Big data, knowledge co-creation and decision making in fashion industry

The influencing mechanism of big data analytics technology capability on enterprise's operational performance: The mediating role of data-tool fit

With the development of network technology, enterprises face the explosive growth of data every day. Therefore, to fully mine the value of massive data, big data analysis (BDA) technology has become the key to developing the core competitiveness of enterprises. However, few empirical studies have investigated the influencing mechanism of the BDA capability of an enterprise on its operational performance. To fill this gap, this study explores how BDA technology capability influences enterprise operation performance, based on dynamic capabilities theory and resource-based theory. It proposes the key variables, including the connectivity, compatibility, and modularization of big data analysis technical capability, enterprise's operational performance, and the fit between data and tools, to establish a model and study the correlation between the variables. The results highlight the mediating role of data-tool fit in the relationships between BDA capability and the enterprise's operational performance, which is a major finding that has not been underlined in the extant literature. This study provides valuable insight for operational managers to help them in mobilizing BDA capability for enterprises' operational management and improving operational performance.

A scholarly network of AI research with an information science focus: Global North and Global South perspectives

This paper primarily aims to provide a citation-based method for exploring the scholarly network of artificial intelligence (AI)-related research in the information science (IS) domain, especially from Global North (GN) and Global South (GS) perspectives. Three research objectives were addressed, namely (1) the publication patterns in the field, (2) the most influential articles and researched keywords in the field, and (3) the visualization of the scholarly network between GN and GS researchers between the years 2010 and 2020. On the basis of the PRISMA statement, longitudinal research data were retrieved from the Web of Science and analyzed. Thirty-two AI-related keywords were used to retrieve relevant quality articles. Finally, 149 articles accompanying the follow-up 8838 citing articles were identified as eligible sources. A co-citation network analysis was adopted to scientifically visualize the intellectual structure of AI research in GN and GS networks. The results revealed that the United States, Australia, and the United Kingdom are the most productive GN countries; by contrast, China and India are the most productive GS countries. Next, the 10 most frequently co-cited AI research articles in the IS domain were identified. Third, the scholarly networks of AI research in the GN and GS areas were visualized. Between 2010 and 2015, GN researchers in the IS domain focused on applied research involving intelligent systems (e.g., decision support systems); between 2016 and 2020, GS researchers focused on big data applications (e.g., geospatial big data research). Both GN and GS researchers focused on technology adoption research (e.g., AI-related products and services) throughout the investigated period. Overall, this paper reveals the intellectual structure of the scholarly network on AI research and several applications in the IS literature. The findings provide research-based evidence for expanding global AI research.

Building SMEs’ resilience in times of uncertainty: the role of big data analytics capability and co-innovation

Purpose

This paper aims to enhance the small and medium enterprises’ (SMEs) ability to develop resilience in the face of any turbulences, addressing the question on how these organizations can maintain business continuity when faced with a critical event.

Design/methodology/approach

A mediated regression analysis is conducted to investigate the relationships among big data analytics (BDA) capabilities, coinnovation (CI) and organizational resilience (OR) with reference to 192 big data SMEs in Europe.

Findings

Research reveals that the BDA capability and CI are positively associated with OR. Moreover, this study discovers the mediating impact of CI on the relationship between BDA capability and OR.

Originality/value

This paper provides important implications for considering CI as a viable strategy especially in a time of crisis and shows how SMEs are more able to recognize business opportunities. The microfoundations of the resilience building capacity of SMEs are also identified. These microfoundations become recommendations for practitioners to enhance SMEs’ responsiveness in light of coronavirus-related crises.

Green service production and environmental performance in healthcare emergencies: role of big-data management and green HRM practices

Purpose

This study aims to examine the impact of big data management on green service production (GSP) and environmental performance (ENPr) while considering green HRM practices (GHRM) in healthcare emergencies.

Design/methodology/approach

The authors collected primary data from major healthcare organizations in India by surveying healthcare professionals. The data analysis through structural equation modelling (PLS-SEM) reveals several significant relationships to extricate the underlying dynamics.

Findings

Grounded in the theories of service production and natural resource-based view (NRBV), this study conceptualizes GSP with its three dimensions of green procurement (GP), green service design (GSD) and green service practices (GSPr). The study conducted in India's healthcare sector with a sample size limited to healthcare professionals serving in COVID-19 identifies the positive and significant impact of big data management on GSP and ENPr that organizations seek to deploy in such emergencies. The findings of the study explain the moderating effects of GHRM on GSP-ENPr relationships.

Research limitations/implications

The study was conducted in the healthcare sector in India, and its sample size was limited to healthcare professionals serving in COVID-19. The practical ramifications for healthcare administrators and policymakers are suggested, and future avenues of research are discussed.

Originality/value

This paper develops a holistic model of big data analytics, GP, GSD, GSPr, GHRM and ENPr. This study is a first step in investigating how big data management contributes to ENPr in an emergency and establishing the facets of GSP as a missing link in this relationship, which is currently void in the literature. This study contributes to the theory and fills the knowledge gap in this area.

Implementing Online Product Reviews and Muslim Fashion Innovation for Resilience during the New Normal in Indonesia

The COVID-19 pandemic in Indonesia has harmed the fashion sector, particularly SMEs (small and medium-sized enterprises). In the wake of the epidemic, the Muslim Fashion Shop (MFS) sector has experienced a drop in sales. Therefore, developing innovative products and excellent customer approaches are critical to MFS resilience. This pandemic has additionally affected the shift from offline to online sales channels. Online sales features, referred to as online product reviews (OPRs), allow customers to leave comments or evaluations. OPRs are one of the sources of product feature information, and are a means of increasing valued for online consumers that some companies are currently underutilizing. In order to develop Muslim fashion designs, this project performed OPRs. The purpose of this study is to show the benefits of OPRs in the development of new Muslim fashion products in Indonesia in order to assist businesses in surviving in the new normal era. The first phase of OPR data collection at Shopee was carried out in five steps. OPR data were collected in Shopee using NVivo’s N-Capture QSR. The data obtained from phase one were needed in order to equalize perceptions and make corrections using the member check obtained data OPR method using Focus Group Discussion (FGD). The second phase consisted of eight steps. This phase sharpened the results of phase one using expert judgement word frequency analysis in NIVO. The third and final phase analysed the fashion industry’s new normal innovation approach. This research shows the usefulness of OPR data for the evolution of fashion design in Indonesia, among other findings. According to this study, companies’ expertise, experience, and design innovation are essential variables in a changing/disruptive marketplace. Ongoing research suggests utilizing OPRs to generate new design trends, high-quality products, and innovative tactics in order to sustain Muslim fashion business.

Investigating the Impact of Big Data Analytics on Perceived Sales Performance: The Mediating Role of Customer Relationship Management Capabilities

A persistent question for information technology researchers and practitioners is how big data analytics (BDA) can improve sales performance. Therefore, this study proposed a research model to investigate the impact of BDA on perceived sales performance in accordance with the resource-based view (RBV) and dynamic capability theory. The 416 valid responses collected from the employees of pharmaceutical organizations were analyzed using structural equation modelling to test the proposed research model. Results indicated that the BDA and customer relationship management (CRM) capabilities shared a strong positive impact on perceived sales performance. BDA, as organizational resources, creates organizational dynamic capabilities, such as CRM capabilities. BDA and CRM capabilities can influence perceived sales performance. Furthermore, CRM capabilities have a significant mediating impact on the relationships between BDA and perceived sales performance. This study also highlighted the practical and theoretical implications of the proposed model, the research limitations, and the future research directions.

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.

Big data for small and medium-sized enterprises (SME): a knowledge management model

Purpose

Big data has raised challenges and opportunities for business, the information technology (IT) industry and research communities. Nowadays, small and medium-sized enterprises (SME) are dealing with big data using their limited resources. The purpose of this paper is to describe the synergistic relationship between big data and knowledge management (KM), analyze the challenges and IT solutions of big data for SME and derives a KM model of big data for SME based on the collected real-world business cases.

Design/methodology/approach

The study collects eight well-documented cases of successful big data analytics in SME and conducts a qualitative data analysis of these cases in the context of KM. The qualitative data analysis of the multiple cases reveals a KM model of big data for SME.

Findings

The proposed model portrays the synergistic relationship between big data and KM. It indicates that strategic use of data, knowledge guided big data project planning, IT solutions for SME and new knowledge products are the major constructs of KM of big data for SME. These constructs form a loop through the causal relationships between them.

Research limitations/implications

The number of cases used for the derivation of the KM model is not large. The coding of these qualitative data could involve biases and errors. Consequently, the conceptual KM model proposed in this paper is subject to further verification and validation.

Practical implications

The proposed model can guide SME to exploit big data for business by placing emphasis on KM instead of sophisticated IT techniques or the magnitude of data.

Originality/value

The study contributes to the KM literature by developing a theoretical model of KM of big data for SME based on underlying dimensions of strategic use of data, knowledge guided big data project planning, IT solutions for SME and new knowledge products.

Transformational leadership and employee efficiency: knowledge sharing as mediator

Purpose

This research aims to examine the influence of transformational leadership on employee efficiency. The research also examines the role of knowledge sharing as a mediator between transformational leadership and employee efficiency.

Design/methodology/approach

The research is based on the survey conducted among 200 employees of logistics firms. Exploratory Factor analysis (EFA) and Confirmatory Factor Analysis (CFA) approaches are used for the evaluation.

Findings

The study found that transformational leadership has positive and significant influence on employee efficiency. The research also demonstrates that after introducing knowledge sharing, it fully mediated the influence of transformational leadership on employee efficiency. The study suggests that, if leaders share their knowledge and expertise among the team, employees have a propensity to be highly effective and efficient than without knowledge sharing.

Research limitations/implications

Blue collar staff and unskilled labors of the firms are not included in the study. So, the study is limited to white collar staff only which can further be expanded by considering other ground staff. Also few or no such researches have been conducted in logistics firms, particularly in Indian logistics firms. So, the result of this study can be used as reference to explore the area. This study can be replicated in the logistics firms of other regions also.

Practical implications

The finding of the study will help the top management of the organizations to formulate strategies to enhance its senior-subordinate relationship through knowledge sharing. The study also suggests that regular dissemination of knowledge among the team improves the efficiency of the team members and hence the performance of the organization.

Originality/value

This research examines the degree to which knowledge sharing acts as a mediator between transformational leadership and employee efficiency, which has not been found in previous studies.

Exchange characteristics, capability upgrading and innovation performance: evidence from Latin America

Purpose

This paper aims to attempt to examine the effect of firm-customer exchange characteristics, frequency and specificity, on the likelihood of the firm to generate customer-driven innovation. The authors draw from social capital theory and argue that repetitive and customer-specific exchange improves the trusts between firm and customers, which in turn ease the flows of tacit knowledge from customers to the firm. From the perspective of customer knowledge management, the authors contribute by examining the mechanism by which a firm can acquire knowledge from and about customers. The authors further argue that a firm’s ability to absorb knowledge from customers and turn them into innovation also depends on its internal capability. A firm that consistently upgrades its capacity is more likely to generate customer-driven innovation than those that do not. Also, the authors argue that the joint effect of exchange characteristics and internal capability upgrading can further increase the likelihood of customer-driven innovation. Such a joint force implies the positive moderating effect of internal capability upgrading to the relationship between exchange characteristics and customer-driven innovation.

Design/methodology/approach

The authors test the hypotheses on 3,000 firms from six countries in Latin America. They take advantage of the 2017 World Bank Enterprises Survey. This most recent of the survey asks questions on various types of innovation and firm-customers exchange characteristics and other firm-level variables.

Findings

The authors find support for our hypotheses that repeated exchange and exchanges tailored to specific customers have a positive effect on customer-driven innovation. Also, they find the support that internal capability upgrading, in the form of investment in product design, marketing and organizational development has a positive effect on customer-driven innovation. The authors also find that investment in product design positively moderates the impact of exchange characteristics on the likelihood of customer-driven innovation.

Originality/value

While past studies focus on strategies to acquire and manage customers’ knowledge, little has been said about how exchange attributes can encourage or discourage innovation? This question is important because various theoretical perspectives may have a different prediction on the effect of firm-customer relationship and innovation. This study attempts to bridge such theoretical tension.

Big data analytics capability and co-innovation: An empirical study

There are numerous emerging studies addressing big data and its application in different organizational aspects, especially regarding its impact on the business innovation process. This study in particular aims at analyzing the existing relationship between Big Data Analytics Capabilities and Co-innovation. To test the hypothesis model, structural equations by the partial least squares method were used in a sample of 112 Colombian firms. The main findings allow to positively relate Big Data Analytics Capabilities with better and more agile processes of product and service co-creation and with more robust collaboration networks with stakeholders internal and external to the firm.

Building supply chain risk resilience

Purpose

The purpose of this paper is to examine how firms can develop business risk resilience from supply chain disruption events, by developing big data analytics (BDA) capabilities within their organization. The authors test whether BDA mediates the impact of institutional response to supply chain disruption events, and information technology infrastructure capabilities (ITICs), on firm’s ability to develop risk resilience from supply chain disruption events.

Design/methodology/approach

The study is based on survey data collected from 225 firms, spread across several sectors in the USA and Europe. The respondents are primarily senior and middle management professionals who have experience within the information technology (IT) and supply chain domain. Validity and reliability analyses were performed using SPSS and AMOS; and covariance-based structural equation modeling was used to test the hypothesis.

Findings

The analysis reveals two significant findings. First, the authors observe that institutional experience with managing supply chain disruption events has a negative impact on firm’s ability to develop business risk resilience. However, if the organizations adopt BDA capabilities, it enables them to effectively utilize resident firm knowledge and develop supply chain risk resilience capacity. The results further suggest that BDA positively adds to an organization’s existing IT capabilities. The analysis shows that BDA mediates the impact of ITIC on the organization’s ability to develop risk resilience to supply chain disruption events.

Originality/value

This study is one of the few works that empirically validate the important role that BDA capabilities play in enabling firms develop business risk resilience from supply chain disruption events. The study further provides a counterpoint to the existing perspective within the supply chain risk management literature that institutional experience of managing past supply chain disruption events prepares the organization to deal with future disruption events. This paper adds to our understanding of how, by adopting BDA capabilities, firms can develop supply chain risk resilience from disruption events.

A dual-pathway model of knowledge exchange: linking human and psychosocial capital with prosocial knowledge effectiveness

Purpose

This study aims to examine a dual-pathway model that recognizes two distinct (formal and informal) but complementary mechanisms of knowledge exchanges – knowledge sharing and knowledge helping. It also investigates how team members use their limited human and psychosocial capital for prosocial knowledge effectiveness.

Design/methodology/approach

A survey-based approach was used to examine the hypotheses of the study. A moderated-mediation model was proposed and tested using bootstrap approach.

Findings

Knowledge sharing and knowledge helping were found to be the significant links through which human capital (capability) and psychosocial capital (motivation and efficacy) significantly predict prosocial knowledge effectiveness. Post hoc analysis suggests that human capital through knowledge sharing influences team learning, whereas the psychosocial capital through knowledge helping influences team leadership.

Originality/value

The present study found two distinct but complementary and yet necessary mechanisms of knowledge exchanges to be linked as the important outlay for the human and psychosocial capital to be effective in the prosocial knowledge behaviours.