Deep Learning Enabled Consumer Research for Product Development

“Needmining” is the analysis of user-generated content as a new source of customer needs, which are an important factor in new product development processes. Current approaches use supervised machine learning to condense large datasets by performing binary classification to separate informative content (needs) from uninformative content (no needs). This study introduces a transformer model and compares it to relevant approaches from the literature. We train the models on data composed from a single product category. Subsequently, we test the models’ ability to detect needs in a validation sample containing product categories not present in the training set, i.e. “out-of-category” prediction. Our cross-validated results suggest that, based on the F1-score, the transformer model outperforms previous approaches at both in-category and out-of-category predictions. This suggests that transformers can make needmining more relevant in practice by improving the efficiency of the needmining process by reducing the resources needed for data preparation

The Value of Incorporating Review Tags into an Online Review System for User Review Generation

Online review mining has become an important way for businesses to understand consumer preferences and product characteristics. Many online review platforms have started to incorporate the extracted information as review tags to guide future reviews. In this study, we leverage a quasi-experiment from an online health service platform to investigate the value of incorporating the review tags (extracted from prior reviews) into the online review system in user review generation. Our preliminary results show that after the provision of review tags, more reviews are provided for doctors but the length of those reviews is shorter. Notably, we also find a decrease in sentiment and an increase in novel reviews. Our findings provide actionable managerial insights for platform managers to design online review systems

Analyzing Customer Needs of Product Ecosystems Using Online Product Reviews

It is necessary to analyze customer needs of a product ecosystem in order to increase customer satisfaction and user experience, which will, in turn, enhance its business strategy and profits. However, it is often time-consuming and challenging to identify and analyze customer needs of product ecosystems using traditional methods due to numerous products and services as well as their interdependence within the product ecosystem. In this paper, we analyzed customer needs of a product ecosystem by capitalizing on online product reviews of multiple products and services of the Amazon product ecosystem with machine learning techniques. First, we filtered the noise involved in the reviews using a fastText method to categorize the reviews into informative and uninformative regarding customer needs. Second, we extracted various customer needs related topics using a latent Dirichlet allocation technique. Third, we conducted sentiment analysis using a valence aware dictionary and sentiment reasoner method, which not only predicted the sentiment of the reviews, but also its intensity. Based on the first three steps, we classified customer needs using an analytical Kano model dynamically. The case study of Amazon product ecosystem showed the potential of the proposed method.https://deepblue.lib.umich.edu/bitstream/2027.42/153962/1/ANALYZING CUSTOMER NEEDS OF PRODUCT ECOSYSTEMS USING ONLINE PRODUCT REVIEWS.pdfDescription of ANALYZING CUSTOMER NEEDS OF PRODUCT ECOSYSTEMS USING ONLINE PRODUCT REVIEWS.pdf : Main articl

What Do Customers Say About My Products? Benchmarking Machine Learning Models for Need Identification

Needmining is the process of extracting customer needs from user-generated content by classifying it as either informative or uninformative regarding need content. Contemporary studies achieve this by utilizing machine learning. However, models found in the literature cannot be compared to each other because they use private data for training and testing. This study benchmarks all previously suggested needmining models including CNN, SVM, RNN, and RoBERTa. To ensure an unbiased comparison, this study samples and annotates a dataset of customer reviews for products from 4 different categories from amazon. Henceforth, the dataset is publicly available and serves as a gold-set for future needmining benchmarks. RoBERTa outperformed other classifiers and seems to be best suited for needmining. The relevance of this study is reinforced by the fact that this benchmark creates a different hierarchy between models than otherwise suggested by comparing the results of previous studies

Identifying Always-the-Same-Rating Reviewers On Amazon.Com Using Big Data Analytics

This study identifies always-the-same-rating reviewers (ASRs), that is, reviewers who give the same star rating for all reviewed products and who write many reviews on Amazon. This study identifies ASRs in 29 product categories by analyzing 230 million individual reviews on Amazon. The findings of this study show that: 1) all product categories contain reviews written by ASRs; 2) the majority of ASRs (99.99%) give the same star rating for all reviewed products in all categories; and 3) the rating distribution of ASRs\u27 reviews is extremely skewed toward the five-star rating (98.02%). The digital music category, in particular, shows a high share and volume of ASRs among all categories, making it an ideal focal category for further empirical analysis of ASRs. This study empirically demonstrates that star rating, the helpfulness of reviews, the length of headline and review, prior reviews, and holidays are potential indicators of reviews written by ASRs. The finding shows that reviews from verified and nonverified ASRs respond differently to some potential indicators. This article is the first step toward identifying irregular reviewer groups and their abnormal rating patterns, which would help in the segmentation of online consumers and a better understanding of online consumer review behaviors

¿Cómo el género y la edad pueden afectar el comportamiento de compra del consumidor? Evidencia desde una perspectiva microeconómica de Hungría

The present study aimed to investigate the effect of demographic variables of gender and age on online consumer purchase behavior (CPB) on Facebook in Hungary. The statistical population of the present study consists of Facebook users in Hungary, including Hungarian natives, foreigners residing in this country including students. A sample of 433 online consumers in different age groups was surveyed. The questionnaire was shared via an online link on the Facebook platform and also on various channels. Welch’s t-test was used to examine the gender variable, and Welch and Brown-Forsythe test was used to examine the age variable. The results showed that there was a significant difference between CPB in all age groups and the age group of over 50 years on Facebook. This important result emphasized the importance and impact of social networks as marketing channels on young people. Another important point was the difference between the purchase behaviors of male and female consumers. The results from this research can have implications for businesses in developing their competitive advantages and adopting proper approaches in advertising and marketing campaigns based on the socio-demographic characteristics of people.El presente estudio tuvo como objetivo investigar el efecto de las variables demográficas de género y edad en el comportamiento de compra del consumidor on-line (CPB, por sus siglas en inglés) en Facebook en Hungría. La población estadística del presente estudio consiste en usuarios de Facebook en Hungría, incluidos los nativos húngaros y los extranjeros que residen en este país, incluidos los estudiantes. Se encuestó a una muestra de 433 consumidores en línea en diferentes grupos de edad. El cuestionario se compartió a través de un enlace en línea en la plataforma de Facebook y también en varios canales. Se utilizó la prueba t de Welch para examinar la variable de género, y la prueba de Welch y Brown-Forsythe, para examinar la variable de edad. Los resultados mostraron que hubo una diferencia significativa entre el CPB en todos los grupos de edad y el grupo de más de 50 años en Facebook. Este importante resultado enfatizó la importancia y el impacto de las redes sociales como canales de comercialización en los jóvenes. Otro punto importante fue la diferencia entre los comportamientos de compra de los consumidores masculinos y femeninos. Los resultados de esta investigación pueden tener implicaciones para que las empresas desarrollen sus ventajas competitivas y adopten enfoques adecuados en las campañas de publicidad y marketing en función de las características sociodemográficas de las personas

The Effect of Hearing the Customer’s Voice in Achieving Sustainable Competitive Advantage: An Exploratory Study of the Opinions of Employees of Baghdad Soft Drinks Company

يهدف البحث الى تحقيق الميزة التنافسية المستدامة من خلال سماع صوت الزبون والاهتمام برغباته ومتطلباته في شركة بغداد للمشروبات الغازية، وتمثلت مشكلة البحث عبر التساؤلات المطروحة بشأن مدى علاقة وتأثير سماع صوت الزبون في تحقيق الميزة التنافسية لشركة بغداد للمشروبات الغازية، ولتحقيق هدف البحث تم اختبار فرضيتين رئيسيتين واخرى فرعية وتم استخدام الاستبانة كأداة لجمع البيانات من خلال توزيع (137) استبانة استرجع منها (100) استبانة قابلة للتحليل، وقد تم تحليل البيانات باستخدام برنامج (Spss)، وتوصل البحث الى وجود مستوى جيد من حيث اهتمام الشركة المبحوثة بصوت زبائنها بالإضافة الى تحقيق علاقتي الارتباط والاثر بين صوت الزبون والميزة التنافسية المستدامة، واوصى البحث بالعديد من التوصيات اهمها اجراء المقابلات الدورية مع زبائن الشركة والاهتمام برغباتهم ومقترحاتهم حول تطوير وتحسين منتجات الشركة.The research aims to achieve sustainable competitive advantage by hearing the customer’s voice and paying attention to his desires and requirements in Baghdad Soft Drinks Company. The research problem was through questions raised about the relationship and effect of hearing the customer’s voice in achieving the competitive advantage of Baghdad Soft Drinks Company. To achieve the research goal, two main hypotheses were tested. And a secondary one, and the questionnaire was used as a tool to collect data by distributing (137) questionnaires from which (100) questionnaires were retrieved for analysis, and the data were analyzed using the (Spss) program, and the research reached a good level in terms of the researched company's interest in the voice of its customers in addition to an investigation My relationship and the impact between the customer's voice and the sustainable competitive advantage, and the research recommended many recommendations, the most important of which is conducting periodic interviews with the company's customers and paying attention to their desires and suggestions about developing and improving the company's products

A taxonomy for deriving business insights from user-generated content

Deriving business insights from user-generated content (UGC) is a widely investigated phenomenon in information systems (IS) research. Due to its unstructured nature and technical constraints, UGC is still underutilized as a data source in research and practice. Using recent advancements in machine learning research, especially large language models (LLMs), IS researchers can possibly derive these insights more effectively. To guide and further understand the usage of these techniques, we develop a taxonomy that provides an overview of business insights derived from UGC. The taxonomy helps both practitioners and researchers identify, design, compare and evaluate the use of UGC in this IS context. Finally, we showcase an LLM-supported demo application that derives novel business insights and apply the taxonomy to it. In doing so, we show exemplary how LLMs can be used to develop new or extend existing NLP applications in the realm of IS

The Anchoring Effect of “Quality Threshold for Monetary Incentive” on Online Review Platforms

The “quality threshold for monetary incentive” mechanism is a common practice in online review platforms. However, the effect of the quality threshold is still not clear in the extant literature. This study attempts to investigate how the introduction of the quality threshold affects content quality. Based on the Anchoring Effect theory, this study first derives some theoretical conclusions based on theoretical models and then conducts a natural experiment to test the conclusions. The findings show that after introducing the quality threshold, (1) the proportion of content with the threshold-level quality will increase; (2) the proportion of content higher than the quality threshold is reduced when there is the “Anchoring Effect”. Moreover, the empirical study also shows that the quality threshold leads to an overall negative effect on the average review quality. Our findings are meaningful to the stakeholders of the online review platforms

Asiakasvaatimusten tunnistaminen ja hyödyntäminen jatkuvassa tuotekehityksessä

Tiivistelmä. Tämä työ käsittelee prosessia, jolla tuotevaatimuksia derivoidaan asiakkailta. Tavoitteena on tunnistaa nykyiset toimintatavat, esitellä yleisimmät tavat kerätä tietoa asiakkailta ja miten niistä saadaan johdettua hyödyllisiä ja realistisia vaatimuksia tuotteelle. Toinen kiinnostava osaalue on se, kuinka nämä vaatimukset voidaan toteuttaa inkrementaalisessa ohjelmistotuotekehitysprosessissa. Tutkimus toteutetaan kirjallisuuskatsauksen avulla. Työ esittelee teoreettiset perusteet tuotekehitykseen, vaatimusmäärittelyyn ja asiakastarpeiden tunnistamiseen. Lopuksi löydökset pyritään analysoimaan ja vetämään yhteen. Työn tuloksena selviää, että asiakkaita pyritään nykyisin integroimaan entistä laajemmin ja avoimemmin tuotekehitysprosesseihin. Asiakkaiden osallistamisella vaikuttaa olevan positiivinen yhteys kykyyn vastata asiakkaiden vaatimuksiin ja kasvattamaan menestyksen todennäköisyyttä ohjelmistotuotannossa.Voice of customer in incremental product development. Abstract. This thesis discusses the process of extracting product requirements from the voice of customers. The aim is to find best practices from the industry, present the most common ways of collecting information from the customers, and how to extract the useful derivatives from that data. Another area of interest is how these requirements can be implemented on the incremental software product development process. The research is carried out using a literature review. The work presents the theoretical foundations for product development, defining requirements and identifying customer needs. Finally, the findings are analyzed and summarized. As a result of the work, it becomes clear that nowadays the aim is to integrate customers more stringly and openly into product development processes. Customer involvement seems to have a positive correlation with the ability to meet customer demands and increase the probability of successful software