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The purpose of this study was to investigate restaurant clientele tipping behavior and its inspiration on foodservice empathy within two- and three-star hotels in Kisumu County, Kenya. This was with the objective of analyzing the tipping effect on restaurant food service quality with an emphasis on Parasuraman, Zeithmal and Barry's empathy as a key dimension of quality in accordance to the SERVQUAL model.

For the purpose of this study, food service empathy was investigated in relation to the influence of tipping on restaurant food service quality. Further, a census of all the two and three star-rated hotels within Kisumu County was carried out. The study applied descriptive research design in the investigation on the tipping behavior and its inspiration on foodservice empathy. Moreover, simple random sampling was employed in the selection of clients since it yielded a sample that is representative of the population. Additionally, the study employed the use of questionnaires for collection of data, which were coded, analyzed and presented in frequencies, tables and graphs.

The study findings reveal that there is a significant relationship between rewards upon perception of service and food service empathy but failed to find a significant relation between incentives for improved future service as well as the social norms and foodservice empathy. However, in general, the study established a significant relationship between tipping and foodservice empathy in the sampled hotels in Kenya. Thus, in summary, at 95% confidence level, the study concluded that there is a significant relationship between tipping and foodservice empathy.

This study was restricted on two and three-star hotels within Kisumu County in Kenya with a sample size of 384 respondents, which would otherwise limit the degree to which the findings were applied. Consequently, the study sought to collect data from restaurant clients although the access and, therefore, direct interaction were denied by some of the hotels. Further, this study employed a survey approach in the collection of data from restaurant clients in two and three-star hotels. Accordingly, there was minimal local and regional research literature available on the study topic.

Tipping in the context of the broad global service industry, including hospitality's restaurant food service, is as old as Roman times. However, tipping is practiced differently across the world with some countries practicing while other countries not practicing the act of tipping. For that reason, tipping is not regulated in some of the countries including Kenya and therefore the lack of policy. Nonetheless, tipping is perceived to be the genesis of food service failures as a result of discriminatory restaurant food service in addition to increasing costs of eating out. This study therefore sought to investigate restaurant tipping behavior and its inspiration on foodservice empathy. The study results might be applied in policy formulation in order to curb the negative effect of tipping on food service empathy.

Minimal studies have been instituted and published in the area of tipping and service quality relationship with an emphasis on each of Parasuraman, Zeithmal and Berry's dimensions of quality. This research survey, therefore, sought to collect data from restaurant clients in two and three-star hotels within Kisumu County in Kenya and therefore investigated restaurant clientele tipping behavior and its inspiration on food service empathy.

In this study, the authors examine whether social capital embedded in individuals' social networks is connected to employees' long-term income development in Finland.

Analyses are based on 25–35-year-old employees from the Finnish Living Conditions Survey of 1994 combined with register data on earned incomes from 1995 to 2016. The authors used questions addressing the frequency of meeting parents or siblings, spending free time with co-workers and participation in associational, civic or other societal activities as measures of the extent of network capital. Ordered logistic model was used to examine whether the size and composition of social networks differ by gender and socio-economic status. Linear growth curve models were employed to estimate the effect of social capital on long-term income development.

Results indicate minor differences in network composition according to gender, but large differences between socio-economic groups. The authors found that income development was faster for those who participated in civic activities occasionally or who met their relatives or co-workers on a monthly basis, that is, for the “middle group”.

Results are generalizable only to Finnish or Nordic welfare state context. The authors’ measures of social capital come from cross-sectional survey. Thus, the authors are not able to address the stability or accumulation of social capital during life course. This restriction will probably cause the authors’ analysis to underestimate the true effect of social capital on earned incomes.

Moderate-level investments to network capital seem to be the most beneficial with regard to the long-term income development.

The study results give support to the idea that social capital can be transformed into economic capital. The results also imply that in economic terms it is important to balance diverse forms of social capital. At the policy level, a special emphasis should be directed to employees with low-socio-economic position. These people are especially vulnerable as their low level of income is combined with network composition that hinders their further income development.

The combined survey and register data give unique insight on how the social capital embedded in individuals' social networks is connected with long-term income development.

Prediction is a critical task in targeted online advertising, where predictions better than random guessing can translate to real economic return. This study aims to use machine learning (ML) methods to identify individuals who respond well to certain linguistic styles/persuasion techniques based on Aristotle’s means of persuasion, rhetorical devices, cognitive theories and Cialdini’s principles, given their psychometric profile.

A total of 1,022 individuals took part in the survey; participants were asked to fill out the ten item personality measure questionnaire to capture personality traits and the dysfunctional attitude scale (DAS) to measure dysfunctional beliefs and cognitive vulnerabilities. ML classification models using participant profiling information as input were developed to predict the extent to which an individual was influenced by statements that contained different linguistic styles/persuasion techniques. Several ML algorithms were used including support vector machine, LightGBM and Auto-Sklearn to predict the effect of each technique given each individual’s profile (personality, belief system and demographic data).

The findings highlight the importance of incorporating emotion-based variables as model input in predicting the influence of textual statements with embedded persuasion techniques. Across all investigated models, the influence effect could be predicted with an accuracy ranging 53%–70%, indicating the importance of testing multiple ML algorithms in the development of a persuasive communication (PC) system. The classification ability of models was highest when predicting the response to statements using rhetorical devices and flattery persuasion techniques. Contrastingly, techniques such as authority or social proof were less predictable. Adding DAS scale features improved model performance, suggesting they may be important in modelling persuasion.

In this study, the survey was limited to English-speaking countries and largely Western society values. More work is needed to ascertain the efficacy of models for other populations, cultures and languages. Most PC efforts are targeted at groups such as users, clients, shoppers and voters with this study in the communication context of education – further research is required to explore the capability of predictive ML models in other contexts. Finally, long self-reported psychological questionnaires may not be suitable for real-world deployment and could be subject to bias, thus a simpler method needs to be devised to gather user profile data such as using a subset of the most predictive features.

The findings of this study indicate that leveraging richer profiling data in conjunction with ML approaches may assist in the development of enhanced persuasive systems. There are many applications such as online apps, digital advertising, recommendation systems, chatbots and e-commerce platforms which can benefit from integrating persuasion communication systems that tailor messaging to the individual – potentially translating into higher economic returns.

This study integrates sets of features that have heretofore not been used together in developing ML-based predictive models of PC. DAS scale data, which relate to dysfunctional beliefs and cognitive vulnerabilities, were assessed for their importance in identifying effective persuasion techniques. Additionally, the work compares a range of persuasion techniques that thus far have only been studied separately. This study also demonstrates the application of various ML methods in predicting the influence of linguistic styles/persuasion techniques within textual statements and show that a robust methodology comparing a range of ML algorithms is important in the discovery of a performant model.