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This research aimed to fill several gaps in the tipping literature which has overlooked the server's perspective in identifying and understanding variables that influence a tip amount and therefore where they concentrate their efforts during the service encounter. Furthermore, the extant literature has theorized how or why certain variables influence the tip amount, but these studies fail to capture insight from server's which would supplement the theory and provide a more in-depth understanding of the mechanisms at play.

This study adopts a grounded theory approach using semi-structured one-on-one interviews with tipped restaurant employees who were identified and selected using snowball sampling. Content analysis is employed to code and categorize the data.

The content analysis revealed five categories where servers focus their time and effort to earn tips: service quality, connection, personal factors, expertise and food quality. The server's personality was identified as a variable the tipping literature has largely ignored as a determinant of the tip amount. Server's shift their style of service for groups of eight or more people, and for regular customers, who must dine in the restaurant at least once per week. Lastly, despite the many drawbacks associated with working for tips, servers would not want to replace it with any other method of compensation.

This is the first qualitative study focused on understanding the server's role in the service exchange relationship since McCarty et al. (1990) study. The results provide new insights on the often-studied variables from the tipping literature.

The purpose of this paper is to identify human capital factors that pertain both to setting up and successfully running a business. To achieve this objective, the authors apply and extend the theory of career choice offered by Lazear (2005) that explains individual selection into entrepreneurship.

The authors hypothesise that individuals with broader educational and professional backgrounds are more likely to start a business and are more likely to run a business in the long term. The authors tested the hypotheses using unique data from 800 current entrepreneurs, 800 employees who were previously entrepreneurs and 842 employees with no entrepreneurial experience, by means of a logit regression with robust standard errors and extensive robustness checks.

The authors empirically show that individuals with more diverse educational and professional backgrounds tend to have both greater chances of starting a company, as well as a higher probability of entrepreneurial success. Surprisingly, having managerial experience proved to exert a negative influence on the likelihood of starting a business while having an insignificant impact on the odds of entrepreneurial success.

The findings are informative for those planning or pursuing an entrepreneurial career, but they are also relevant for the purpose of entrepreneurship education.

The author's extend the body of research supporting Lazear's (2005) theory by showing that broad education and professional experience not only contribute to a higher propensity to start a company but they are also success factors in business per se.

The purpose of this paper is to propose a system dynamic simulated process model for maintenance work management incorporating the Fourth Industrial Revolution (4IR) technologies.

The extant literature in physical assets maintenance depicts that poor maintenance management is predominantly because of a lack of a clearly defined maintenance work management process model, resulting in poor management of maintenance work. This paper solves this complex phenomenon using a combination of conceptual process modeling and system dynamics simulation incorporating 4IR technologies. A process for maintenance work management and its control actions on scheduled maintenance tasks versus unscheduled maintenance tasks is modeled, replicating real-world scenarios with a digital lens (4IR technologies) for predictive maintenance strategy.

A process for maintenance work management is thus modeled and simulated as a dynamic system. Post-model validation, this study reveals that the real-world maintenance work management process can be replicated using system dynamics modeling. The impact analysis of 4IR technologies on maintenance work management systems reveals that the implementation of 4IR technologies intensifies asset performance with an overall gain of 27.46%, yielding the best maintenance index. This study further reveals that the benefits of 4IR technologies positively impact equipment defect predictability before failure, thereby yielding a predictive maintenance strategy.

The study focused on maintenance work management system without the consideration of other subsystems such as cost of maintenance, production dynamics, and supply chain management.

The maintenance real-world quantitative data is retrieved from two maintenance departments from company A, for a period of 24 months, representing years 2017 and 2018. The maintenance quantitative data retrieved represent six various types of equipment used at underground Mines. The maintenance management qualitative data (Organizational documents) in maintenance management are retrieved from company A and company B. Company A is a global mining industry, and company B is a global manufacturing industry. The reliability of the data used in the model validation have practical implications on how maintenance work management system behaves with the benefit of 4IR technologies' implementation.

This research study yields an overall benefit in asset management, thereby intensifying asset performance. The expected learnings are intended to benefit future research in the physical asset management field of study and most important to the industry practitioners in physical asset management.

This paper provides for a model in which maintenance work and its dynamics is systematically managed. Uncontrollable corrective maintenance work increases the complexity of the overall maintenance work management. The use of a system dynamic model and simulation incorporating 4IR technologies adds value on the maintenance work management effectiveness.