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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.

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 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.

A critical step in influencer marketing is influencer outreach, where a brand reaches out to an influencer and forms a partnership. Yet little is known about how factors related to this process might influence the outcomes of sponsored posts. To address this gap, the authors investigated whether, how and when the order of influencers' product use and brand outreach (i.e. use/outreach order) affects post persuasiveness.

The authors conducted three experimental studies. Studies 1 and 2 examined the effect of disclosure type (use-first, outreach-later vs. outreach-first, use-later vs. no disclosure) on consumers' responses to the post. Study 3 investigated the moderating effects of compensation disclosure type.

The results revealed that when the influencer used the product before (vs. after) being contacted by the brand, consumers had more favorable attitudes about the product and greater purchase intention upon reading the sponsored posts; perceived information diagnosticity mediated this effect. However, this tendency was mitigated if the influencer disclosed the specific monetary payment from the brand.

This research advances understanding of sponsorship disclosure and provides a way to manage its impact on message persuasiveness.

The halal cosmetics industry continues to grow significantly. Furthermore, using halal cosmetics is a must for Muslims. This study aims to analyze the factors influencing the switching intention to halal cosmetics in Indonesia.

This quantitative study uses a Partial Least Square-Structural Equation Modelling (PLS-SEM) on 214 respondents. The variables include halal certification, halal awareness, product image, perceived behavioral control, subjective norm, attitude, advertisement and switching intention.

The product image plays the most influential role in deriving the attitude toward switching intention to halal cosmetics, following perceived behavioral control, halal awareness and subjective norm, but not halal certification and advertisement. The result indicates that the image of halal cosmetics influences customers’ attitudes toward switching to using halal cosmetics. Indonesian customers know the obligation to use halal products because they are Muslim. However, the existence of halal certification does not derive the switching intention to halal cosmetics.

This study conducts research only in Indonesia. As a recommendation, further studies might conduct a comparative test using multicultural respondents in several countries. Other studies also suggested examining factors of switching intention through different generational, especially in countries with high individualism traits.

This study will encourage the halal industry, especially the halal cosmetics industry, to pay more attention to the product image. Meanwhile, the government can provide incentives or rewards to promote industry participation in halal cosmetics. The findings provide a more detailed understanding of how product image can influence someone to switch to halal cosmetics.

Research on switching intention to halal cosmetics is still limited. This study uses halal variables, while previous studies only used religiosity. This study also introduced the product images motivating customers’ switching intention to use halal cosmetics.

One of the critical reasons for the nonacceptance of additive manufacturing (AM) processes is the lack of understanding and structured knowledge of design for additive manufacturing (DfAM). This paper aims to assist designers to select the appropriate AM technology for product development or redesign. Using the suggestion provided by the design assist tool, the user’s design alterations depend on their ability to interpret the suggestion into the design without affecting the design’s primary objective.

This research reports the development of a tool that evaluates the efficacy values for all seven major standard AM processes by considering design parameters, benchmark standards within the processes and their material efficacies. In this research, the tool provides analytical and visual approaches to suggestion and assistance. Seventeen design parameters and seven benchmarking standards are used to evaluate the proposed product and design quality value. The full factorial design approach has been used to evaluate the DfAM aspects, design quality and design complexity.

The outcome is evaluated by the product and design quality value, material suit and material-product-design (MPD) value proposed in this work for a comparative assessment of the AM processes for a design. The higher the MPD value, the better the process. The visual aspect of the evaluation uses spider diagrams, which are evaluated analytically to confirm the results’ appropriateness with the proposed methodology.

The data used in the database is assumed to make the study comprehensive. The output aims to help opt for the best process out of the seven AM techniques for better and optimized manufacturing. This, as per the authors’ knowledge, is not available yet.