Diginomics Working Paper
Industrial and academic communities in the field of operations and supply chain management (OSCM) have been paying increasing attention to social media analytics (SMA). However, the disparity of social media has inspired new ways of thinking about how data are produced, organized and analyzed. This chapter addresses how OSCM is affected by this disparity and provides an overview of SMA use, applications and challenges in the domain. A directed content analysis of current, application-oriented research is carried out to review SMA in OSCM from a signaling theory perspective. In particular, we shed light on data sources, opportunities, challenges, paradoxes and current managerial issues and seek to inform research practices and policy in order to advance operations and supply chain management research. The chapter contributes to the understanding of SMA in OSCM by identifying a set of paradoxes and challenges that have not previously been identified in OSCM research. By relating SMA to social media data sources and OSCM activities, it shed light on preferred sources and application scenarios and discusses the imponderables of social media signal processing in OSCM.
For many years, shipping companies, terminal operators, port industry actors and logistics service providers have been successfully engaged in innovating their assets, processes and business activities. The best-known key innovation is the introduction of the standardized container. This innovation has been the basis for other innovations such as supply chain management, just-in-time delivery, port integration, ship size development and linked hinterland connections. The business world is abuzz with talk about automation, self-control, cloud logistics, electrification, green processes and dematerialization. However, the most important key innovation now and for the foreseeable future is related to digitalization and digital transformation of business activities. Digital transformation is widely understood as the process of implementing digital technologies and supporting capabilities to create digital business models. In this context, a rethinking of previous approaches to communication, coordination and cooperation between stakeholders along the supply chain is appropriate and necessary. The purpose of the present chapter is to reflect the opportunities and challenges related to this digital transformation, focusing on ocean shipping, port management and hinterland connectivity. This chapter aims to identify and characterize expectations, opportunities and peculiarities of this transformation, and to sketch a picture of the future. Particular attention is given to the influence of digital gaps, cultural differences and the power of data.
The most important resource to improve technologies in the field of artificial intelligence is data. Two types of policies are crucial in this respect: privacy and data-sharing regulations, and the use of surveillance technologies for policing. Both types of policies vary substantially across countries and political regimes. This chapter examines how authoritarian and democratic political institutions can influence the quality of research in artificial intelligence, and the availability of large-scale datasets to improve and train deep learning algorithms. We focus mainly on the case of China, and find that – ceteris paribus – authoritarian political institutions continue to have a negative effect on innovation. They can, however, have a positive effect on research in deep learning, via the availability of large-scale datasets that have been obtained through government surveillance. We propose a research agenda to study which of the two effects might dominate in a race for leadership in artificial intelligence between countries with different political institutions, such as the United States and China.
Digital assets are continually evolving into a “mainstream” asset class. Institutional interest is growing every day, with the market capitalization of digital assets rising in value and importance not only for retail investors, but also for global banks, hedge funds and regulators. This chapter provides an overview for interested readers of how digital assets compare to traditional asset classes, how a blockchain works, and an assessment of whether academic research has uncovered first trading strategies and other relevant findings in this emergent asset class of virtual tokens.
The authors present a compact overview of machine learning applications in financial accounting and audit research as well as management accounting research. Here, the application of machine learning has the potential to provide novel insights into empirical data and to improve predictive performance. The authors highlight the potential use of deep learning to process unstructured and structured data more efficiently and a greater focus on model interpretability as viable opportunities for future research.
Based on 26,883 investment decisions, this chapter examines the influence of social media marketing on crowd participation in equity crowdfunding. The authors distinguish between different types of informative and persuasive posts on Facebook and Twitter. Informative posts provide investors with information about the crowdfunding campaign; persuasive posts do not, but rather aim to directly influence an investor’s decision-making process. The authors find that both types of posts have a positive impact on the number of investments. However, persuasive posts also increase the amount of an investment if they contain a statement about the previous investment success of the campaign and signal the crowd that they are not investing alone.
Manipulative product and interface designs known as digital “dark nudges” have become a common phenomenon in the digital economy. This chapter investigates the behavioral science background and the main problem areas of such unethical online business practices. We also show the limits of the existing statutory framework for combating digital dark nudges. The chapter concludes by discussing potential private and statutory remedies to address dark nudges in the digital economy.
This chapter examines the lack of entrepreneurial resources for venture development in rural regions and presents a study of how virtual business incubators (VBIs) can fill this gap and support rural entrepreneurial activities. The authors employ a single case study approach in which the empirical data builds on six interviews conducted with rural entrepreneurs and managers of VBIs. The data collection and data analysis follow the grounded theory approach set forth by Charmaz (2014). The main contribution of this research is the application of the VBI concept in the rural entrepreneurship context, with the aim of overcoming the challenges inherent to this context while conserving its benefits.
Over the course of digitization, many innovative marketing technologies have emerged that – theoretically speaking – promise firms gains in efficiency and/or effectiveness. However, a central task for marketing is not to allow the use of these technologies to become an end in itself, but to preserve the guiding principle of marketing, namely customer orientation. This means that the new technologies only offer added value for firms if they also offer (perceived) added value for consumers. Using three specific application areas as examples (chatbots, voice assistants and data privacy management), we show how firms can combine innovative marketing technologies and consumer interests in a purposeful manner.
Digital dialogue systems, known as “chatbots,” are an application sub-area in automation which is increasingly used in both work and business contexts. The growing use, functionality and benefits of chatbots are therefore also of interest for university teaching in economics. Students at the University of Bremenʼs Department of Economics are developing their own transdisciplinary chatbot project. Working together with partners from different companies within the framework of the HumanRoboLab (HRL), they explore human–machine interaction from an applicationoriented perspective. The main objective of the HRL is to test a didactic concept for transdisciplinary human–machine interaction projects in order to subsequently integrate this concept into business studies and business psychology degree programs. The purpose of such a module is to provide students without prior experience in programming with the knowledge, tools and competences to research, apply and question AI technologies in a self-effective manner; in other words, the HRL aims to help students develop expertise with regard to digitization, benefiting them in both social and economic contexts. The concept of the HRL, as well as some examples of the projects and the role of AI, are outlined in this chapter in order to provide a template for university teachers who might wish to adapt these concepts.
This chapter provides an overview of important topics in human resource management (HRM) that are affected by digitalization and automation. It is outlined how work in HRM is changing in areas such as mental health at work, work design, leadership and personnel development. The last section shifts focus and introduces a new way of working in HRM, known as HR analytics or people analytics. The fact that the various topics are not independent of each other and indeed intersect with each other is illuminated in the individual sections.
In the past decade, crowdworking on online labor market platforms has become the main source of income for a growing number of people worldwide. This development has led to increasing political and scientific interest in the wages that people can earn on such platforms. In this article, we extend the literature based on a single platform, region, or category of crowdworking by conducting a meta-analysis of the prevalent hourly wages. After a systematic and rigorous literature search, we consider 20 primary empirical studies, including 104 wages and 76,282 data points from 22 platforms, eight different countries, and a time span of 12 years. We find that, on average, microwork results in an hourly wage of less than $6. This wage is significantly lower than the mean wage of online freelancers, which is roughly three times higher. We find that hourly wages accounting for unpaid work, such as searching for tasks and communicating with requesters, tend to be significantly lower than wages not considering unpaid work. Legislators and researchers evaluating wages in crowdworking should be aware of this bias when assessing hourly wages, given that the majority of the literature does not account for the effect of unpaid work time on crowdworking wages. To foster the comparability of different research results, we suggest that scholars consider a wage malus to account for unpaid work. Finally, we find that hourly wages collected through surveys tend to be lower than wages collected via browser plugins or other technical data collection methods.
This article reports on an investigation of the role of lock-in exploitation and the impact of reputation portability on workers’ switching behaviors in online labor markets. Online platforms using reputation mechanisms typically prevent users from transferring their ratings to other platforms, inducing lock-in effects and high switching costs and leaving users vulnerable to platform exploitation. With a theoretical model, in which workers in online labor markets are locked-in by their reputational data, we test the effects using an online lab-in-the-field decision experiment. In addition to comparing a policy regime with and without reputation portability, we vary lock-in exploitation using platform fees to consider how switching behavior might differ according to monetary motives and fairness preferences. Theoretically, this study reveals how reputational investments can produce switching costs that platforms can exploit. Experimentally, the results suggest that reputation portability mitigates lock-in effects, making users less susceptible to lock-in exploitation. The data further show that switching is driven primarily by monetary motives, but perceiving the fee as unfair also has a significant role.
Are investors willing to give up a higher return if the investment generates positive environmental impact? We investigate this question with a decision experiment among crowdfunders, where they choose between a higher return or environmental impact. Overall, 65% of investors choose environmental impact at the expense of a higher return for sufficiently large impact, 14% choose impact independent of the magnitude of impact, while 21% choose the higher return independent of impact. Combining the experimental data with historical investments, we find that investors allocate a larger share of funds to green projects if they value environmental impact more, and if they expect green projects to be more profitable. These findings suggest that investors have a preference for positive environmental impact, and satisfy it by investing in green projects. We further show that the preference for environmental impact is distinct from a preference for positive social impact. Finally, we introduce new survey measures of impact for future use, which are experimentally validated and predict field behavior.
We study the impact fintech startups have on the performance and the default risk of traditional financial institutions. We find a positive relationship between fintech startup formations and incumbent institutions’ performance for the period from 2005 to 2018 and a large sample of financial institutions from 87 countries. We further analyze the link between fintech startup formations and the default risk of traditional financial institutions. Fintech startup formations decreases stock return volatility of incumbent institutions and decreases the systemic risk exposure of financial institutions. Our findings indicate that the development of fintech startups should be monitored very closely by legislators and financial supervisory authorities, because fintechs not only have a positive effect on the financial sector’s performance, but can also improve financial stability relative to the status quo.
In this study, we investigate whether and to what extent community managers in online collaborative communities can stimulate community activities through their engagement. Using a novel data set of 22 large online idea crowdsourcing campaigns, we find that moderate but steady manager activities are adequate to enhance community participation. Moreover, we show that appreciation, motivation, and intellectual stimulation by managers are positively associated with community participation but that the effectiveness of these communication strategies depends on the form of participation community managers want to encourage. Finally, the data reveal that community manager activities requiring more effort, such as media file uploads (vs. simple written comments), have a stronger effect on community participation.
When using digital devices and services, individuals provide their personal data to organizations in exchange for gains in various domains of life. Organizations use these data to run technologies such as smart assistants, augmented reality, and robotics. Most often, these organizations seek to make a profit. Individuals can, however, also provide personal data to public databases that enable nonprofit organizations to promote social welfare if sufficient data are contributed. Regulators have therefore called for efficient ways to help the public collectively benefit from its own data. By implementing an online experiment among 1,696 US citizens, we find that individuals would donate their data even when at risk of getting leaked. The willingness to provide personal data depends on the risk level of a data leak but not on a realistic impact of the data on social welfare. Individuals are less willing to donate their data to the private industry than to academia or the government. Finally, individuals are not sensitive to whether the data are processed by a humansupervised or a self-learning smart assistant.
We study the extent of fraud in initial coin offerings (ICOs), and whether information disclosure prior to the issuance predicts fraud. We document different types of fraud, and that fraudulent ICOs are on average much larger than the sample average. Issuers that disclose their code on GitHub are more likely to be targeted by phishing and hacker activities, which suggests that there are risks related to disclosing the code. Generally, we find it extremely difficult to predict fraud with the information available at the time of issuance. This calls for the need to install a thirdparty that certifies the quality of the issuers, such as specialized platforms, or the engagement of institutional investors and venture capital funds that can perform a due diligence and thus verify the quality of the project.