Search results

The purpose of this study is to understand the strategy adopted by family firms in using social media for their business. Based on the social media usage, this paper attempts to segment family firms. To do so, a reactive – proactive – innovative (RPI) scale was developed for the study. Then, the family firms were categorised as reactive, proactive or innovative social media users. Further, based on the scale developed, clusters were created. Family firms were placed into different clusters based on the strategy that they had for using social media platforms for their business.

A pilot sample of 50 family firms and a main study of 256 Indian family firm entrepreneurs were surveyed through self-administered questionnaires. Factor analysis reduced the 12 scale-based questions to three distinct factors. Confirmatory factor analysis was then conducted on the main sample to confirm the constructs identified using exploratory factor analysis. Cluster analysis was used to build clusters of entrepreneurs who use social media as part of their digital marketing strategy.

Findings reveal that the Indian family firm market is largely divided into four main segments. These segments represent distinct behaviours with respect to the use of social media. The four segments of family firm entrepreneurs were named as high rollers, ignorant inhabitants, trend-setters, combative crowd based on their social media usage behaviour. These clusters give deep insights into the strategic usage of social media by family firms.

The limitation of this study is that entrepreneurs from all Indian states were not considered in the sample because of cost implications. This research study has only created the segmentation of the family firms as reactive, proactive or innovative social media users and also has created the clusters as high rollers, ignorant inhabitants, trend-setters and combative crowd. Also, the reasons for their behaviour and root cause for the strategic usage have not been studied.

This study reflects on current practices of family firms with respect to usage of social media and groups them into large identifiable clusters. Equipped with the findings from this study, the RPI scale developed for the study and the clusters created, entrepreneurs can now move towards better use of social media for innovation.

Although past studies have advocated the use of social media to spur innovation in firms, this study segments the current market based on their practices. It allows readers to gauge the proportion of family firms using social media for innovation and paves the way for a change in behaviour amongst these firms.

Research questions that this paper attempts to answer are – do the features in general email communication have any significance to a teaching faculty member leaving the business school? Do the sentiments expressed in email communication have any significance to a teaching faculty member leaving the business school? Do the stages mentioned in the transtheoretical model have any relevance to the email behaviour of an individual when he or she goes through the decision process leading to the decision to quit? The purpose of this paper is to study email patterns and use predictive analytics to correlate with the real-world situation of leaving the business school.

The email repository (2010–2017) of 126 teaching faculty members who were associated with a business school as full-time faculty members is the data set that was used for the research. Of the 126 teaching faculty members, 42 had left the business school during this time frame. Correlation analysis, word count analysis and sentiment analysis were executed using “R” programming, and sentiment “R” package was used to understand the sentiment and its association in leaving the business school. From the email repository, a rich feature set of data was extracted for correlation analysis to discover the features which had strong correlation with the faculty member leaving the business school. The research also used data-logging tools to extract aggregated statistics for word frequency counts and sentiment features.

Those faculty members who decide to leave are involved more in external communication and less in internal communications. Also, those who decide to leave initiate fewer email conversations and opt to forward emails to colleagues. Correlation analysis shows that negative sentiment goes down, as faculty members leave the organisation and this is in contrary to the existing review of literature. The research also shows that the triggering point or the intention to leave is positively correlated to the downward swing of the emotional valence (positive sentiment). A number of email features have shown change in patterns which are correlated to a faculty member quitting the business school.

Faculty members of only one business school have been considered and this is primary due to cost, privacy and complexities involved in procuring and handling the data. Also, the reasons for exhibiting the sentiments and their root cause have not been studied. Also the designation, roles and responsibilities of faculty members have not been taken into consideration.

Business schools all over India always have a challenge to recruit good faculty members who can take up research activities, teach and also shoulder administrative responsibilities. Retaining faculty members and keeping attrition levels low will help business schools to maintain the standards of excellence that they aspire. This research is immensely useful for business school, which can use email analytics in predicting the intention of the faculty members leaving their business school.

Although past studies have studied attrition, this study uses predictive analytics and maps it to the intention to quit. This study helps business schools to predict the chance of faculty members leaving the business school which is of immense value, as appropriate measures can be taken to retain and restrict attrition.

In India, one of the prime focuses of a post-graduate management program is to prepare students and make them job-ready. Masters in Business Management (MBA) program helps students to imbibe theoretical and practical skills which are required by the industry, which can make them hit the ground running from the day they start their career. Many students (almost 40–50%) get pre-placement offers based on their performance in summer internship. The selection for summer interns by the corporate happens within a few months of the student joining the MBA program. Signaling theory in education indicates that the level of productivity of an individual is independent of education, but the educational qualification acts as a testimony for higher ability. However, this theory does not explain the reason for the mismatch between “education and work” or “education and the disparity in salary” between individuals who earn differently but have the same qualification. The paper aims to explore three attributes namely – “employability”– the chance of being employable; “pre-placement offers” – the chance of securing a job offer based on the performance in internship and “salary” – the chance of bagging a good job offer with a high salary.

The authors have used longitudinal data consisting of 1,202 students who graduated from reputable business schools (B-Schools) in India. In the study, the authors have used predictive analytics on six years data set that have been gathered. The authors have considered 24 attributes including educational background at the graduate level (BE, B Tech, B Com, BSc, BBA and others), score secured in class ten (high, medium and low), score secured in class twelve (high, medium and low), score secured in graduation (high, medium and low), competency in soft skills (high, medium and low), participation in co-curricular activities (high, medium and low) and social engagement status (high, medium and low).

The findings of the study contradict the signaling theory in education. The findings suggest that the educational qualification alone cannot be the predictor of the employability and the salary offered to the student. The authors note that the better performance at a lower level of qualification (class 12) is the strong predictor in comparison to the student performance at their graduation and post-graduation level. The authors further observed at the post-graduate management education level that soft skills and participation in co-curricular activities are the major deciding factors to predict employability and pre-placement job opportunity and marks secured in class 12 is one more factor that gets added to this list to predict salary. The paper can immensely help management graduates to focus on key aspects that can help to hone appropriate skills and also can help management institutions to select the right students for management programs.

The analysis and the predictive model may apply to Indian B-Schools wherein the quality of students are almost the same or better. Predictive analytics has been used to explain the employability of management graduates alone and not any other.

The authors' study might be useful for those students who often fail to understand “what” skills are the most important predictors of their performance in the pre-placement and final-placement interviews. Moreover, the study may serve as a useful guide to those organizations that often face dilemmas to understand “how” to select an ideal candidate for the particular job profile from a campus.

The authors believe that the current study is one of the few studies that have attempted to examine the employability of management graduates using predictive analytics. The study further contradicts that the signaling theory in education does not help better explain the employability of the students in extremely high-paced business environments.

There is always a need to discover how a paradox between a customer’s desire for a more personalized experience and their privacy and security concerns would shape their intention to continue using contactless payment methods. However, personalization–privacy paradox has not been well-covered over the area of contactless payment. Therefore, this study aims to empirically examine the impact of personalization–privacy paradox on the customers’ continued intention (CIN) to use contactless payment.

/methodology/approach – The empirical part of the current study was conducted in Saudi Arabia by collecting the primary data using online questionnaire from a convenience sample size of 297 actual users of contactless payment methods.

Based on structural equation modeling, personalization and privacy invasion were approved to significantly impact perceived value of information disclosure (PVD). Strong causal associations were confirmed between perceived severity, structural assurance and response cost with privacy invasion. Finally, both PVD and privacy invasion significantly predict CIN.

There are other important factors (i.e. technology interactivity, technology readiness, social influence, trust, prior experience, etc.) were not tested in the current study. Therefore, future studies would pay more attention regarding the impact of these factors. The current study data were also collected using a convenience sample of actual users of contactless payment methods. Therefore, there is a concern regarding the generalizability of the current study results to other kind of customers who have not used contactless payment.

This study has integrated both personalization–privacy paradox and protection motivation theory in one model. The current study holds value in providing a new and complete picture of the inhibitors and enablers of customers’ CIN to use contactless payment, including new types of inhibitors. Furthermore, personalization–privacy paradox has not been fully examined over the related area of Fintech and contactless payment in general. Therefore, this study was able to extend the theoretical horizon personalization–privacy paradox to new area (i.e. contactless payment) and new cultural context (Saudi Arabia).

Pricing is widely acknowledged as a market entry challenge for servitising companies. The purpose of this research is to ascertain the attributes that contribute to willingness to pay (WTP) for industrial services and suggest incorporating those attributes to a pricing model.

Three attributes (Quality of Service, Nearness of Service Provider and Brand Equity of Service Provider) were analyzed at three respective levels to ascertain their importance on WTP. Conventional conjoint analysis (CCA), using an orthogonal design, was the method used. The 346 respondents were decision-makers and top management professionals from various industries.

Brand Equity emerged as the most significant attribute contributing to WTP, having more than 45% importance – followed by the Quality and Nearness.

The scope of the study is limited to the industries and its Allies. However, the relative importance of the attributes may vary depending on the type of service.

The importance of attributes and their WTP preference helps future researchers create a pricing model involving these attributes. This helps service providers price their services rationally, thus succeeding in servitization.

Product life is extended because the manufacturers themselves are servicing it and also help recycle the product with their expertise. Servitization is also helpful for the Indian economy, as it is turning into a manufacturing economy.

This research investigates three attributes that contribute to WTP, in accordance with their level of contribution. It also provides a direction to establish an adequate pricing model for industrial services.

The purpose of this study was to understand the knowledge sharing in projects based on knowledge flow patterns. The impact of attrition, thereby leading to a loss of tacit knowledge, inability to capture and reuse knowledge and inability to understand the knowledge flow patterns, which leads to lack of structured workspace collaboration, are frequently faced challenges in organizations. The change in knowledge sourcing behaviors by the current generation workforce has a high reaching impact in driving collaboration among employees.

This paper attempts to study this impact and identify means to improve the effectiveness of collective knowledge sharing via social computing platforms. As part of this study, customized solutions are devised based on knowledge flow patterns prevalent in teams. Knowledge network analysis (KNA), a socio-metric analysis, is performed to understand knowledge flow patterns among employees in a team which helps understand the relationships between team members with respect to knowledge sharing. KNA helps in understanding ties and interactions between human and system resources.

Significant changes were observed in knowledge sourcing and sharing behaviors. Capture of the tacit knowledge of employees further resulted in reducing the impact of knowledge attrition. For instance, targeted communities of practice (CoPs) based on the presence of cliques within teams enabled teams to complete projects effectively and efficiently.

The results are used to identify push and pull networks to enable effective knowledge management (KM). Results of this study reveal that analyzing knowledge flow patterns in a team and deploying a customized social computing platform that is tailored to address the needs of specific knowledge flow patterns within that team, significantly enhances collaborative sharing as opposed to a standardized “one-size-fits-all” platform.

This paper is an original creation after research by the authors for a continuous assessment of KM within the organization.

Consumer adoption of online shopping continues to increase each year. At the same time, online retailers face intense competition and few are profitable. This suggests that businesses and researchers still have much to learn regarding key antecedents of online shopping adoption and success. Based on extensive past research that has focused on the importance of various online shopping antecedents, this work seeks to provide an integrative, comprehensive nomological network.

The authors employ a mixed-methods approach to develop a comprehensive model of consumers online shopping behavior. To that end, in addition to a literature review, qualitative data are collected to identify a broad array of possible antecedents. Then, using a longitudinal survey, the model of consumer shopping intentions and behaviors is validated among 9,992 consumers.

The authors identified antecedents to online shopping related to culture, demographics, economics, technology and personal psychology. Our quantitative analysis showed that the main drivers of online shopping were congruence, impulse buying behavior, value consciousness, risk, local shopping, shopping enjoyment, and browsing enjoyment.

The validated model provides a rich explanation of the phenomenon of online shopping that integrates and extends prior work by incorporating new antecedents.

Although the number of virtual communities has increased dramatically over the past few years, attracting and maintaining members remains the biggest challenge to establishing virtual social networks. This study seeks to integrate the roles of individual factors (issue involvement), social factors (social interaction), and system factors (system interactivity), and to explore how these factors contribute to member commitment in virtual communities.

A total of 402 undergraduate students, who are all current members of virtual communities, participated in this study. Data were analyzed using structural equation modeling (SEM).

The findings reveal that member commitment to communities was influenced more by their issue involvement compared to their perceived social interaction or perceived system interactivity.

This research contributes to online community literature by integrating critical antecedent factors in the field of community commitment behavior. The findings indicate that issue involvement is more important than social interaction and system interactivity for influencing member commitment to communities. Additionally, the findings suggest that online community administrators should consider community positioning and topic selecting programs when attempting to influence users to commit to communities.