Identifying customer knowledge on social media through data analytics

Exploring trade-offs between basic and advanced sustainable supply chain practices among consumer electronics: a social media analytics approach

Purpose

This study explores the sustainable supply chain trade-offs in the electronics industry.

Design/methodology/approach

The study employs a social media analytics approach and analyses Twitter posts from August 2017 to December 2021. Thematic analysis is applied to discover the pattern in sustainable supply chain trade-offs based on the consumers' perceptions. In addition, a chi-square test was used to measure whether a relationship exists between product groups and sustainable supply chain perceptions.

Findings

The results indicate that environmental practices are the most frequent topic among consumers on social media. Further, although basic sustainable supply chain practices are prioritised in the environmental aspect, advanced sustainable supply chain practices take precedence over basic ones in the social dimension. The result from the chi-square independence test reveals that there is no significant relationship between different products and perceptions of consumers except for economically advanced sustainable supply chain practices.

Practical implications

The main implications of the present study are to offer a fast and efficient method to marketers and companies for discovering customer perceptions. In a way, they can identify where the quality of practices needs to improve in their supply chains to gain customer satisfaction. Additionally, the authors suggest industries declare their trade-off preferences between sustainable supply chain practices transparently.

Originality/value

The findings extend the abundance of sustainable supply chain literature by identifying the sustainable supply chain trade-offs among consumer electronics. Also, the reason for customers' dissatisfaction is provided. In the end, six propositions are presented based on the explorations.

The role of enterprise information systems strategies enabled strategy-making on organizational innovativeness: a resource orchestration perspective

Purpose

The paper aims to establish the role of enterprise information systems strategies (ISS) enabled by business strategies for attaining organizational innovativeness (ORIN) mediated by performance (decision-making and business processes) under environmental turbulence.

Design/methodology/approach

The research framework is developed based on theoretical grounding and validated with the help of 408 responses from Brazil using SmartPLS path modeling.

Findings

The results of the research suggest that the resource orchestra of enterprise information systems strategy-enabled strategy-making can be a viable alternative to enhance innovation activities in the organizations through the mediated role of performance (decision-making and business process).

Practical implications

The research demonstrates the role of business function (information systems) strategy enabled overall business strategy-making for achieving innovations in the organization. Fortune organizations are exploiting the information systems strategy enabled business strategy for innovations in the organization; such as Amazon, Walmart, Costco, etc.

Originality/value

The proposed and validated model is a contribution to the enterprise information systems strategy theory. This model presents the role of resource orchestras in achieving innovations in organizations.

Measuring SERVQUAL dimensions and their importance for customer-satisfaction using online reviews: a text mining approach

Purpose

The extant literature has utilized the SERVQUAL scale to measure service quality dimensions and their importance towards customer-satisfaction using close-ended survey-based questions and not open-ended questions and/or user-generated qualitative responses. On the other hand, while measuring customer-satisfaction drivers from user-generated content (UGC), extant studies have majorly used overall or aspect-wise evaluations and not evaluations specific to SERVQUAL dimensions. In this study, the authors try to bridge the gap.

Design/methodology/approach

The authors suggest a methodology consisting of text mining, machine learning and econometric techniques that can measure consumer evaluations of SERVQUAL dimensions. The authors used qualitative and quantitative UGC obtained from 27,052 online reviews on 362 airlines by reviewers of 158 nationalities for our analysis.

Findings

The authors established a unique method which combines qualitative and qualitative UGC to measure service quality. The authors have also uncovered the comparative importance of such dimensions in creating customer-satisfaction and recommendation in the context of the airline industry.

Originality/value

The paper is one of the pioneering studies that try to find measures of SERVQUAL dimensions from online consumer reviews and their influence on customer satisfaction.

Using data mining to track the information spreading on social media about the COVID-19 outbreak

Purpose

COVID-19, a causative agent of the potentially fatal disease, has raised great global public health concern. Information spreading on the COVID-19 outbreak can strongly influence people behaviour in social media. This paper aims to question of information spreading on COVID-19 outbreak are addressed with a massive data analysis on Twitter from a multidimensional perspective.

Design/methodology/approach

The evolutionary trend of user interaction and the network structure is analysed by social network analysis. A differential assessment on the topics evolving is provided by the method of text clustering. Visualization is further used to show different characteristics of user interaction networks and public opinion in different periods.

Findings

Information spreading in social media emerges from different characteristics during various periods. User interaction demonstrates multidimensional cross relations. The results interpret how people express their thoughts and detect topics people are most discussing in social media.

Research limitations/implications

This study is mainly limited by the size of the data sets and the unicity of the social media. It is challenging to expand the data sets and choose multiple social media to cross-validate the findings of this study.

Originality/value

This paper aims to find the evolutionary trend of information spreading on the COVID-19 outbreak in social media, including user interaction and topical issues. The findings are of great importance to help government and related regulatory units to manage the dissemination of information on emergencies, in terms of early detection and prevention.

Elucidation of big data analytics in banking: a four-stage Delphi study

Purpose

In today's networked business environment, a huge amount of data is being generated and processed in different industries, which banking is amongst the most important ones. The aim of this study is to understand and prioritize strategic applications, main drivers, and key challenges of implementing big data analytics in banks.

Design/methodology/approach

To take advantage of experts' viewpoints, the authors designed and implemented a four-round Delphi study. Totally, 25 eligible experts have contributed to this survey in collecting and analyzing the data.

Findings

The results revealed that the most important applications of big data in banks are “fraud detection” and “credit risk analysis.” The main drivers to start big data endeavors are “decision-making enhancement” and “new product/service development,” and finally the focal challenge threatening the efforts and expected outputs is “information silos and unintegrated data.”

Originality/value

In addition to stepping forward in the literature, the findings advance our understanding of the main managerial issues of big data in a dynamic business environment, by proposing effective further actions for both scholars and decision-makers.

Differential effects of online signals on sales performance of local brand clothing products

Purpose

Businesses need to make quick decisions and adjustments to fulfill the growing online demand. Previous studies examined various factors affecting the online sales performance of products such as books, electronics and movies; however, they paid limited attention toward the local brand clothing products. The current study investigates the importance of different kinds of seller-generated and consumer-generated signals such as price, discount, product ratings, review volume, review sentiment, number of questions and interaction between some of these factors for predicting the sales performance of clothing products.

Design/methodology/approach

The multiple linear regressions has been employed to investigate the influence of various predictor variables on sales performance. The study also examines the importance of these predictor variables by using different machine learning models, including random forest (RF), neural networks and support vector regression (SVR).

Findings

The findings of the study emphasize the importance of price and discount rates offered on the product. The quantitative characteristics of reviews, such as review volume and average rating, have been found to be more important predictors than sentiment strengths. However, the sentiment strength of reviews with higher helpfulness scores plays a significant role in predicting sales performance.

Originality/value

The study highlights the varying importance of seller-based and consumer-based signals in predicting sales performance. It also investigates the interaction effect of these two kinds of signals. The consumer-generated signals have been further divided into two components based on social influence theory, and the interaction effects of these components have also been examined.

Does marketing analytics capability boost firms' competitive marketing performance in data-rich business environment?

Purpose

Few well-documented studies have explained the importance of researching firms' marketing analytics capability (FMAC). In spite of its significance, there is scant attention to conceptualising and empirically investigating FMAC and its consequences in a data-driven business context. Thus, this study aims to develop and test a conceptual model that relates FMAC and its repercussions in the data-rich business environment.

Design/methodology/approach

This study analysed the data from 250 managers amongst large and medium-sized manufacturing and service-intensive firms. Furthermore, this research performed an empirical study by using operationalised questionnaire survey method to verify the hypotheses and reach its theoretical and managerial implications. Structural equation modelling with maximum-likelihood estimation method was applied to verify the validity of the proposed research model.

Findings

Multivariate analysis results show that FMAC significantly influences firms' competitive marketing performance (FCMP) with the presence of holistic marketing decision-making (HMDM) as a mediator. Moreover, adoption of artificial intelligence (AAI) enhances the relationship of FMAC-HMDM and FMAC-FCMP linkages.

Practical implications

This study analyses how FMAC can enhance FCMP and contributes to resource-based views and technological capability theories. From a managerial perspective, guidelines are provided for marketers to adopt advance technologies, such as AI, to optimise FMAC and HMDM to achieve competitive marketing performance.

Originality/value

Believing that “how to be competitive in marketing performance under data-rich-environment”, this research is the first to use the data of a firm manager to facilitate the understanding of FMAC, which provides a new direction for improving marketing performance. In addition, HMDM and AAI are also proposed for firms to optimise FCMP.

Influence of political leaders on sustainable development goals – insights from twitter

Purpose

The importance and criticality of sustainable development goals is witnessed by 195 member countries. For its full-fledged adoption and implementation, it needs to be understood by masses and political leaders are critical agents those engage diverse communities through social media such as twitter. Therefore, in this study focuses on how political leaders can influence the sustainable development goals through Twitter.

Design/methodology/approach

This study examines the social media conversations of political leaders on Twitter. Social media analytics methods such as sentiment mining, topic modelling and content analysis-based methods have been used.

Findings

The findings indicate that most political leaders are primarily discussing the sustainable development goals (SDGs) “partnership for goals” and “peace, justice and strong institutions”. Many other goals such as “clean water and sanitation”, “life below water”, “zero hunger”, “no poverty” and “educational quality” are not being focused on.

Research limitations/implications

This study offers implications in terms of collective decision making and the role of policy makers towards the goals of promoting SDGs. The authors highlight how political leaders need to involve key stakeholders in this journey.

Originality/value

This study scores and provides a cohort-specific prioritization of the leadership within these countries with regard to SDGs, which could be beneficial to the society.

CSR Communication through Social Media: A Litmus Test for Banking Consumers’ Loyalty

Prior literature in the field of corporate social responsibility (CSR) has largely focused on investigating its relationship with organizational-related outcomes, whereas the impact of CSR on consumer behavior is largely ignored in the recent literature. Further, most of the prior studies have investigated CSR with a philanthropic viewpoint, but its importance in achieving marketing-related outcomes is something that is to date, underexplored. Hence, the aim of the present study is to investigate the impact of CSR communication through social media on consumer loyalty with the mediating effect of consumers’ brand admiration in the banking sector of Pakistan. The banking sector was selected due to the reason that this sector is homogenized in nature and creating consumers’ loyalty due to this homogenized character of this sector is challenging. The data of the present study were collected from different banking consumers through an adapted questionnaire on a five-point Likert scale. A total of 448 fully filled questionnaires were received which included 289 male and 159 female banking consumers. The results of the present study revealed that CSR communications through social media have a positive impact on consumer loyalty, and consumers’ brand admiration partially mediates this relationship. The findings of the present study would help policymakers from banking institutions to use CSR strategy from the perspective of marketing which is undoubtedly very important for every organization in the current digital age.

A Novel Framework for Identifying Customers’ Unmet Needs on Online Social Media Using Context Tree

Customer needs and user contexts play an important role in generating ideas for new products or new functions. This study proposes a novel framework for identifying customers’ unmet needs on online social media using the Context Tree through the Hierarchical Search of Concept Spaces (HSCS) algorithm. The Context Tree represents the hierarchical structure of nodes associated with related keywords and corresponding concept spaces. Unlike other methods, the Context Tree focuses on finding the unmet needs of customers from online social media. The proposed framework is applied to extract customer needs for home appliances. Identified customer needs are used to make user scenarios, which are used to develop new functions of home appliances.

The role of social media in e-recruitment process: empirical evidence from developing countries in social network theory

Purpose

This study aims at demonstrating how social media shape the recruitment and selection processes of individuals in developing countries. It further explores the impacts of social media on business productivity, cost efficiency, widening of search, less employee turnover and competitive advantage mediated by adopting e-recruitment processes. This research adopts social network theory to discuss the findings and highlight the new mechanisms that legitimise business manipulation in e-recruitment process by exploring the usage of social media.

Design/methodology/approach

Secondary data based on literature review is triangulated with 37 semi-structured qualitative interviews with managerial and non-managerial members of staff.

Findings

The findings show that e-recruitment has immense advantages to businesses. However, the authors also consider the dark side of social media and e-recruitment process by considering social network theory as a manipulation tool in organisations of developing countries.

Originality/value

Having adopted the social network theory, this research highlights the new mechanisms that legitimise business manipulation in e-recruitment process. Thus, it demonstrates technological advancements that reshape the dynamics of social networks and recruitment processes.

Exploring the impact of short-text complexity and structure on its quality in social media

Purpose

The purpose of this paper is to explore to which extent the quality of social media short text without extensions can be investigated and what are the predictors, if any, of such short text that lead to trust its content.

Design/methodology/approach

The paper applies a trust model to classify data collections based on metadata into four classes: Very Trusted, Trusted, Untrusted and Very Untrusted. These data are collected from the online communities, Genius and Stack Overflow. In order to evaluate short texts in terms of its trust levels, the authors have conducted two investigations: (1) A natural language processing (NLP) approach to extract relevant features (i.e. Part-of-Speech and various readability indexes). The authors report relatively good performance of the NLP study. (2) A machine learning technique in more precise, a random forest (RF) classifierusing bag-of-words model (BoW).

Findings

The investigation of the RF classifier using BoW shows promising intermediate results (on average 62% accuracy of both online communities) in short-text quality identification that leads to trust.

Practical implications

As social media becomes an increasingly new and attractive source of information, which is mostly provided in the form of short texts, businesses (e.g. in search engines for smart data) can filter content without having to apply complex approaches and continue to deal with information that is considered more trustworthy.

Originality/value

Short-text classifications with regard to a criterion (e.g. quality, readability) are usually extended by an external source or its metadata. This enhancement either changes the original text if it is an additional text from an external source, or it requires text metadata that is not always available. To this end, the originality of this study faces the challenge of investigating the quality of short text (i.e. social media text) without having to extend or modify it using external sources. This modification alters the text and distorts the results of the investigation.

Linkage between social identity creation and social networking site usage: the moderating role of usage intensity

Purpose

Interaction and communication through social networking sites (SNSs) has witnessed exponential growth every year. The rising popularity of this platform has made researchers take a deeper look at this phenomenon and try and study it in a structured fashion. The purpose of this study is to investigate the moderating role of SNS usage intensity on the relationship between social capital and social identity. There are very few published literature studies available and none in the context of a developing economy, which is undergoing enormous digital transformation. While studies like this have been done in abundance in the Western world, it is still a new approach in this part of the world. Even though the variables that are being studied have been adapted from the work done earlier by other researchers, the application and interpretation are very different, primarily because of the context.

Design/methodology/approach

A primary online survey was conducted to collect data for this study. A majority of 258 respondents were in the age group of 20–40 years. Most of them had an undergraduate and/or a postgraduate degree and spent an average of 70% of their weekly time on social media. The sample size was balanced in terms of gender (male/female) as well. To validate the research model and test the hypotheses of the study, through two analysis phases including measurement model and structural model, reliability analysis, confirmatory factor analysis (CFA), correlations and hierarchical multiple regression were deployed. The CFA was applied to assess the validity of the four factors under study.

Findings

Factors that were studied in this article were checked for content validity and reliability. Cronbach's alpha values were <1.0 indicating the reliability of the factors taken for the study. Hierarchical multiple regression showed that with the increase in bridging and SNS usage intensity, social identity also increases at a high level of bridging. Similar results were observed when regression was conducted for bonding and SNS usage intensity. Thus, the hierarchical multiple regression analysis showed that SNS usage intensity positively moderated the effects of social capital on social identity. Hence, the two hypotheses were supported.

Originality/value

The results of this study are significant for business organizations and society as well. A similar type of study in the context of an economy, which has embarked on the path of digitization as a state-sponsored policy has not been reported.

Customer Knowledge Management in SMEs Facing Digital Transformation

This paper provided a novel definition of customer knowledge management (CKM) as the logical intersection of customer relationship management (CRM) and knowledge management (KM). The main aim was to investigate the digital technologies supporting small and medium enterprises (SMEs) operating in creative industries in their customer knowledge management strategies. To achieve this aim, a survey involving 73 handicraft and/or retail SMEs operating in luxury jewelry industry was conducted. The survey results pointed out that in a few years the scenario has changed and that surveyed SMEs make more intensive use of traditional technologies supporting customer knowledge management processes rather than more innovative digital technologies, which are also cheap and easy to use. This finding showed the difficulties of SMEs operating in creative industries to be responsive to the rapid technological changes that are affecting CKM, as well as the lack of support from information technology vendors in the decision-making process for choosing adequate digital systems.

Innovating the customer loyalty program with social media

Purpose

Few academic studies specifically investigate how businesses can use social media to innovate customer loyalty programs. The purpose of this paper is to present an in-depth case study of the Shop Your Way (SYW) program, which is regarded as one of the most successful customer loyalty programs with social media.

Design/methodology/approach

This paper uses case study research as the methodology to uncover innovative features associated with the SYW customer loyalty program. The authors collected the data from SYW’s social media forums and tweets. The data set was analyzed using social media analytics tools including the R package and Lexicon.

Findings

Based on the research results, the authors summarize innovative social media features identified from SYW. The authors also provide insights and recommendations for businesses that are seeking to innovate their customer loyalty programs using social media technologies.

Originality/value

The results of this case study set a good example for businesses which want to innovate and improve their customer loyalty programs using social media technologies. This is the first in-depth case study on the SYW program, one of the most successful customer loyalty programs with social media. The results shed light on how social media can innovate customer loyalty programs in both theory and practice.

Opinion Mining on Social Media Data: Sentiment Analysis of User Preferences

Any brand’s presence on social networks has a significant impact on emotional reactions of its users to different types of posts on social media (SM). If a company understands the preferred types of posts (photo or video) of its customers, based on their reactions, it could make use of these preferences in designing its future communication strategy. The study examines how the use of SM technology and customer-centric management systems could contribute to sustainable business development of companies by means of social customer relationship management (sCRM). The two companies included in the study provide a general consumer good in the beverage industry. As such, it may be said that users interacting with the posts these companies make on their official channels are in fact customers or potential customers. The study aims to analyze customer reaction to two types of posts (photos or videos) on six social networks: Facebook, Twitter, Instagram, Pinterest, Google+ and Youtube. It brings evidence on the differences and similarities between the SM customer behaviors of two highly competitive brands in the beverage industry. Drawing on current literature on SM, sCRM and marketing, the output of this study is the conceptualization and measurement of a brand’s SM ability to understand customer preferences for different types of posts by using various statistical tools and the sentiment analysis (SA) technique applied to big sets of data.