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Solar photovoltaic (PV) technology offers a promising means to alleviate environmental and electricity costs challenges for cryptocurrency miners. To analyze this promise, this study investigated the feasibility of using electricity from individually optimized PV systems to power: 1) an individual Bitcoin miner, 2) a DIY intermodal shipping container holding 50 miners, and 3) a commercial mining farm container holding 408 miners. In a controlled lab environment, miners were monitored for electricity use. Then using these values, numerical simulations of both the PV system yield and sensitivity ranges based on the Bitcoin price, Bitcoin halving events, and miner hardware were investigated for informed financial planning. In addition, sensitivity for geographic locations in North America, utility electric rates and PV capital costs were analyzed. The profitability and return on investment (ROI) varied by location primarily because of the geographic distribution of solar flux and utility rates. The ROI for using PV with Bitcoin mining was found to be negative for Toronto and Montreal because of low-cost electricity, while it was 8% for Calgary. In the U.S. cities evaluated, the ROIs were substantial and ranged from 34% in New York, to 64% in Boulder, and up to 104% in Los Angeles. Although the study is based in North America regarding energy rates, climate, and energy laws, the analysis methodology is generalizable globally and grants the average cryptocurrency business the knowledge to make an informed decision on whether to pursue this venture from a financial and environmental perspective. This study contributes to the body of knowledge in cryptocurrency mining by providing an economic means of environmental preservation by powering cryptocurrency miners with renewable solar energy.

Several studies have shown that there is a widespread lack of digital media literacy, particularly the ability to identify and discriminate between reliable and relevant online information from a variety of search results (Meßmer et al., 2021; Breakstone et al., 2021; Fraillon et al., 2020; Wineburg et al., 2016; Maurer et al., 2017; Metzger & Flanagin, 2013). As the ability to find and evaluate reliable and relevant online information is essential for self-directed learning, this skill also has important implications for university students. In recent years, AI-based chatbots have been shown to be able to teach multiple skills (Rad-Khosrawi et al., 2022; Wolly et al., 2021; Hobert & Meyer von Wolff, 2019; Winkler & Söllner, 2018). However, despite the importance of digital media literacy in today's digital society, there is still a lack of use cases for educational chatbots to train these specific skills. The present study aims to fill this research gap. To this end, an educational chatbot was built based on the Cognitive Apprenticeship model (Collins, Brown & Newman, 1987; Collins, 2005). A between-subjects design (chatbot vs. checklist vs. baseline) is used to evaluate the effectiveness of this chatbot intervention. The evaluation is facilitated by the use of the simulated search engine environment, EVON, which consists of eight tasks that ask subjects to identify the most reliable and relevant website among several non-target websites (Hahnel et al., 2020). As a control for prior knowledge and related digital media literacy skills, the SWE-IV-16 (Behm, 2018) is measured. The effectiveness of an educational chatbot for the scalable promotion of digital media literacy will be discussed, also in the light of recent developments such as Microsoft's integration of GPT-4 into its Bing search engine.

This exploratory study aims to investigate the effectiveness of emotion-focused and problem-focused coping strategies in reducing technostress among business professionals at a Swiss corporation. Specifically, the study will examine how these coping strategies can be used to manage information overload, techno-invasion, and techno-complexity in the workplace. The study will employ field experiments and qualitative interviews to gather data from participants. Through this research, we aim to identify best practices for reducing technostress and improving the productivty of employees.

We want to understand to what extend product market fit concepts can be applied to deep tech startups considering the challenges and specificities of those compared to "shallow" or "traditional" startups. At the end, it should provide (1) a comprehensive definition of product market fit and (2) first insights on how product market fit concepts could be applied to deep tech startups.

In this research project, I study how people perceive and judge Algorithmic Leadership, in terms of leadership behavior (Relational vs. Task oriented). The underlying theories will be Implicit Leadership Theories (ILTs), Behavioral approach to leadership, and Leader-Member Exchange. Two methods of Implicit Association Tests (IAT) and scenario experiments with randomization are used. The main motivation for this research is to understand how people implicitly view algorithms in leadership roles and how it is compared with a human leader. This question can be answered by conducting a study using Implicit Association Tests and Explicit measures to see 1) to what degree people associate algorithmic leaders with relational vs task-oriented leadership behavior.

In this study, we assess the question: How does AI change the value of cheap talk? We hypothesize that AI content-generators make it easier for senders to fake knowledge or expertise about a particular topic, but also increase the threshold of knowledge expected by receivers to believe cheap talk messages. Together, this reduces the value of cheap talk altogether. We seek to test this in experiments using chatGPT. We ask a set of “sender” subjects to compose persuasive texts, both without help and using ChatGPT as an assistant. We then ask a separate set of subjects (“receivers”) to assess the persuasive text written by the senders and indicate the degree of belief. The “receivers” are told whether the senders’ messages were composed with the help of ChatGPT or not.

This preregistration is for a survey data collection to validate a model developed based on qualitative data.

We want to collect insights into effective monitoring and response to challenges brought by cybercrime in law enforcement. Data will be collected via mixed methods, constituting of interviews and a survey.

This project seeks to understand U.S. adult consumers' knowledge of and attitudes toward the egg industry's practices regarding male chicks and technology-based alternatives, including in-ovo egg sexing (to prevent the unwanted hatching of male chicks).

I want to find out what the role of a tester is in scaled agile frameworks. Also, how the role differs from those of a traditional tester and how it is impacting the traditional tester.

A qualitative explorative study is made as part of my PhD journey. After a first qualitative exploration stage with initial partners, I am now launching a new stage of data collection and I would like to improve the quality of my research process, hence this registration intention.

In this master thesis, I will examine the influence of the vividness of a stimulus, in this case a kickstarter product video, on the emotions during the decision making process and their subsequent influence on the investment decision.

With the rise of AI technologies, there are also rising customer concerns about data privacy, especially provoked by various security scandals and recent data leakages. Customer concerns are further intensified due to lacking reliable conveyance of the data security features a product does and does not entail. Such customer concerns mainly exist due to a relatively slow adoption of new technology (Diffusion of Innovation Theory; Rogers 2003). This is also related to the technology acceptance model (TAM; Davis, 1989; Marangunić & Granić, 2014) which suggests that perceived usefulness and perceived ease of use influence the final technology acceptance and, thus, actual usage of technology. Concerns of consumers in the face of new AI-based technology can also be explained with the concept of AI anxiety (Wang & Wang, 2019) that extends the well-recognised concept of computer anxiety (Zhang et al., 2021). Security labels such as the Digital Trust Label (Swiss Digital Initiative, 2022) that can be compared to food nutrition labels (e.g., NutriScore), could be used to achieve transparency regarding data security features of AI technologies in an easy and fast to process format. In turn, this could help achieve an appropriately low or high level of trust in corresponding technologies and resolve customers' concerns regarding the use and adoption of AI products. Such labels do not yet widely exist and it is yet unclear how they would need to be designed to support transparency and increase trust in consumers. Thus, assessing digital trust labels is an interesting area of research essential to support trust in AI products in the future. Prior studies suggest to convey the security of AI products through labels and certificates by third parties (Hammer et al., 2015; Wißner et al., 2014), as labels issued by third parties are perceived as more trustworthy and reliable (Josang et al., 2007). To date, security labels have only been studied to a small extent and, to our knowledge, no studies on security labels for AI products have been conducted yet. This is mostly due to the relatively new emergence of such labels and their current sparse application. For instance, Emami-Naeini et al. (2019) have conducted a qualitative study on a prototype Internet of Things (IoT) label (text and ratings) which found that information regarding security features is almost impossible to find for consumers and demonstrated that security and privacy the are most valued features after a product's price. Their label was shown to be accessible and useful to almost all study participants, encouraging them to consider the privacy and security in their future IoT buying decisions (Emami-Naeini et al., 2019). Furthermore, Shen and Vervier (2019) have designed two IoT labels based on previous research, one comparable to a food nutrition label including ticked boxes and colour, and one which is a table-like label with leveraged icons and text, that they both suggest for wider implementation given a decent mixture of complexity (amount of conveyed information) and simplicity. In this study, two different types of security labels (graphical vs. text-based labels) are compared, to understand the effect on consumers’ trust and AI anxiety as well as the value attributed to the AI product. The study aims to provide first suggestions regarding the design and usage of digital trust labels. Should such labels prove to be effective, concrete implications for businesses and governments could be derived that ultimately increase transparency and trust in AI products, creating a win-win situation for all parties involved.

This study is a follow-up to the first study manipulating the degree of cognitive load plays a part in the decision to utilize algorithmic decision-makers.

We want to explore viable use cases of GPT-based tools in HRM as well as the regulatory, ethical, and governance considerations when doing so and the possible resulting implications and challenges for HR professionals.

Master Thesis BFH: An in-depth analysis of the event- and marketing-industry in Switzerland

MSc Master of Science in Digital Business Administration Bern University of Applied Sciences, Switzerland DR4 - MATH

In this research project, I study how people perceive and judge Algorithmic Leadership, in terms of Trust (Ability, Integrity, Benevolence). The underlying theories will be Implicit Leadership Theories (ILTs), Organizational Trust, and Leader-Member Exchange. Two methods of Implicit Association Tests (IAT) and scenario experiments with randomization are used. The main motivation for this research is to understand how people implicitly view algorithms in leadership roles and how it is compared with a human leader. This question can be answered by conducting multiple studies using Implicit Association Tests and Explicit measures to see 1) to what degree people associate algorithmic leaders with three elements of trust (ability, integrity, benevolence)

Despite the urgency and interest in cryptocurrencies in the recent years, its research within the academia is mostly limited to data-based non-experimental studies on crypto’s price and volatility, related to the Effective Market hypothesis – EMH – (e.g., see Yang, 2019; Juwita et al., 2022). While some researchers find its fluctuations to be compliant with the EMH and driven by supply and demand, and few macro-finance factors (e.g., Gonçalves et al., 2022), others suggest that Bitcoin price does not correlate with stocks or commodities (Hidajat, 2018), and that it is significantly predicted by sentiment and speculative investment (Castro, 2019). Moreover, Steinmetz (2021) finds that cryptocurrencies have more technological features than those of a currency or a security. A typical investor’s profile also vastly differs between crypto and stock market users (Johnson et al., 2023). At the same time, crypto is largely used as a payment method (Steinmetz, 2021), and there is evidence that loss realization with crypto might be less hurtful for an individual than a loss from a regular stock (Castro, 2019). On the other hand, the author also suggests that the opposite may be true: investors exhibit more self-control with bitcoin, because they are aware of its volatility. Therefore, a cryptocurrency with negative profit might cause more negative feelings, such as regret, than a regular stock would (Castro, 2019). Although the first attempts to investigate the behavioural and psychological factors that are usually applied in relation to money and stock have been made towards the study of cryptocurrencies (e.g., Andrade & Newall, 2023; Ballis & Verousis, 2022), research in this area is rather incipient. The current research aims to understand how cryptocurrencies are perceived, taking as a comparison point, money. It aims to provide exploratory evidence of effects of mental accounting on cryptocurrencies. The topic’s relevance is underlined by many investigators, who call for crypto-related studies, and/or its intersection with mental accounting and its correlates (e.g., see Muehlbacher & Kirchler, 2019; Fettahoğlu & Sayan, 2021; Gonçalves et al., 2022; Mai et al., 2020). It aims to do so by focusing on quantitative gathering of preliminary empirical data on the perceived similarities and differences between money and cryptocurrencies, such as how easy/hard to use, how fungible, how hedonically-pleasing they are perceived to be, among others. We hope this study will allow for a better understanding of the perception of cryptocurrency, as well as the base for future studies on mental accounting of cryptocurrency we plan on conducting.

An Analysis on what to consider when implementing Process Mining in Swiss Financial Institutions on an operational level

This study project aims to derive persuasive strategies for social entrepreneurs crowdfunding campaigns. Data collection will be conducted with help of semi structured interviews and qualitative content analysis. (Lukas Howald: DR4 - Master Thesis)

Digital medicine manufacturing of the future will almost certainly involve greater use of advanced analytics tools for more data-driven decision-making. In this article, the role of people in the deployment of data analytics tools within the pharmaceutical manufacturing setting is explored. Some perspectives are presented for how people can make sense of working cross-functionally on the analytics journey to deliver new knowledge and insights that enable data-driven decisions to be made.Data analytics is defined and is proposed as a critical common ground between the “on-the-ground” data practitioners and the decision-makers who act on new knowledge. Reference is made to published cases on the role of people in deploying advanced analytics tools for gaining new knowledge and insight across pharmaceutical manufacturing.

In this study, we are interested in how consumers use, interact and perceive human-like (vs. robot-like) chatbots when they have to purchase a (non-)embarrassing product.

Olivier Marti: DR4 - Master Thesis

We want to gain insights into the current state of the implemetation of ML and AI in the real estate industry, key drivers, challenges and opportunities related to new technologies and sustainability in the sector.

How can organisations identify employee's with an intrapreneur profile to drive business innovation?

Venture capital (VC) plays a pivotal role in the survival and success of high-growth startup firms. Yet there is significant discretion in how such investors determine which entrepreneurs warrant their capital and mentorship. This has lead to insular investment decisions that venture capitalists themselves are sounding the alarm on. One of the most alarming areas of disparity is the gap between female and male entrepreneurs that receive venture capital backing for their startup ventures, with female founders accounting for only 10% of such startups. Part of the problem is that venture capital investors have to grapple with significant technological and business uncertainty as they make their investment decisions, leading them to default to social cues, familiarity, and stereotypes to guide their intuition. Prior research has found that this leads investors to more positively assess the quality of startups pitched by males. Given this well-documented finding, this study aims to take the next step forward, in understanding whether the use of metrics and quantification in a startup pitch can close the gap between how female and male entrepreneurs are perceived. The objective is to take a first step in exploring the tools and mechanisms that might allow disadvantaged entrepreneurs to overcome entrenched biases, and get a fair sounding board for their ideas.

The research focuses on the gap between government digitization plans and the awareness of micro and small organizations, despite the readiness and understanding of the first ones.

DR4 - Master Thesis Damian Tanner

DR4 - Master Thesis

Explore the experience of using Planning poker as a method for cost estimation through interviews with experts and define the challenges and opportunities of applying this method.

Master Thesis DR4 MATH: Qualitative Study for Bern University of Applied Sciences

Artificial intelligence (AI) is revolutionizing various industries and the human resource (HR) field is no exception. The labor market 2.0 has arrived in Switzerland and large companies such as SBB, Migros, Axpo and Post are already using AI in their processes. AI-driven recruitment has the potential to streamline the hiring process and improve the candidate experience. However, despite the increasing use of AI in HR, there has been limited research on how people perceive and emotionally feel about these technologies. While experts in AI acknowledge the importance of building trust between humans and AI systems and increasing the transparency of AI methods, there remains a gap in the understanding of how humans emotionally feel about such AI software in the business and public life. This lack of understanding presents a significant challenge for organizations looking to implement AI-driven recruitment processes and for researchers looking to study the impact of AI in the HR field.

We want to find out what specific factors of Big Data implementation in the freight forwarding industry contribute to the lack of emphasis on ecological sustainability and what actions can be taken to make it a strategic priority in the industry.

This study will be conducted for the Master Thesis at the study program Master of Science (MSc) in Digital Business Administration at Bern University of Applied Sciences. The Master Thesis covers the topic of business ecosystems and their impact on the business model of retail banking and its core services. The results will be published on OSF.

How does the use of VR/AR technology in existing architecture enterprises in Switzerland affect customer experience, considering potential innovation resistance factors that may impede its adoption and diffusion?

I analyze the data with different type, especially technical, health, etc...

Through interviews, we want to find the enablers that contribute to the quality of software products in large-scale agile software development.

A study exploring the relationships between voice assistant use and stereotypical gender views.

Exploring the value logics of a platform company from the complementors and customers point of view to provide insights in the benefits of platform participation, and thus how value logics may be aligned, affecting value creation, delivery and capture among a platform’s ecosystem participants

A protocol for a scoping review to identify cross-sector collaboration and adaptation of processes and products to promote innovation which may apply to aged care

This project investigates which employee positions are going to be obsolete through the use of smart grid.

The study aims to develop the influence of automated front desk services on customer satisfaction in hotels. DR1 - Marketing aspects of service automatization

This project investigates which employee positions are going to be obsolete through the use of smart grid.

Our research aims to quantitatively assess the intention of German crop farmers to adopt spot spraying for smart and sustainable weed control. In arable farming of developed countries, smart weeding technology bears high potential for the mitigation of negative environmental impacts caused by intense herbicide application. Reaping this potential is highly desirable both from an ecological and societal point of view. Moreover, the substantial savings in herbicides can lead to input cost savings. But due to the infancy of technological development, early examples of the technology are not necessarily economically viable. Additionally, farmers’ intrinsic environmental or innovation-related attitudes next to how they perceive their colleagues’ and society’s opinions towards their farming practices may hold conducive or obstructive effects on their consideration of smart and sustainable weeding technologies. Thus, we observe a general reluctance in arable farmers to explore or adopt early prototypes of such technologies. To assess what determines farmers' intention to adopt spot spraying we collect a unique data set on arable farmers from North Rhine-Westphalia (and potentially one other federal state of Germany), via an online survey distributed between February 2022 and March 2022. The core of the collected data consists of a set psychographic constructs developed within the framework of the Theory of Planned Behavior (TBP), established by Icek Ajzen (1991). We extended the TPB via three additional constructs in order to more accurately depict the smart farming innovation context. Furthermore, common sociodemographic and farm structural variables are collected for descriptive and control purposes. Structural Equation Modelling is applied to analyze the relations between individual constructs, especially as they pertain to the main construct of interest, i.e., the intention to use spot spraying. The questionnaire will be administered online via Qualtrics and econometrically analyzed with a statistical software package, e.g. R or SmartPLS.

Video analytics is the practice of combining digital video data with machine learning models to infer various characteristics from that video. This capability has been used for years to detect objects, movement and the number of customers in physical retail stores but more complex machine learning models combined with more powerful computing power has unlocked new levels of possibility. Researchers claim it is now possible to infer a whole host of characteristics about an individual using video analytics–such as specific age, ethnicity, health status and emotional state. Moreover, an individual’s visual identity can be augmented with information from other data providers to build out a detailed profile–all with the individual unknowingly contributing their physical presence in front of a retail store camera. Some retailers have begun to experiment with this new technology as a way to better know their customers. However, those same early adopters are caught in an evolving legal landscape around privacy and data ownership. This research looks into the current legal landscape and legislation currently in progress around the use of video analytics, specifically in the retail in-store setting. Because the ethical and legal norms around individualized video analytics are still heavily in flux, retailers are urged to adopt a ‘wait-and-see’ approach or potentially incur costly legal expenses and risk damage to their brand.