Accountable algorithms? The ethical implications of data-driven business models

Ethical governance model for the data economy ecosystems

Purpose

Data economy is a recent phenomenon, raised by digital transformation and platformisation, which has enabled the concentration of data that can be used in economic purposes. However, there is a lack of clear procedures and ethical rules on how data economy ecosystems are governed. As a response to the current situation, there has been criticism and demands for the governance of data use to prevent unethical consequences that have already manifested. Thus, ethical governance of the data economy ecosystems is needed. The purpose of this paper is to introduce a new ethical governance model for data economy ecosystems. The proposed model offers a more balanced solution for the current situation where a few global large-scale enterprises dominate the data market and may use oligopolistic power over other stakeholders.

Design/methodology/approach

This is a conceptual article that covers theory-based discourse ethical reflection of data economy ecosystems governance. The study is based on the premise of the discourse ethics where inclusion of all stakeholders is needed for creating a transparent and ethical data economy.

Findings

This article offers self-regulation tool for data economy ecosystems by discourse ethical approach which is designed in the governance model. The model aims to balance data “markets” by offering more transparent, democratic and equal system than currently.

Originality/value

By offering a new ethically justified governance model, we may create a trust structure where rules are visible and all stakeholders are treated fairly.

Programmatic advertising in online retailing: consumer perceptions and future avenues

Purpose

Digital advertising enables retailers to rely on large volumes of data on consumers and even leverage artificial intelligence (AI) to target consumers online with personalised and context-aware advertisements. One recent example of such advertisements is programmatic advertising (PA), which is facilitated by automatic bidding systems. Given that retailers are expected to increase their use of PA in the future, further insights on the pros and cons of PA are required. This paper aims to enhance the understanding of the implications of PA use for retailers.

Design/methodology/approach

A theoretical overview is conducted that compares PA to traditional advertising, with an empirical investigation into consumer attitudes towards PA (an online survey of 189 consumers using an experimental design) and a research agenda.

Findings

Consumer attitudes towards PA are positively related to attitudes towards the retailer. Further, perceived ad relevance is positively related to attitudes towards PA, which is moderated by (1) consumer perceptions of risks related to sharing their data with retailers online and (2) consumer perceptions of AI's positive potential. Surprisingly, the disclosed use of AI for PA does not significantly influence consumer attitudes towards PA.

Originality/value

This paper contributes to the literature on technology-enabled services by empirically demonstrating that ad relevance drives consumer attitudes towards PA. This paper further examines two contingencies: risk beliefs related to data (i.e. the source of PA) and perceptions of AI (i.e. the somewhat nebulous technology associated with PA) as beneficial. A research agenda illuminates central topics to guide future research on PA in retailing.

Data-driven strategies in operation management: mining user-generated content in Twitter

In recent years, the business ecosystem has focused on understanding new ways of automating, collecting, and analyzing data in order to improve products and business models. These actions allow operations management to improve prediction, value creation, optimization, and automatization. In this study, we develop a novel methodology based on data-mining techniques and apply it to identify insights regarding the characteristics of new business models in operations management. The data analyzed in the present study are user-generated content from Twitter. The results are validated using the methods based on Computer-Aided Text Analysis. Specifically, a sentimental analysis with TextBlob on which experiments are performed using vector classifier, multinomial naïve Bayes, logistic regression, and random forest classifier is used. Then, a Latent Dirichlet Allocation is applied to separate the sample into topics based on sentiments to calculate keyness and p-value. Finally, these results are analyzed with a textual analysis developed in Python. Based on the results, we identify 8 topics, of which 5 are positive (Automation, Data, Forecasting, Mobile accessibility and Employee experiences), 1 topic is negative (Intelligence Security), and 2 topics are neutral (Operational CRM, Digital teams). The paper concludes with a discussion of the main characteristics of the business models in the OM sector that use DDI. In addition, we formulate 26 research questions to be explored in future studies.

Algorithmic branding through platform assemblages: core conceptions and research directions for a new era of marketing and service management

Purpose

Contemporary branding transpires in a complex technological and media environment whose key contextual characteristics remain largely unexplained. The article provides a conceptual understanding of the elements of contemporary branding as they take place using networked platforms and explains them as an increasingly important practice that affects customer and manager experience.

Design/methodology/approach

This article draws on a variety of recent sources to synthesize a model that offers a more contextualized, comprehensive and up-to-date understanding of how branding has become and is being altered because of the use of branded service platforms and algorithms.

Findings

Core terminology about technoculture, technocultural fields, platform assemblages, affordances, algorithms and networks of desire set the foundation for a deeper conceptual understanding of the novel elements of algorithmic branding. Algorithmic branding transcended the mere attachment of specific “mythic” qualities to a product or experience and has morphed into the multidimensional process of using media to manage communication. The goal of marketers is now to use engagement practices as well as algorithmic activation, amplification, customization and connectivity to drive consumers deeper into the brand spiral, entangling them in networks of brand-related desire.

Practical implications

The model has a range of important managerial implications for brand management and managerial relations. It promotes a understanding of platform brands as service brands. It underscores and models the interconnected role that consumers, devices and algorithms, as well as technology companies and their own service brands play in corporate branding efforts. It suggests that consumers might unduly trust these service platforms. It points to the growing importance of platforms' service brands and the consequent surrender of branding power to technology companies. And it also provides a range of important ethical and pragmatic questions that curious marketers, researchers and policy-makers may examine.

Originality/value

This model provides a fresh look at the important topic of branding today, updating prior conceptions with a comprehensive and contextually grounded model of service platforms and algorithmic branding.