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The purpose of this study/paper is conducting a Systematic mapping review, as a systematic literature review method for reviewing the literature of new product development by textmining and mapping the results of this review.

This research has been conducted with the aim of systematically reviewing the literature on the field of design and development of products based on textual data. This research wants to know, how text data and text mining methods, can use for the design and development of new products.

This review finds out what are the most popular algorithms in this field? What are the most popular areas in using these approaches? What types of data are used in this area? What software is used in this regard? And what are the research gaps in this area?

The contribution of this review is creating a macro and comprehensive map for research in this field of study from various aspects and identifying the pros and cons of this field of study by systematic mapping review.

This paper aims to study the impact of manufacturer’s upgrading strategy of durable products on the retailer’s decision on trade-in program and her decision on the secondary market.

This paper develops a channel that consists of a manufacturer and a retailer, where the manufacturer releases an upgraded product, and the retailer introduces a trade-in program for consumers, simultaneously, decides whether to enter the secondary market. These approaches are modeled through Stackelberg game.

This paper reveals that the optimal conditions for manufacturer to release upgraded products and retailer to resell used products in the secondary market, and it reveals that under what conditions it is profitable for retailer to enter the secondary market under product upgrade levels.

If the manufacturer’s upgrade level is low, it is profitable for the retailer to enter the secondary market. However, if the manufacturer’s upgrade level is high, it is unprofitable for the retailer to enter the secondary market.

In this paper, the active secondary market, upgrading of new products, consumer market segmentation and especially, the upgrade degree of new products as a function of consumer demand are considered simultaneously.

In this context, the aim of the present research is to examine what factors determine that consumers restrain from shopping used products through the Internet. So, this research aims to analyze what makes consumers prevent from shopping second-hand products online.

For this purpose, the authors propose and empirically test a conceptual model of the barriers towards online second-hand shopping behavior. Drawing on a sample of 405 consumers data were analyzed through structural equation modeling (SEM).

The findings reveal that contamination effects and the lack of trust towards the online store, followed by the low perceived product reliability and the poor product perceived quality prevent consumers from shopping used products online. Conversely, consumer embarrassment for shopping second-hand products and the purchase uncertainty do not influence consumers' second-hand shopping behavior.

This study contributes to the marketing literature on second-hand shopping, being an attempt to explore the factors that prevent consumers from purchasing used products through the Internet.

The purpose of this study is to develop a novel general mathematical model to find the optimal product mix of commercial graphite products, which has a complex production process with alternative sub-processes in the graphite mining production process.

The network optimization was adopted to model the complex graphite mining production process through the optimal allocation of raw graphite, byproducts, and saleable products with comparable sub-processes, which has different processing capacities and costs. The model was tested on a selected graphite manufacturing company, and the optimal graphite product mix was determined through the selection of the optimal production process. In addition, sensitivity and scenario analyses were carried out to accommodate uncertainties and to facilitate further managerial decisions.

The selected graphite mining company mines approximately 400 metric tons of raw graphite per month to produce ten types of graphite products. According to the optimum solution obtained, the company should produce only six graphite products to maximize its total profit. In addition, the study demonstrated how to reveal optimum managerial decisions based on optimum solutions.

This study has made a significant contribution to the graphite manufacturing industry by modeling the complex graphite mining production process with a network optimization technique that has yet to be addressed at this level of detail. The sensitivity and scenario analyses support for further managerial decisions.

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.

This article explores how companies in multiple controversial industries report their controversial issues. For the first time, the authors use a new conceptualization of controversial industries, focused on harm and solutions, to investigate the reports of 28 companies in seven controversial industries: Agricultural Chemicals, Alcohol, Armaments, Coal, Gambling, Oil and Tobacco.

The authors thematically analyzed company reports to determine if companies in controversial industries discuss their controversial issues in their reporting, if and how they communicate the harm caused by their products or services, and what solutions they provide.

From this study data the authors introduce a new legitimacy reporting method in the controversial industries literature: the solutions companies offer for the harm caused by their products and services. The authors find three solution reporting methods: no solution, misleading solution and less-harmful solution. The authors also develop a new typology of reporting strategies used by companies in controversial industries based on how they report their key controversial issue and the harm caused by their products or services, and the solutions they offer. The authors identify seven reporting strategies: Ignore, Deny, Decoy, Dazzle, Distort, Deflect and Adapt.

Further research can test the typology and identify strategies used by companies in different institutional or regulatory settings, across different controversial industries or in larger populations.

Investors, consumers, managers, activists and other stakeholders of controversial companies can use this typology to identify the strategies that companies use to report controversial issues. They can assess if reports admit to the controversial issue and the harm caused by a company's products and services and if they provide solutions to that harm.

This paper develops a new typology of reporting strategies by companies in controversial industries and adds to the theory and discourse on social and environmental reporting (SER) as well as the literature on controversial industries.

The purpose of this study is to investigate the impact of greening and promotional effort dependent stochastic market demand on the remanufacturer's and the collector's profits when the quality of used products for remanufacturing is uncertain in a reverse supply chain.

The proposed model is developed to obtain optimal profits for the remanufacturer, the collector and the whole supply chain. Both the centralized and decentralized scenarios are considered. To motivate the collector through profit enhancement, the remanufacturer designs a cost-sharing contract. Through numerical examples and sensitivity analysis, the consequences of greenness and promotional effort on optimal profits are investigated.

The results show that the remanufacturer gets benefited from greening and promotional effort enhancement. However, a higher value of minimum acceptable quality level decreases the profits of the manufacturer and the collector. A cost-sharing contract coordinates the supply chain and improves the remanufacturer's and the collector's profits. Besides green innovation, remanufacturing mitigates the harmful effects of waste in the environment.

Two different viewpoints of remanufacturing are considered here – environmental sustainability and economic sustainability. This paper considers a reverse supply chain with a remanufacturer who remanufactures the used products collected by the collector. The quality of used products is uncertain, and customer demand is stochastic, green and promotional effort sensitive. These two types of uncertainty with green and promotional effort sensitive customer demand differs the current paper from the existing literature.

This paper aims to present a case study of virtual-reality-based product demonstrations featuring items of furniture. The results will be of use in further design and development of virtual-reality-based product demonstration systems and could also support effective student learning.

A new method was introduced to guide the experiment by confirming orthogonal arrays. User interactions were then planned, and a furniture demonstration system was implemented. The experiment comprised two stages. In the evaluation stage, participants were invited to experience the virtual-reality (VR)-based furniture demonstration system and complete a user experience (UX) survey. Taguchi-style robust design methods were used to design orthogonal table experiments and planning and design operation methods were used to implement an experimental display system in order to obtain optimized combinations of control factors and levels. The second stage involved a confirmatory test for the optimized combinations. A pilot questionnaire was first applied to survey demonstration scenarios that are important to customers.

The author found in terms of furniture products, product interactive display through VR can achieve good user satisfaction through quality design planning. VR can better grasp the characteristics of products than paper catalogs and website catalogs. And VR can better grasp the characteristics of products than online videos. For “interactive inspection”, “function simulation”, “style customization” and “set-out customization” were the most valuable demonstration scenarios for customers. The results of the experiment confirmed that the “overall rating”, “hedonic appeal” and “practical quality” were the three most important optimized operating methods, constituting a benchmark of user satisfaction.

The author found that it is possible to design and build a VR-based furniture demonstration system with a good level of usability when a suitable quality design method is applied. The optimized user interaction indicators and implementation experience for the VR-based product demonstration presented in this study will be of use in further design and development of similar systems.

This study was carried out to analyze the importance of consumer preference data in forecasting demand in apparel retailing.

To collect preference data, 729 hypothetical stock keeping units (SKU) were derived using a full factorial design, from a combination of six attributes and three levels each. From the hypothetical SKU's, 63 practical SKU's were selected for further analysis. Two hundred two responses were collected from a store intercept survey. Respondents' utility scores for all 63 SKUs were calculated using conjoint analysis. In estimating aggregate demand, to allow for consumer substitution and to make the SKU available when a consumer wishes to buy more than one item in the same SKU, top three highly preferred SKU's utility scores of each individual were selected and classified using a decision tree and was aggregated. A choice rule was modeled to include substitution; by applying this choice rule, aggregate demand was estimated.

The respondents' utility scores were calculated. The value of Kendall's tau is 0.88, the value of Pearson's R is 0.98 and internal predictive validity using Kendall's tau is 1.00, and this shows the high quality of data obtained. The proposed model was used to estimate the demand for 63 SKUs. The demand was estimated at 6.04 per cent for the SKU cotton, regular style, half sleeve, medium priced, private label. The proposed model for estimating demand using consumer preference data gave better estimates close to actual sales than expert opinion data. The Spearman's rank correlation between actual sales and consumer preference data is 0.338 and is significant at 5 per cent level. The Spearman's rank correlation between actual sales and expert opinion is −0.059, and there is no significant relation between expert opinion data and actual sales. Thus, consumer preference model proves to be better in estimating demand than expert opinion data.

There has been a considerable amount of work done in choice-based models. There is a lot of scope in working in deterministic models.

The proposed consumer preference-based demand estimation model can be beneficial to the apparel retailers in increasing their profit by reducing stock-out and overstocking situations. Though conjoint analysis is used in demand estimation in other industries, it is not used in apparel for demand estimations and can be greater use in its simplest form.

This research is the first one to model consumer preferences-based data to estimate demand in apparel. This research was practically tested in an apparel retail store. It is original.

Front-of-packaging (FOP) is a critical branding tool that uses “cues” to communicate product attributes and establish distinct brand images. This paper aims to understand how food brands utilize cues and their relative proportions to hierarchically communicate brand image and belonging to particular subcategories.

A content analysis is used for analysing 543 food FOPs sold in Australia (breakfast cereals, chips, snack bars). Samples are collected and classified into product sub-categories defined by ingredients, consumer-audience and retail placement. A novel 10 × 10 coding grid is applied to each FOP to objectively analyse cue proportion, with statistical comparison undertaken between sub-categories.

Results reveal intrinsic cues are favoured over extrinsic cues, except for those in the eatertainment sub-category. Hierarchies are evidenced that treat product and branding cues as primary, with health cues secondary. Statistically significant differences in cue proportions are consistently evident across breakfast cereals, chips and snack-bar FOPs. Clear differentiation is evidenced through cue proportions on FOP for health/nutrition focused sub-categories and eatertainment foods.

“Cue utilization theory” research is extended to an evaluation of brand encoding (not consumer decoding). Design conventions reveal how cue proportions establish a dialogue of communicating brand/product image hierarchically, the trade-offs that occur, a “meso-level” to Gestalt theory, and achieving categorization through FOP cue proportions. Deeper understanding of packaging design techniques provides inter-disciplinary insights that extend consumer behaviour, retailing and design scholarship.