Investigating the impact of satisfaction and relational capital on repurchase behavior using a text mining method

2021 ◽  
Author(s):  
Zhepeng Lv ◽  
Yue Jin ◽  
Jinghua Huang
Keyword(s):  
Text Mining ◽  
Relational Capital ◽  
Mining Method ◽  
The Impact

Infoveillance based on Social Sensors to Analyze the impact of Covid19 in South American Population (Preprint)

2020 ◽  
Author(s):  
Josimar E. Chire Saire
Keyword(s):  
Text Mining ◽  
Electronic Communication ◽  
Spanish Speakers ◽  
South American ◽  
South American Population ◽  
The People ◽  
Data Source ◽  
The Impact ◽  
General Concern ◽  
Social Sensors

BACKGROUND Infoveillance is an application from Infodemiology field with the aim to monitor public health and create public policies. Social sensor is the people providing thought, ideas through electronic communication channels(i.e. Internet). The actual scenario is related to tackle the covid19 impact over the world, many countries have the infrastructure, scientists to help the growth and countries took actions to decrease the impact. South American countries have a different context about Economy, Health and Research, so Infoveillance can be a useful tool to monitor and improve the decisions and be more strategical. The motivation of this work is analyze the capital of Spanish Speakers Countries in South America using a Text Mining Approach with Twitter as data source. The preliminary results helps to understand what happens two weeks ago and opens the analysis from different perspectives i.e. Economics, Social. OBJECTIVE Analyze the behaviour of South American Capitals in front of covid19 pandemics and show the helpfulness of Text Mining Approach for Infoveillance tasks. METHODS Text Mining process RESULTS - Argentina and Venezuela capitals are the biggest number of post during this period, opposite with Bolivia, Ecuador and Uruguay. - Most relevant users are related to mass media like radio, television or newspapers. - There is a general concern about covid19 but every country talks about different areas: Economics, Health, Environmental Impact. CONCLUSIONS Infoveillance based on Social Sensors with data coming from Twitter can help to understand the trends on the population of the capitals. Besides, it is necessary to filter the posts for processing the text and get insights about frequency, top users, most important terms. This data is useful to analyse the population from different approaches. INTERNATIONAL REGISTERED REPORT RR2-https://doi.org/10.1101/2020.04.06.20055749

scholarly journals The use of text mining to detect key shifts in Japanese first-year medical student professional identity formation through early exposure to non-healthcare hospital staff

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yayoi Shikama ◽  
Yasuko Chiba ◽  
Megumi Yasuda ◽  
Maham Stanyon ◽  
Koji Otani
Keyword(s):  
Text Mining ◽  
Professional Identity ◽  
Identity Formation ◽  
Role Models ◽  
Healthcare Professionals ◽  
Clinical Work ◽  
Working Environment ◽  
First Year ◽  
Professional Identity Formation ◽  
The Impact

Abstract Background Professional identity formation is nurtured through socialization, driven by interaction with role models, and supported through early clinical exposure (ECE) programmes. Non-healthcare professionals form part of the hospital community but are external to the culture of medicine, with their potential as role models unexplored. We employed text mining of student reflective assignments to explore the impact of socialization with non-healthcare professionals during ECE. Methods Assignments from 259 first-year medical students at Fukushima Medical University, Japan, underwent hierarchical cluster analysis. Interrelationships between the most-frequently-occurring words were analysed to create coding rules, which were applied to elucidate underlying themes. Results A shift in terms describing professional characteristics was detected, from “knowledge/skill” towards “pride [in one’s work]” and “responsibility”. Seven themes emerged: contribution of non-healthcare professionals, diversity of occupation, pride, responsibility, teamwork, patient care and gratitude. Students mentioning ‘contribution of non-healthcare professionals’ spoke of altruistic dedication and strong sense of purpose. These students expressed gratitude towards non-healthcare professionals for supporting clinical work, from a doctor’s perspective. Conclusion Socialization with non-healthcare professionals provides important insights into the hospital working environment and cultural working norms. Through role modelling altruism and responsibility, non-healthcare professionals positively influenced student professional identity formation, promoting self-conceptualisation as a doctor.

scholarly journals Relationship lending and the use of trade credit: the role of relational capital and private information

2021 ◽  
Author(s):  
Pierluigi Murro ◽  
Valentina Peruzzi
Keyword(s):  
Private Information ◽  
Business Models ◽  
Trade Credit ◽  
Banking Regulation ◽  
Manufacturing Firms ◽  
Soft Information ◽  
Relational Capital ◽  
Relationship Lending ◽  
The Impact ◽  
Italian Manufacturing Firms

AbstractUsing a unique sample of Italian manufacturing firms, we investigate the impact of relationship lending on firms’ use of trade credit. We find that firms maintaining close and long-lasting relationships with their main banks are associated with higher amounts of trade credit extended by suppliers. This result is robust to alternative measures of trade credit and relationship lending, and to different estimation techniques. We also analyze the mechanisms driving the association between relationship lending and the use of trade credit. Regression results suggest that the positive link between accounts payable and relationship lending is especially significant for firms that use to provide soft information to their lenders and for companies with greater relational abilities.Plain English Summary The existence of close and long lasting lending relationships positively affects the amount of trade credit manufacturing firms receive from their suppliers. By relying on the Survey on Italian Manufacturing Firms, we show that the positive link between relationship lending and the use of trade credit is driven by two channels: private information and relational capital. In a policy perspective, our findings reveal a need for banking regulation and supervision to encompass banking business models in evaluating banks. The current approach might not be suitable for local banks investing in soft information acquisition and could weaken SMEs’ chances to receive both bank financing and trade credit from suppliers. Moreover, from a managerial point of view, our results uncover the relevance of firms’ ability to create strong relationships with banks, suppliers, and other companies that may help alleviating financial constraints.

Complaint management model of manufacturing products using text mining and potential failure identification

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ririn Diar Astanti ◽  
Ivana Carissa Sutanto ◽  
The Jin Ai
Keyword(s):  
Quality Management ◽  
Text Mining ◽  
Failure Mode ◽  
Management System ◽  
Main Part ◽  
Mining Method ◽  
Content Type ◽  
Complaint Management ◽  
Potential Failure ◽  
Failure Identification

PurposeThis paper aims to propose a framework on complaint management system for quality management by applying the text mining method and potential failure identification that can support organization learning (OL). Customer complaints in the form of email text is the input of the framework, while the most frequent complaints are visualized using a Pareto diagram. The company can learn from this Pareto diagram and take action to improve their process.Design/methodology/approachThe first main part of the framework is creating a defect database from potential failure identification, which is the initial part of the failure mode and effect analysis technique. The second main part is the text mining of customer email complaints. The last part of the framework is matching the result of text mining with the defect database and presenting in the form of a Pareto diagram. After the framework is proposed, a case study is conducted to illustrate the applicability of the proposed method.FindingsBy using the defect database, the framework can interpret the customer email complaints into the list of most defect complained by customer using a Pareto diagram. The results of the Pareto diagram, based on the results of text mining of consumer complaints via email, can be used by a company to learn from complaint and to analyze the potential failure mode. This analysis helps company to take anticipatory action for avoiding potential failure mode happening in the future.Originality/valueThe framework on complaint management system for quality management by applying the text mining method and potential failure identification is proposed for the first time in this paper.

scholarly journals Social Relational Capital as a Community Economic Development Factor

2019 ◽  
Vol IV (I) ◽  
pp. 108-119
Author(s):  
Mujib Ur Rahman ◽  
Muhammad Faizan Malik ◽  
Wisal Ahmad
Keyword(s):  
Economic Development ◽  
Business Community ◽  
Community Economic Development ◽  
Relational Capital ◽  
Purposive Sampling ◽  
Development Factor ◽  
High Investment ◽  
The Impact ◽  
The Relationship

The paper examined the impact of relational capitals on community economic development. For this purpose, the handloom business community was taken as a case study from Peshawar Valley. Data was collected through purposive sampling from169 handlooms firms. The results concluded that the impact of relational capital is significant, and the relationship is positive. This study hereby suggests that government and policymakers should invest in making ties and a strong network of firms within and outside of the community; hence with high investment in making strong social-relational capital can develop the entire entrepreneurial communities.

scholarly journals Feature Selection Optimization for Highlighting Opinions Using Supervised and Unsupervised Learning on Arabic Language

2021 ◽  
Vol 10 (2) ◽  
pp. 636-642
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Text Mining ◽  
Unsupervised Learning ◽  
Arabic Language ◽  
Machine Learning Techniques ◽  
Business Decision ◽  
Supervised And Unsupervised Learning ◽  
Proposed Model ◽  
The Impact

Text mining utilizes machine learning (ML) and natural language processing (NLP) for text implicit knowledge recognition, such knowledge serves many domains as translation, media searching, and business decision making. Opinion mining (OM) is one of the promised text mining fields, which are used for polarity discovering via text and has terminus benefits for business. ML techniques are divided into two approaches: supervised and unsupervised learning, since we herein testified an OM feature selection(FS)using four ML techniques. In this paper, we had implemented number of experiments via four machine learning techniques on the same three Arabic language corpora. This paper aims at increasing the accuracy of opinion highlighting on Arabic language, by using enhanced feature selection approaches. FS proposed model is adopted for enhancing opinion highlighting purpose. The experimental results show the outperformance of the proposed approaches in variant levels of supervisory,i.e. different techniques via distinct data domains. Multiple levels of comparison are carried out and discussed for further understanding of the impact of proposed model on several ML techniques.

Measuring the impact of relational capital on customer performance in the SME B2B sector

2017 ◽  
Vol 23 (6) ◽  
pp. 1144-1166 ◽  
Author(s):  
Lara Agostini ◽  
Anna Nosella ◽  
Benedetta Soranzo
Keyword(s):  
Absorptive Capacity ◽  
Open Innovation ◽  
Moderating Effect ◽  
High Tech ◽  
Relational Capital ◽  
Marketing Capability ◽  
Content Type ◽  
Innovation Practices ◽  
External Actors ◽  
The Impact

Purpose The purpose of this paper is to investigate the influence that different components of relational capital (marketing capability, open innovation with business and scientific partners, technological reputation, brand) have on customer performance (CP). Moreover, the moderating effect of absorptive capacity on such relationships is tested. Design/methodology/approach First, the direct relationship between the different components of relational capital and CP is analyzed through a linear regression model. Then, to test the moderating effect, two distinct regression analyses are conducted into two sub-samples, defined according to the level of absorptive capacity. The authors carried out these analyses on a sample of 150 small- and medium-sized enterprises (SMEs) in the medium- and high-tech B2B context. Findings Results of this study prove that CP is enhanced through firm marketing capability, open innovation with business partners and technological reputation, while brand and open innovation with scientific partner do not have an association with CP. In particular, the impact of marketing capability and open innovation with business actors on CP is greater for firms with higher absorptive capacity. Research limitations/implications This paper, highlighting the relevance of relational capital and absorptive capacity in improving CP, enhances our knowledge about the factors that help to strengthen the relationships with customers, which is an under-investigated issue especially for SMEs competing in B2B industries, and extends our knowledge on open innovation practices. Practical implications Findings of this paper suggest that, to achieve better CP, managers should pay special attention to nurturing their marketing capability and high-quality relationships with external actors and invest in absorptive capacity to enhance the positive effect of such linkages. Originality/value This work, combining the external perspective of relational capital and the internal organizational dimension of absorptive capacity, provides valuable insights about the knowledge and resource mix that firms might rely on to achieve better customer satisfaction and loyalty.

Sign in / Sign up

Export Citation Format

Share Document