Penerapan Text Network Analysis Dalam Menganalisis Pendapat Customer Product Patriot di Shopee

E-commerce has changed the buying and selling process and the way people interact via the internet. One company that uses e-commerce is PT Patriot Memory Indonesia. PT Patriot Memory Indonesia sells well-known computer peripherals, including the Solid State Drive (SSD). PT Patriot Memory Indonesia wants to analyze customer feedback regarding SSD products sold in e-commerce, namely Shopee by using Text Network Analysis (TNA) which is one part of social computing. Social computing is a science that focuses on social behavior and social contexts using computing systems. One of the tools of social computing, namely Text Network Analysis (TNA), is a research technique that focuses on identifying and comparing network relationships between words, sentences, and systems to model interactions that generate new knowledge or information. In this study, TextNetwork Analysis will show consumer perceptions through the feedback it provideson buyer reviews. The opinions expressed by consumers in buyer reviews can be analyzed so that they can connect each word and create associations of consumer perceptions of a product. Thus, it can be seen the aspects that must be addressed by the company to improve consumer perceptions. The problem analyzed is the development of social computing in analyzing big data. Can the company take advantage of this information so that they know the perceptions of their consumers through the information in the customer feedback at Shopee. Through Text Network Analysis in social computing, researchers will know the association of each word of consumer perception and can see the perception that has the highest degree of a product and see its relationship with other perceptions. This study looks at consumer perceptions of Patriot SSD products at Shopee. The results of this study can help provide customer feedback information to PT Patriot Memory Indonesia.

The transformative and evolutionary approach of omnichannel in retail companies: insights from multi-case studies in Portugal

PurposeOmnichannel implementation in retail requires business transformation and faces several operational barriers. This research discusses how omnichannel has been implemented, in a managerial perspective, and how integration of operations has overcome the identified transformative barriers.Design/methodology/approachThe authors followed a multi-case study approach, with observation and interviews with managers of four big retail companies in Portugal.FindingsThe results suggest that retail companies have overcome the barriers to implement omnichannel models through the integration of information technology (IT), the accomplishment of organisational changes and the optimisation of customer feedback, achieving positive business indicators, namely increased sales.Research limitations/implicationsThe conclusions of the paper provide valuable information to help companies to design the process of channel's integration in order to overcome the transformative constraints of omnichannel. However, those conclusions emerge from Portuguese case studies of retail companies, and a generalised discussion should consider the contextual diversity of consumer expectations, cultural user experiences in retail and the maturity of digital transformation and omnichannel implementation stage in each country.Originality/valuePrevious studies had characterised the items of omnichannel retail, fulfilment processes, the benefits of channels' integration in customer experience, satisfaction and loyalty and had identified barriers for its implementation. Considering the existence of different stages of omnichannel implementation, this paper explores how retail companies increment omnichannel operations, overcome transformative barriers and achieve the omnichannel benefits, through the whole involvement of the organisation system, the customer approach and the business model, besides the technology integration.

Usefulness of Netnography in the Digital Banking Context

This chapter discusses the usefulness of netnography as a research method in the digital banking context. Netnography has become a relative attractive data collection and data analysis method in some social science research areas but is still relatively unknown in financial research. Compared with other research methods, netnography seems to have some advantages in the digital banking world, such as real-time customer feedback. Moreover, virtual observations can be used not only by researchers but also by bank representatives to, for example, find out how bank customers can contribute to value co-creation.

Elicitation of Hidden Expectations of Customers in Healthcare Service

In healthcare service, the customer feedback is often incomplete unless the unexpressed expectations are captured at the right time. These latent feelings and opinions are the ‘hidden expectations’ which are the quality indicators of the service offered. This short communication paper describes a couple of techniques for elicitation of hidden expectations of customers in the context of healthcare services. As an illustration, the paper depicts the application of these techniques for identification and prioritization of hidden expectations at a diagnostic center. Any organization in healthcare service sector could employ these techniques quickly and conveniently to improve quality of service thereby enhancing customer satisfaction. Keywords: Elicitation, Customer satisfaction, Hidden expectation, MoSCoW, Healthcare.

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A Model-Based Design Method for the Correlation between Customer Feedback and Technical Design Parameters in the Context of Systems Engineering

Nowadays considering trends such as digitalization, automated driving as well as electric mobility in products in automotive development processes is a major challenge, which has led to an enormous increase in the number of product functions of technical systems. However, the recognized processes in automotive development are strongly component-oriented and such processes partially support the development of product functions. In order to meet future trends and ensure long term customer satisfaction, a transfer from component-oriented to function-oriented development is necessary. Accordingly, a holistic concept can be useful that enables the integration of customer feedback into the early phase of product development in the context of function-orientation. However, the customer feedback evaluation and their mapping with technical subsystems have been considered mainly in the context of component-oriented development. In this contribution, a method is proposed, which is generated in the context of a product model of product generation engineering. Product Generation Engineering enables the structuring of the development process of a product generation and supports function-oriented development. The Product Model provides customer- oriented development of mechatronic products. The proposed method is achieved in the sense of model-based systems engineering and validated by the exemplarily application of a case study of a specific vehicle. Both the past and current product generations of the specific vehicle are taken into account in the development of the subsequent product generation.

The Use of Transformer Model in Opinion Summarisation

We live in an age of information, therefore collected data and documentation are practically treasure resources. All about a business and its development can be estimated with clarity via statistics. Any machine that could really analyse information to predict a projected outcome is known for being extremely vital for the business. It is critical for the system to provide accurate and useful knowledge of the products in order to conduct accurate assessment. Summarisation is a technique for obtaining a rundown from series of sentences in a study or observation that facilitates us with understanding the basic content of the knowledge expressed within. Simple and brief summaries of just a product will assist the system in performing prospective product research and development. In our paper, we use a deep learning framework that provides to extract clean, relevant, brief summaries from comprehensive customer feedback. Strategies of abstractive text summarisation is used. The method of extracting the primary keyword from a statement and using them in the summary is defined as extractive text summarisation. We utilise abstractive summarisation in this case, which evolves from sample information and provides the best feasible description. Utilising Transformer with Depth Scaling MultiHeaded Attention as well as GloVe word embedding with positional encoding, we illustrate an abstractive approach to extract summaries from the Amazon fine food reviews dataset. Transformer aids in the parallelisation of workloads in order to process data more quickly. We have used an Attention layer which boost the model's quality and enables it to become more effective. The BLUE rating is used to quantify the model's potency.