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The aim of the study reported in this article was to establish whether the quality of clothes of a given brand is perceived and assessed by consumers at the same level. An additional purpose was to identify the features that characterize the quality of popular, among Polish young female consumers, clothing brands and to classify them according to their quality level (perceived and assessed).

The article presents two approaches to consumer assessment of the quality of clothes: the survey method, in which the product quality was determined by 320 Polish female young respondents (19–25) based on their own previous experiences and impressions related to a particular clothing brand, and a direct assessment of shirts of selected brands using a sensory quality assessment method, a five-point hedonic scale with the verbal anchor (115 evaluators).

The research has revealed a significant difference between the perception of selected quality features of brands and the consumer assessment of the products. The perception maps developed based on the PROFIT analysis (PROperty FITting) as well as on cluster analysis provided interesting information about the situation of selected brands and their features in comparison with others and allowed to identify strong and weak features characterizing a given category.

This study has several limitations. First and foremost, the research results cannot be generalized to all consumers because they encompass the results from one national context and one population of respondents (young females). It should also be noted that the conducted research comprises only the most popular clothing brands available on the Polish market, in particular, the fast fashion segment brands. Furthermore, it would be advisable to carry out a sensory assessment of the quality of other clothing items offered under the brand names investigated.

This research could be a valuable source of information for clothing company managers, thanks to which they could better manage their brand and its position on the market. When undertaking marketing activities consisting of building positive perceptions about the product, it is important to make sure that the product offers an attractive sensory experience. When real quality deviates from consumer perceptions about the quality, managers should take corrective actions to restore and even improve the brand image in the eyes of the consumer, as well as to ensure the brand and the products offered under it a stable position on the market.

By comparing two approaches to consumer quality assessment, discrepancies between declared and real (sensory) quality of clothes have been identified and the distinctive features that differentiate selected brands regarding their quality level have been indicated.

Artificial intelligence (AI) has started to receive attention in the field of digital entrepreneurship. However, few studies propose AI-based models aimed at assisting entrepreneurs in their day-to-day operations. In addition, extant models from the product design literature, while technically promising, fail to propose methods suitable for opportunity development with high level of uncertainty. This study develops and tests a predictive model that provides entrepreneurs with a digital infrastructure for automated testing. Such an approach aims at harnessing AI-based predictive technologies while keeping the ability to respond to the unexpected.

Based on effectuation theory, this study identifies an AI-based, predictive phase in the “build-measure-learn” loop of Lean startup. The predictive component, based on recommendation algorithm techniques, is integrated into a framework that considers both prediction (causal) and controlled (effectual) logics of action. The performance of the so-called active learning build-measure-predict-learn algorithm is evaluated on a data set collected from a case study.

The results show that the algorithm can predict the desirability level of newly implemented product design decisions (PDDs) in the context of a digital product. The main advantages, in addition to the prediction performance, are the ability to detect cases where predictions are likely to be less precise and an easy-to-assess indicator for product design desirability. The model is found to deal with uncertainty in a threefold way: epistemological expansion through accelerated data gathering, ontological reduction of uncertainty by revealing prior “unknown unknowns” and methodological scaffolding, as the framework accommodates both predictive (causal) and controlled (effectual) practices.

Research about using AI in entrepreneurship is still in a nascent stage. This paper can serve as a starting point for new research on predictive techniques and AI-based infrastructures aiming to support digital entrepreneurs in their day-to-day operations. This work can also encourage theoretical developments, building on effectuation and causation, to better understand Lean startup practices, especially when supported by digital infrastructures accelerating the entrepreneurial process.

Both governments and the food industry are interested in plant-based products. New products are advertised as climate-friendly, with plant-based materials increasingly replacing animal-based content. In Finland, oat milk dominates the plant-based milk market. The authors studied what features young and urban users of plant-based and cow's milk value in oat milk for coffee and how the preferences of the users relate to ethical food-choice motives.

In total, 308 students filled in an e-questionnaire. The survey used best-worst scaling (BWS), a discrete choice approach, to measure the perceived values related to oat milk characteristics. The ethical motives were measured by a version of the Lindeman and Väänänen scale. Also the respondents' diets were asked. Preference clusters were identified and viewed with the ethical food-choice motives and diets.

The respondent group that exclusively used cow's milk attached more value to taste, added nutritional elements, discounts and recommendations by friends. The rest of the respondents attached more value to origin and sustainability-related features of oat milk. In the six-cluster solution, one extreme cluster was valuing taste and the other was valuing sustainability-related issues. All the ethical food-choice motives: ecological welfare, political values and religion were (roughly) the higher the cluster valued sustainability-related items. The respondents eating meat were more likely to belong to the clusters valuing taste than non-meat eaters that belong more likely to clusters valuing sustainability-related features.

Very few earlier studies have explored the heterogeneity of valuations of plant-based products and the products' relationship with ethical food-choice motives.

It is evident that the local small and medium enterprises of Bangladesh suffered in the competition because of the strong presence of foreign brands in the market. The purpose of this study is to figure out the factors influencing consumers to purchase a foreign brand predominantly among young consumers.

It is a quantitative study considering 257 young demographics as respondents. They are drawn randomly. Confirmatory factor analysis, regression analysis and a path model of structural equation model using AMOS graphics software are used in this study to analyze the data.

This study finds two out of three identified variables are influencing the foreign product purchase for the young Bangladeshi consumers. Brand value and product features of the given foreign product are the most dominant factors behind this adoption. However, a significant relationship between product quality and brand choice is not evident in this study.

It will give a clue to the small or medium startup of the country that normally target the young customer but face challenges to understand their mind and sometimes lagging because of the strong presence of the international brands.

A comprehensive review of the literature suggests that not so many studies took place that determines the respective factors behind the purchase of foreign brands particularly among young demographics of a lower middle income developing nation.

This paper analyses the factors that make fashion-product crowdfunding campaigns successful. The authors argue that crowdfunding is an innovative and functional way of bringing new fashion items to the market. The purpose of this paper is to answer the question whether product innovation, lifecycle and sustainability have a positive effect on the success of fashion crowdfunding campaigns. The findings highlight that the success of the fashion crowdfunding campaigns depends on creators' adherence to the values of the platform which they use to raise capital.

A total of 300 fashion crowdfunding projects running between the 17th of October and the 15th of December 2017 were collected from Kickstarter – the world's largest crowdfunding platform based on reward-based all-or-nothing model. Two-step binomial logistic regression was used to analyse the data.

The model predicted a significant increase in the odds of success for the fashion items crowdfunded during the first-time production, and innovative and environmentally sustainable products with a higher price range of rewards. In line with previous literature, regression analyses predicted a significant effect of the control variables of goal amount (negative) and the number of rewards (positive). Contrary to previous studies, neither the presence of a video nor the campaign length predicted success.

The novel findings of this study contribute to the literature by providing an analysis of success factors of fashion items on crowdfunding platforms. The results show that innovative, environmentally sustainable and higher-priced products produced by early-stage ventures are better welcomed by the audiences.

This work aims to explore why people review their experienced online shopping in such a manner (promptness), and what is the potential relationship between the users’ review promptness and review motivation as well as reviewed contents.

To evaluate the customers’ responses regarding their shopping experiences, in this paper, the “purchase-review” promptness is studied to explore the temporal characteristics of users’ reviewing behavior online. Then, an aspect mining method was introduced for assessment of review text. Finally, a theoretical model is proposed to analyze how the customers’ reviews were formed.

First, the length of time elapsed between purchase and review was found to follow a power-law distribution, which characterizes an important number of human behaviors. Within online review behaviors, this meant that a high frequency population of reviewers tended to publish relatively quick reviews online. This showed that the customers’ reviewing behaviors on e-commerce websites may have been affected by extrinsic motivations, intrinsic motivations or both. Second, the proposed review-to-feature mapping technique is a feasible method for exploring reviewers’ opinions in both massive and sparse reviews. Finally, the customers’ reviewing behaviors were found to be mostly consistent with reviewers’ motivations.

First, the authors propose that the “promptness” of users in posting online reviews is an important external manifestation of their motivation, product experience and service experience. Second, a semi-supervised method of review-to-aspect mapping is used to solve the data quality problem in mining information from massive text data, which vary in length, detail and quality. Finally, a huge amount of e-commerce customers’ purchase-review promptness are studied and the results indicate that not all product features are responsible for the “prompt” posting of users’ reviews, and that the platform’s strategy to encourage users to post reviews will not work in the long term.

Social networks have changed the communication patterns significantly. Information available from different social networking sites can be well utilized for the analysis of users opinion. Hence, the organizations would benefit through the development of a platform, which can analyze public sentiments in the social media about their products and services to provide a value addition in their business process. Over the last few years, deep learning is very popular in the areas of image classification, speech recognition, etc. However, research on the use of deep learning method in sentiment analysis is limited. It has been observed that in some cases the existing machine learning methods for sentiment analysis fail to extract some implicit aspects and might not be very useful. Therefore, we propose a deep learning approach for aspect extraction from text and analysis of users sentiment corresponding to the aspect. A seven layer deep convolutional neural network (CNN) is used to tag each aspect in the opinionated sentences. We have combined deep learning approach with a set of rule-based approach to improve the performance of aspect extraction method as well as sentiment scoring method. We have also tried to improve the existing rule-based approach of aspect extraction by aspect categorization with a predefined set of aspect categories using clustering method and compared our proposed method with some of the state-of-the-art methods. It has been observed that the overall accuracy of our proposed method is 0.87 while that of the other state-of-the-art methods like modified rule-based method and CNN are 0.75 and 0.80 respectively. The overall accuracy of our proposed method shows an increment of 7–12% from that of the state-of-the-art methods.

The purpose of this study is to analyze how positive behaviors toward influencers (customer interaction) and promoted products (looking for product information) can be achieved, taking into account influencer–product fit, in a fashion marketing campaign. In addition, account following and product involvement are examined as possible moderators in these relationships.

The data were gathered from online participants. The participants were Instagram users who already knew a popular influencer on the platform. The experimental design manipulated the types of picture posted by the influencer to observe customers’ reactions in terms of intention to interact with the influencer’s account and to look for further information about promoted products.

The authors’ findings suggested that influencer–product matches in posts on Instagram encourage users to search for information about promoted products but do not affect their intention to interact with influencers’ accounts. Nevertheless, customers’ reactions toward an influencer’s posts differ based on whether they are followers of the influencer and whether they are highly or lowly involved with the promoted product.

Both brands and influencers should properly manage influencer marketing actions. Brands should control influencers’ audiences and their involvement with featured products so that they are seen to promote them in a natural way. Influencers should endorse branded products that fit their own style; this will increase the interaction on their accounts.

This research contributes to a better understanding of how users can be encouraged to undertake positive online actions as regards influencers (interaction with their accounts) and promoted products (information search) in influencer marketing campaigns.

Esta investigación analiza cómo lograr comportamientos positivos hacia los influencers (mayor interacción del consumidor con la cuenta) y los productos promocionados (búsqueda de información sobre el producto), en función del ajuste influencer-producto en una campaña de marketing de moda. Además, se estudian el seguimiento de la cuenta y la implicación con el producto como posibles moderadores en estas relaciones.

Los datos de la investigación se recogieron a través de una encuesta realizada a usuarios de Instagram que conocían previamente a la influencer estudiada. A través de un diseño experimental se manipularon las publicaciones de la influencer para analizar las reacciones de los consumidores, más concretamente, sus intenciones de interactuar con la cuenta del influencer y de buscar más información sobre los productos promocionados.

Los resultados de la investigación sugieren que un buen ajuste entre los influencers y los productos promocionados incentiva a los usuarios a buscar información sobre éstos productos, pero no afecta a su intención de interactuar con la cuenta de Instagram de la influencer. No obstante, las reacciones hacia las publicaciones de la influencer difieren dependiendo de si los consumidores son o no seguidores de la influencer y del nivel de implicación con el producto promocionado.

El trabajo muestra la necesidad de gestionar adecuadamente las acciones de marketing en las que participan influencers. Las marcas deben conocer el público al que se dirigen los influencers y su implicación con los productos promocionados, para que la publicación promocionada resulte natural. Por su parte, los influencers deben promocionar los productos de marcas que se ajusten a su propio estilo para así incrementar la interacción del público con sus cuentas.

Este trabajo indica algunas de las características que han de tener las campañas de marketing con influencers cuando su objetivo es estimular comportamientos de los consumidores tales como la interacción con la cuenta y la búsqueda de información sobre el producto.

Trabajo de investigación