Job satisfaction and employee turnover determinants in high contact services: Insights from Employees'Online reviews

We explore a special case of electronic word of mouth that of employees' online reviews to study the determinants of job satisfaction and employee turnover. We perform our analysis using a novel dataset of 297,933 employee online reviews from 11,975 US tourism and hospitality firms, taking advantage of both the review score and text. Leadership and cultural values are found to be better predictors of high employee satisfaction, while career progression is critical for employee turnover. One unit increase in the rating for career progression reduces the likelihood of an employee to leave a company by 14.87%. Additionally, we quantify the effect of job satisfaction on firm profitability, where one unit increase leads to an increase between 1.2 and 1.4 in ROA. We do not find evidence supporting the reverse relationship, that growth on firm profitability increases job satisfaction. The feedback to management in employee reviews provides specific managerial implications

Front- and Back-End Employee Satisfaction during Service Transition

Purpose Scholars studying servitization argue that manufacturers moving into services need to develop new job roles or modify existing ones, which must be enacted by employees with the right mentality, skill sets, attitudes and capabilities. However, there is a paucity of empirical research on how such changes affect employee-level outcomes. Design/methodology/approach The authors theorize that job enrichment and role stress act as countervailing forces during the manufacturer's service transition, with implications for employee satisfaction. The authors test the hypotheses using a sample of 21,869 employees from 201 American manufacturers that declared revenues from services over a 10-year period. Findings The authors find an inverted U-shaped relationship between the firm's level of service infusion and individual employee satisfaction, which is flatter for front-end staff. This relationship differs in shape and/or magnitude between firms, highlighting the role of unobserved firm-level idiosyncratic factors. Practical implications Servitized manufacturers, especially those in the later stage of their transition (i.e. when services start to account for more than 50% of annual revenues), should try to ameliorate their employees' role-induced stress to counter a drop in satisfaction. Originality/value This is one of the first studies to examine systematically the relationship between servitization and individual employee satisfaction. It shows that back-end employees in manufacturing firms are considerably affected by an increasing emphasis on services, while past literature has almost exclusively been concerned with front-end staff

Exploring demographic information in social media for product recommendation

In many e-commerce Web sites, product recommendation is essential to improve user experience and boost sales. Most existing product recommender systems rely on historical transaction records or Web-site-browsing history of consumers in order to accurately predict online users’ preferences for product recommendation. As such, they are constrained by limited information available on specific e-commerce Web sites. With the prolific use of social media platforms, it now becomes possible to extract product demographics from online product reviews and social networks built from microblogs. Moreover, users’ public profiles available on social media often reveal their demographic attributes such as age, gender, and education. In this paper, we propose to leverage the demographic information of both products and users extracted from social media for product recommendation. In specific, we frame recommendation as a learning to rank problem which takes as input the features derived from both product and user demographics. An ensemble method based on the gradient-boosting regression trees is extended to make it suitable for our recommendation task. We have conducted extensive experiments to obtain both quantitative and qualitative evaluation results. Moreover, we have also conducted a user study to gauge the performance of our proposed recommender system in a real-world deployment. All the results show that our system is more effective in generating recommendation results better matching users’ preferences than the competitive baselines

Flying to Quality: Cultural Influences on Online Reviews

Customers increasingly consult opinions expressed online before making their final decisions. However, inherent factors such as culture may moderate the criteria and the weights individuals use to form their expectations and evaluations. Therefore, not all opinions expressed online match customers’ personal preferences, neither can firms use this information to deduce general conclusions. Our study explores this issue in the context of airline services using Hofstede’s framework as a theoretical anchor. We gauge the effect of each dimension as well as that of cultural distance between the passenger and the airline on the overall satisfaction with the flight as well as specific service factors. Using topic modeling, we also capture the effect of culture on review text and identify factors that are not captured by conventional rating scales. Our results provide significant insights for airline managers about service factors that affect more passengers from specific cultures leading to higher satisfaction/dissatisfaction

The Opinion Evaluation Network: Ranking Imprecise Social Interactions

Current models of ranking in Information Retrieval (IR) are somewhat blind to the consideration of the social context that surrounds an information resource as a parameter that affects the precision of the ranking in the query results. On the other hand that social context is depicted upon the relational ties of the affiliated social entities (authors) thus is something that cannot be measured and quantified accurately by back-link and citation based models. In this thesis we adopt an imprecise modeling approach of the depicted relational ties using the paradigm of fuzzy sets as to express partial degrees of membership depicted on the concept of 'opinion' as an input to a model that considers both the informational (hyperlink) and social (relational) context of the information resources as to provide better ranking of the retrieved results with respect to both contexts. A formalization of the algorithm and a validation of the model using simulation is given as a proof of concept along with discussion of the obtained results

Big Data for Enhancing Learning Analytics:A Case for Large-Scale Comparative Assessments

Recent attention on the potentiality of cost-effective infrastructures for capturing and processing large amounts of data, known as Big Data has received much attention from researchers and practitioners on the field of analytics. In this paper we discuss on the possible benefits that Big Data can bring on TEL by using the case of large scale comparative assessments as an example. Large scale comparative assessments can pose as an intrinsic motivational tool for enhancing the performance of both learners and teachers as well as becoming a support tool for policy makers. We argue why data from learning processes can be characterized as Big Data from the viewpoint of data source heterogeneity (variety) and discuss some architectural issues that can be taken into account on implementing such an infrastructure on the case of comparative assessments