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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.

The study’s objective is to measure the importance consumers attach to AI-based attributes, namely, chatbots, face recognition, virtual fitting room, smart parking and cashier-free station in retail stores. The study also examines the specific purpose of using these attributes for shopping.

A conjoint experiment was conducted using fractional factorial design. Consumers were given 14 profiles (AI attributes and its levels) to rank according to their visiting preferences.

The results revealed that the retail chatbot was considered the most important attribute, followed by face recognition, virtual fitting room, smart parking system and cashier-free station. Moreover, consumers prefer to use chatbots for in-store shopping assistance over alerts and updates, customer support and feedback. Similarly, consumers wish a face recognition facility for greetings while entering the store over other services. In addition, cluster analyses revealed that customer groups significantly differ in their preferences for AI-based attributes.

The study guides retail managers to invest in AI technologies to provide consumers with a technology-oriented shopping experience.

Our results provide an insight into the receptivity of AI technologies that consumers would like to experience in their favorite retail stores. The present study contributes to the literature by investigating consumer preferences for various AI technologies and their specific uses for shopping.

This paper aims to identify the profiles of beginner and experienced consumers (BCs and ECs) of craft beer and evaluate their perception, knowledge and purchase intention.

A total of 291 craft beer consumers including 148 BCs and 143 ECs participated in the study, which was divided into 2 stages: quantitative research through a questionnaire and conjoint analysis.

BCs are mostly female, with a lower age, income and consumption frequency, presented a shallower understanding of International Bitterness Units (IBU) and considered 7.0% alcohol by volume (ABV) as a high-relative value in craft beers. Otherwise, ECs are mostly men with a higher age and consumption frequency, and they don't consider 50 IBU as high bitterness. The results of conjoint analysis indicate that both groups of consumers attribute greater relative importance to ABV and nonalcoholic beers had lower purchase intentions. Finally, the authors show that beer with 30 IBU and 4.5% ABV is preferred by both BCs and ECs.

The authors' identification about the distinct behaviors of different groups of consumers, based on their consumption time of craft beer, as managerial implications for industries producing craft beer and, as a theoretical contribution, the authors have defined “BCs” and “ECs” based on the experiences with and durations of craft beer consumption.

This article investigates the impact of the COVID-19 pandemic on the consumer preference for the attributes of online food delivery (OFD) services in India. It also shows how the order size influences the consumer's willingness to pay (WTP) for the attributes of OFD services.

This work incorporates a conjoint analysis-based approach to determine the consumer preference for the attributes of OFDs such as price, delivery time, restaurant rating and packing quality during the COVID-19 pandemic. The fractional factorial design is applied for the data collection. The relative importance of the attributes and the part-worth utility of the attributes' levels have been determined. Further, the utility associated with the attributes' levels is used to find the consumer's WTP for different attributes.

The COVID-19 pandemic has changed consumer preference from price to food and packing quality in India. When the order is small, consumers exhibit a higher preference to the delivery time than packing quality. In contrast, consumers show a higher preference to packing quality than delivery time with the increase in order size. The consumer's WTP attains the highest level in case of food quality, followed by convenience and packing quality. The WTP for the attributes rises with the increase in order size.

The insights highlight the need for the online food delivery industry to redesign the business framework in the post-pandemic era. The hygiene and safety measures maintained by the consumers during the pandemic have significantly changed their purchasing behaviour, raising their preference for service quality (food and packing quality) of the OFD services apart from price.

This work determines the consumers' utility for each attribute level of OFDs, along with their relative importance. Moreover, this study contributes to the existing literature by exhibiting the impact of the COVID-19 pandemic on the consumer preference and order size on consumer's WTP for the attributes.

This study proposes a methodology based on data source triangulation to measure the “strategic fit” for the automotive supply chain.

At first, the authors measured the responsiveness of the Indian automobile supply chain, encompassing the top ten major automobile manufacturers, using both sentiment and conjoint analysis. Second, the authors used data envelopment analysis to identify the frontiers of their supply chain. The authors also measured the supply chain's efficiency, using the balance sheet. Further, the authors analyzed the “strategic fit” zone and discussed the results.

The results indicate that both the proposed methods yield similar outcomes in terms of strategic fitment.

The study outcomes facilitate measuring the strategic fit, thereby leveraging the resources available to align. The methodology proposed is both easy to use and practice. The methodology eases time and costs by eliminating hiring agencies to appraise the strategic fit. This valuable method to measure strategic fit can be considered feedback for strategic actions. This methodology could also be incorporated possibly as an operative measurement and control tool.

Data triangulation meaningfully enhances the accuracy and reliability of the analyses of strategic fit. Data triangulation leads to actionable insights relevant to top managers and strategic positioning of top managers within a supply chain.

Pricing is widely acknowledged as a market entry challenge for servitising companies. The purpose of this research is to ascertain the attributes that contribute to willingness to pay (WTP) for industrial services and suggest incorporating those attributes to a pricing model.

Three attributes (Quality of Service, Nearness of Service Provider and Brand Equity of Service Provider) were analyzed at three respective levels to ascertain their importance on WTP. Conventional conjoint analysis (CCA), using an orthogonal design, was the method used. The 346 respondents were decision-makers and top management professionals from various industries.

Brand Equity emerged as the most significant attribute contributing to WTP, having more than 45% importance – followed by the Quality and Nearness.

The scope of the study is limited to the industries and its Allies. However, the relative importance of the attributes may vary depending on the type of service.

The importance of attributes and their WTP preference helps future researchers create a pricing model involving these attributes. This helps service providers price their services rationally, thus succeeding in servitization.

Product life is extended because the manufacturers themselves are servicing it and also help recycle the product with their expertise. Servitization is also helpful for the Indian economy, as it is turning into a manufacturing economy.

This research investigates three attributes that contribute to WTP, in accordance with their level of contribution. It also provides a direction to establish an adequate pricing model for industrial services.

The paper aims to identify adoption criteria for “revolutionary” business techniques; based on case material, it invites further research.

Building on the idea of scientific revolution, three cases from sub-disciplines of business administration are chosen to illicit adoption criteria for business techniques.

The analysis shows that a logical response to a problem, the availability of a controllable procedure, the software and the means to apply the procedure easily, and the hardware, jointly seem to explain the adoption of “revolutionary” business technologies.

In case analysis in general, the results do not lead to induction. More research might identify additional success criteria. Furthermore, it might lead to determining adoption probabilities of techniques.

Managers being introduced to new techniques in business administration might use the criteria outlined here for their evaluation.

The author believes that the paper sheds new light on the development of business technologies and that this light might guide developers of technologies to come up with more potentially successful technologies.

This study aims to examine decision factors for global sourcing, differentiated into transcontinental and continental sourcing to obtain insight into locational aspects of sourcing decisions and global trends. This study analyzed various country perceptions to reveal their influence on sourcing decisions. The country of origin (COO) theory explains why certain country perceptions and images influence purchasing experts in their selection of suppliers.

This study used a two-study approach. In Study 1, the authors conducted discrete choice card experiments with 71 purchasing experts located in Europe and the USA to examine the importance of essential decision factors for global sourcing. Given the clear evidence that location is a factor in sourcing decisions, in Study 2 the authors investigated purchasers’ perceptions and images of countries, adding country ranking experiments on various perceived characteristics such as quality, price and technology.

Study 1 provides evidence that the purchasers’ personal relationship with the supplier plays a decisive role in the supplier selection process. While product quality and location impact sourcing decisions, the attraction of the buying company and cultural barriers are less significant. Interestingly, however, these factors seem as important as price to respondents. This implies that a strong relationship with suppliers and good quality products are essential aspects of a reliable and robust supply chain in the post-COVID-19 era. Examining the locational aspect in detail, Study 2 linked the choice card experiments with country ranking experiments. In this study, the authors found that purchasing experts consider that transcontinental countries such as Japan and China offer significant advantages in terms of price and technology. China has enhanced its quality, which is recognizable in the country ranking experiments. Therefore, decisions on global sourcing are not just based on such high-impact factors as price and availability; country perceptions are also influential. Additionally, the significance of the locational aspect could be linked to certain country images of transcontinental suppliers, as the COO theory describes.

The new approach divides global sourcing into transcontinental and European sourcing to evaluate special decision factors and link these factors to the locational aspect of sourcing decisions. To deepen the clear evidence for the locational aspect and investigate the possible influence of country perceptions, the authors applied the COO theory. This approach enabled authors to show the strong influence of country perception on purchasing departments, which is represented by the locational effect. Hence, the success of transcontinental countries relies not only on factors such as their availability but also on the purchasers’ positive perceptions of these countries in terms of technology and price.

The purpose of this article is to revisit the concepts of city singularity and identity, as well as the concept’s related topics (i.e. place identity, place attachment and place dependence). The aim is to investigate the impact of development projects on people's preferences for old and contemporary features in modern cities. The evidence for this can be seen mainly at historical sites such as Heliopolis in Cairo, Egypt.

The authors investigated how development projects affect place identity and city singularity through a three-step process. As a first step, an online questionnaire was administered to experts to assess the characteristics that shape the identity of local districts. In the second and third steps, interviews were conducted, followed by online surveys directed at Heliopolis residents and non-residents. The weight of people’s preferences was determined using multi-criteria decision-making (MCDM).

The results highlighted people's preferences to consider when developing projects at historical sites. Based on these preferences, the authors’ concluded remarks provide insight into some considerations for developing projects in historic places.

The added value here is surveying people’s preferences about development projects in historical places. The physical and social components interplay contributes to city identity and singularity. Based on these preferences, this investigation offers valuable insights into enhancing historical site development.

The current study has drawn attention to investigating the impact of social media influencers’ (SMIs) authenticity on followers buying behavior by using followers who have an ongoing relationship with an influencer and are knowledgeable about the influencer. The study further intends to reveal the mediating effect of parasocial interaction on the relationship between SMI's authenticity and followers' purchase behavior.

The study has analyzed data from an online survey of 458 participants (Instagram followers) using structured equation modeling (CB-SEM) to investigate the relationship among authenticity attributes, parasocial interaction and followers' purchase behavior.

CB-SEM results reveal that authenticity attributes positively influence followers' buying behavior. The findings from mediation analysis specify that parasocial interaction mediates the relationship between authenticity attributes (sincerity, truthful endorsement and visibility) and buying behavior excluding expertise, uniqueness attributes.

The findings of the study reinforce the need to use authentic influencers by the marketers for the brand endorsements. Further, the findings of the study can benefit marketers in implementing strategic practice of social media influencer marketing.

The study overcomes the limitations of preceding studies by using Instagram followers who are well-informed about SMIs and have an ongoing relationship with them. This study has uniquely combined the behavioral data from real influencer campaigns with followers' assessment of an influencer's authenticity.