evaluate distributors’ performance based on at the “Enables” and “Results”, taking a system dynamics approach. The model results have been discussed and validated, in business marketing channel. The context of this study is India, an emerging market.