Relevance & Research Question

Measuring human perception of attributes such as text readability (Crossley et al., 2023) or perceived ideology of politicians (Hopkins and Noel, 2022) is oftentimes difficult because rating scales are hard to interpret. Pairwise comparisons between candidates—for instance: which of these two politician is more conservative?—pose a viable alternative. Given such pairwise comparisons, the task is to recover a global ranking of all candidates. This is non-trivial because of its probabilistic nature, i.e., a "weaker" candidate might win a comparison by chance. Furthermore, resources are often limited and not all pairs can be compared until a satisfactory estimate of individual strength is reached. Therefore, pairs of individuals must be selected according to a specified sampling strategy.

In recent years, a subfield of machine learning has developed around the quantification of fairness. While not without criticism, researchers propose fairness metrics and integrate fairness targets into machine learning algorithms. Lately, algorithmic fairness research has expanded from classification tasks to ranking scenarios as well. Yet, the fairness of ranking recovery from pairwise comparisons remains largely unexplored. This is particularly relevant since measured human perceptions are likely biased. For instance in hiring, pairwise comparisons between candidates for a position might not lead to the identification of the ideal candidate in the presence of such biases.

To the best of my knowledge, no previous research is concerned with the combined influence of sampling strategies and ranking recovery methods on the accuracy and fairness of recovered rankings. I thus propose the following research questions:

1. What is the effect of the sampling strategies and ranking recovery methods on overall accuracy?
2. Under which conditions do ranking recovery methods put an unprivileged group at a disadvantage?
3. Can sampling strategies or ranking recovery methods mitigate the effects of existing biases?

Methods & Data

In this thesis, I present a framework that manipulates the sampling of individuals for comparison in the presence of bias. I simulate individuals with latent "skill scores" on a certain task. I then separate the individuals into two groups and subtract a bias from the scores of the "unprivileged" group. I implement three distinct sampling strategies for selecting individuals from both groups for comparison: (1) random sampling (2) oversampling the unprivileged group and (3) sampling by previous success. Using the Bradley-Terry model (Bradley and Terry, 1952), I then simulate pairwise comparisons between the sampled individuals.

On the simulated pairwise comparison data, I apply various ranking recovery methods including basic heuristics (David, 1987) and a state-of-the art method that involves graph neural networks: GNNRank (He et al., 2022). Further, I recover rankings with Fairness-Aware PageRank (Tsioutsiouliklis et al., 2021)—an algorithm developed for a different task, that is, however, group-aware and aims at eliminating bias.

In order to evaluate the interaction between sampling strategies and ranking recovery methods, I propose a novel group-conditioned accuracy measure tailored towards ranking recovery. Using this measure, I am able to evaluate both the overall accuracy of the recovered ranking, but also its fairness as operationalized through group representation (exposure) and group-conditioned accuracy.

I provide a Python package under MIT license to facilitate replication of my findings as well as for further investigation of fairness in ranking recovery.

Results

Regarding the effect of sampling strategies, I find that both oversampling and rank-based sampling harm the accuracy of the recovered ranking. This is surprising as we would expect oversampling to improve the ranking accuracy of the unprivileged group that is oversampled. However, since this group's ranking accuracy also depends on correct comparisons against the individuals of the other group, the oversampled group's accuracy suffers as well. Oversampling thus is not a good remedy against biased comparisons.

In scenarios where there is no bias present against the unprivileged group, the optimal choice of ranking recovery method depends on the sampling that was used before pairwise comparison. Under random sampling, more advanced methods add little to no benefit in accuracy compared to heuristics based methods (i.e., David's Score). When oversampling or rank-based sampling is applied, however, GNNRank outperforms the other methods.

In the presence of bias against the unprivileged group, Fairness-Aware PageRank outperforms all other ranking recovery methods. Not only does it mitigate group representation bias from the recovered ranking, it also improves the ranking's accuracy when measured against the unbiased, latent "skill scores". This highlights the importance of group-aware ranking recovery over marginal benefits observed between the other ranking recovery methods.

Added Value

This thesis bridges the gap between previous research on fairness in machine learning and ranking recovery from pairwise comparisons. It is the first to introduce a framework for systematic investigation of fairness in ranking recovery and focusses on real-world sampling strategies and existing ranking recovery methods. Further, I propose a novel group-conditioned accuracy measure tailored towards ranking recovery. The results highlight the importance of fairness-aware ranking recovery methods and I supply recommendations on which ranking recovery method to use under which circumstances.

Relevance & Research Question:

Digitalisation is shaping a new consumption era characterised by high connectivity, mobility and a broad range of easily accessible information on products, prices and alternatives. The modern consumers are broadly connected via social media and more mobile than ever with their smart devices. This empowers consumers to make sophisticated buying decisions based on a comprehensive amount of easily accessible online information, while having a broad range of options to choose from. Moreover, they compare prices, ask for opinions online and are willing to choose alternative products or services if they fit better in their lifestyle and meet their needs. As a result, it becomes more difficult than ever to understand modern consumers along their complex and dynamic path to purchase. However, since the modern consumers are constantly online through their smartphones, they produce a notable amount of data about their mobile and online behaviour such as movement, social media activities, online purchases or google searches. This behavioural data is immensely valuable for companies because it allows them to get a deep understanding about the mobile consumption behaviour of their customers. Yet, there is no solution on how to use this data to follow the consumers on their mobile devices. Therefore, this thesis investigates the extent to which mobile data collected with sensing technologies is useful to describe mobile consumer behaviour. The goal was to propose a first approach on how mobile data can be analysed to understand mobile consumers along their customer journey. For this purpose, an explorative analysis was conducted based on the following research question: What analyses can be performed using data generated with smartphone sensing technology to understand mobile consumer behaviour along the customer journey?

Methods & Data:

As a first step, a literature review on current customer journey analytics theories, models and practices was conducted as foundation for the explorative data analysis. Because there could not be found any reasonable research that focuses on analysing customer journeys from mobile consumers, a mobile customer journey model was developed by adapting current models that are used among practitioners in customer journey analytics.

For the data analysis, the author collaborated with Murmuras, which developed a smartphone sensing technology for collecting sensing data via an application on participants’ mobile phones. The collection process adheres to GDPR compliance standards, with data exclusively stored on servers located in Germany. Importantly, no personal information is tracked; instead, only consumption-relevant data is recorded. The company runs an ongoing incentivised smartphone sensing panel with a constant participant basis of approximately 1.500 smartphone users in Germany. Because of this, the thesis could be provided with long-term data from 01.10.2021 to 31.08.2022. This mainly included app usage data as well as mobile browser data (e.g. google search terms, website visits, etc.) and specific in-app content such as advertisement in the Facebook and Instagram app and in-app shopping content from the Amazon shopping app.

The data was provided and analysed via the platform Metabase, which mainly uses SQL-programming for analysing data. As the author has previous experience working with the data and analytics platform during a student internship, this knowledge could be used to transfer the mobile customer journey model into analytics concepts. Based on that, an explorative data analysis was conducted to explore the full potential of sensing data in the context of customer journey analytics.

Results:

The results show that mobile sensing data can be used in three main research areas among customer journey analytics: examining the touchpoint performance of a brand across mobile apps, describing different target groups by their smartphone usage behaviour and deriving real customer journeys on users’ devices. For these areas interactive dashboards using different types of sensing data were developed.

The first dashboard focuses on analysing the touchpoint performance across various sensing datasets, including general app usage, in-app advertising, browser data, and Amazon shopping data. Key Performance Indicators (KPIs) were calculated to assess both general and app-related touchpoint performance. The integrated mobile customer journey provides an overview of all brand touchpoints over time, with detailed analyses of ads, browser interactions, and shopping behaviour. The second dashboard dives into target group analysis, aiming to understand mobile behaviour and preferences by providing insights into demographics, smartphone usage habits, contact channels, and mobile shopping behaviours on Amazon. The last part of the analysis employs the dashboards to conduct a deep analysis of an individual brand customer. This involved identifying relevant touchpoints, observing intercorrelations between touchpoints, analysing phone and mobile shopping habits, and mapping the customer journey stages. The insights gained from this analysis contribute to a comprehensive customer journey map and offer opportunities for the brand based on a deeper understanding of the consumers’ mobile life.

Added Value:

Although the vast amount of sensing data and the complexity of its analysis in the context of customer journey analytics remains challenging, it could be shown that sensing data presents a big opportunity for companies and researchers in this research area. It is not only possible to follow the relevant customers on their complex path to purchase, but also act on it by having the knowledge on how and where exactly to interact with their customers in the mobile world. As this has been a blind spot for companies and researchers before, they now have the ability to decode the whole customer journey of target groups by combining existing data with the insights derived from mobile sensing data. As sensing technology and sensing capabilities as well as smart devices are constantly improving, it is expected that an even more complete picture of mobile customer journeys can be analysed, which will add further value to customer journey analytics in future.

Relevance & Research Question:

Over the past decade online networking platforms have become integral parts of everyday life for most people, reshaping the way individuals communicate and network both privately and professionally. The growing popularity of these sites has sparked both enthusiasm and apprehension, resulting in a heated debate on the negative consequences of social network site (SNS) use on users’ well-being in both popular culture and academia. Almost simultaneously with the rise of private network sites such as Facebook, professional network sites (PNSs) including LinkedIn have gained popularity. Despite the great interest in usage patterns (active and passive use) and the negative effects of SNS use on users’ well-being, relatively little research has been performed on PNSs. Especially the association between PNS use and well-being has received very little academic attention so far. In view of the increasing popularity of PNSs for both private users and organizations this is surprising. Examining the impact on well-being is important as PNSs become more popular, leading to an increasing number of users who may be affected by the potentially harmful consequences such as decreased satisfaction with life, increased depressive symptoms or loneliness some authors have previously attributed to SNS use.

The aim of this study was to transfer previous findings on SNS use to the context of PNSs, exploring the multifaceted relationship between usage patterns and users’ well-being leading to the following research questions:

RQ1 What is the relationship between PNS usage type and users’ subjective well-being?

RQ2 What factors play a role in determining the influence of PNS usage type on the subjective well-being of the users?

RQ2.1 How does bridging social capital influence the relationship between active use and users’ subjective well-being?

RQ2.2 How do social comparison and envy influence the relationship between passive use and users’ subjective well-being?

Methods & Data:

A quantitative online survey was conducted which yielded an adjusted total sample of 526 LinkedIn users (173 male, 350 female, 2 diverse, 1 undisclosed) aged 19 to 65 (M = 28.69, SD = 8.66). A convenience sample was recruited using WhatsApp, LinkedIn and university mailing lists. Additionally, three survey sharing platforms (i.e. SurveyCircle, SurveySwap and PollPool) were used.

According to the active-passive model of SNS use (Verduyn et al., 2017), which was employed as the theoretical framework for this thesis and transferred to the context of PNSs for this purpose, the effects of active and passive use on users’ subjective well-being are explained by three mediating variables: social capital for active and social comparison as well as envy for passive use. Followingly, participants were asked to fill out measures regarding their usage pattern on LinkedIn, their subjective well-being, their tendency to engage in social comparison behavior, their experiences with envy as well as their levels of social capital.

Three mediation analyses were run using the PROCESS add on (Hayes, 2013) for IBM SPSS 28.0.1.0. To test the relationship between active LinkedIn use and subjective well-being, which was predicted to be mediated by bridging social capital, a simple mediation model was tested (model 1). Next, a serial mediation analysis was run to test the relationship between upward social comparison and envy as mediators in the relationship between passive LinkedIn use and subjective well-being (model 2). The same procedure was repeated, replacing upward comparison by downward social comparison (model 3).

Results:

Results of the mediation analyses revealed an indirect positive relation between active use of LinkedIn and well-being. Conversely, a negative indirect relation was found between passive use of LinkedIn and subjective well-being.

Bridging social capital fully mediated the relationship between active LinkedIn use and well-being (significant positive indirect effect ab = .0624, 95%-CI [.0303; .0999] and insignificant direct effect c’ = .0967, p = .1237, 95%-CI [-.0191; .1585]).

As predicted, social comparison and envy acted as serial mediators in the relation between passive LinkedIn use and subjective well-being (model 2: a1d21b2 = -.0347, 95%-CI [-.0583; -.0120]; model 3: a1d21b2 = -.0101, 95% CI [-.0217; -.0009]).

Though, results of the two mediation models examining passive LinkedIn use indicated possible omissions of other mediating variables as the direct effect between passive LinkedIn use and subjective well-being (model 2: c’ = .1692, p < .001, 95%-CI [.0904; .2481]; model 3: c’ = .1433, p < .001, 95%-CI [.0651; .2215]) remained significant after the mediator variables were added to the model.

Added Value:

The results of this thesis further expand upon previous research by examining users of PNSs. This study extends prior findings of other studies in two ways. First, it advances literature on online networking site use and well-being as it explores PNS use. Previous research mainly examined the relation between SNS use and well-being with special attention to Facebook. Moreover, prior studies have mainly focused on examining either passive or active use, while this study examined both usage patterns at once.

While results of this study are preliminary and should not be generalized, findings suggest that SNSs and PNSs share similarities, that lead to similar effect patterns when examining the relationship between usage patterns and well-being. Testing the active-passive model of SNS use (Verduyn et al., 2017) in the context of PNSs, revealed appropriate applicability. Results of this thesis also have practical relevance for both users and creators of platforms like LinkedIn. Active use behavior should be promoted and encouraged as it has been associated with positive affects on users’ well-being. When being educated on the different effects of usage patterns, users can proactively change their behaviors, positively affecting their well-being.