Consumer Privacy Research Articles

Self-Service Technology: Benefits and Challenges

The adoption of self-service technology (SST) has increased significantly in business and society, particularly since the advent of the COVID-19 pandemic. SST is a vital tool for many businesses in their quest to improve operational efficiency and increase profits. However, businesses must weigh the advantages of this technology against its drawbacks before committing to it. This study seeks to investigate the applications and benefits of SST, the challenges of adopting SST, and some implementation ideas for SST techniques. Furthermore, it seeks to examine whether SST techniques are successful and whether managers should consider SST as a strategic objective. The key research question in this study is how SST initiatives can help a business, and what challenges can managers encounter when implementing them. A review of the current literature shows that SST has helped companies increase customer satisfaction, increase management efficiency and effectiveness, cut costs, and increase financial performance. Some of the challenges facing SST usage include customer reluctance, consumer privacy, and substantial investments and costs for implementation.

Privacy‐Enhancing Factors and Consumer Concerns: The Moderating Effects of the General Data Protection Regulation

AbstractPrivacy is a fundamental right, with humans often wanting to keep their information private. Technological advancements are now challenging this right by reducing our control and creating enhanced privacy risks. The General Data Protection Regulation (GDPR) is a law introduced to protect this right. The aim of this paper is to analyse how privacy‐enhancing factors can influence consumer privacy concerns and whether these have been affected by consumer beliefs relating to the GDPR. This paper examines the influence of four privacy‐enhancing factors (i.e. organizational trust, perceived personalization value, perceived consumer control and data transparency), which mostly have personality or attitude‐like traits, and the GDPR as a moderating variable. Data were collected from 1154 respondents residing in European Union countries. Results reveal that personalization value has a significant negative relationship with privacy concerns, while consumer control shares a significant positive relationship with privacy concerns. Organizational trust and data transparency did not have a significant effect on privacy concerns. The relationship between the privacy‐enhancing factors and privacy concerns was not moderated by consumer belief in the GDPR. Implications and recommendations are provided to indicate which privacy‐enhancing factors should be chosen to reduce privacy concerns and to highlight the role of the GDPR in moderating these relationships. Ultimately, the study's findings provide useful insights for firms operating online in Europe and marketers aiming to reduce their consumers’ privacy concerns.

Context-aware privacy-preserving access control for mobile computing

In mobile and pervasive computing applications, opportunistic connections allow co-located devices to exchange data directly. Keeping data sharing local enables large-scale cooperative applications and empowers individual users to control what and how information is shared. Supporting such applications requires runtime frameworks that allow them to manage the who, what, when, and how of access to resources. Existing frameworks have limited expressiveness and do not allow data owners to modulate the granularity of information released. In addition, these frameworks focus exclusively on security and privacy concerns of data providers and do not consider the privacy of data consumers. We present PADEC, a context-sensitive, privacy-aware framework that allows users to define rich access control rules over their resources and to attach levels of granularity to each rule. PADEC is also characterized by its expressiveness, allowing users to decide under which conditions should which information be shared. We provide a formal definition of PADEC and an implementation based on private function evaluation. Our evaluation shows that PADEC is more expressive than other mechanisms, protecting privacy of both consumers and providers.

A hybrid Bayesian BWM and Pythagorean fuzzy WASPAS-based decision-making framework for parcel locker location selection problem.

One of the main causes of the significant commercial vehicle traffic in the city region is last-mile deliveries. Parcel lockers, which are one of the easiest and most environmentally friendly solutions for last-mile delivery, are one of the most studied subjects recently. The parcel locker ensures consumer privacy while being quick and efficient. Its full-time service can effectively address the issue of student and office worker pickup. In this paper, the location of a parcel locker intended to be established in the most convenient location in Beşiktaş district of İstanbul, Turkey has been determined. This problem can be solved using a multi-criteria decision-making (MCDM) structure due to the availability of numerous aspects that must be considered while choosing the optimum location. Additionally, the benefit of fuzzy logic is employed to translate expert opinions into mathematical expressions and incorporate them into decision-making processes. To choose the ideal location for the parcel locker, a novel model integrating the Bayesian Best Worst Method (B-BWM) and Pythagorean fuzzy Weighted Aggregated Sum Product Assessment (PF-WASPAS) approaches is proposed for the first time in the literature. Additionally, a sensitivity analysis is conducted to evaluate the model's robustness. As a consequence, the suggested model effectively identifies the best location for a parcel locker in Istanbul.

The role of marketing analytics in the ethical consumption of online consumers

Modern marketing requires a dynamic shift from traditional to digital worlds where technology with data handling significantly needs marketing analytics effectiveness with a customised approach, leading to ethical online consumption for consumers. This current study validated the model developed from the contributions of Davis et al. (2021) and Grewal et al. (2020) by focusing on the effectiveness of marketing analytics. Data was collected from 435 respondents who were either business heads or employees working at a managerial level through a well-drafted questionnaire. Hair et al. (2017) incitation on PLS-SEM robust measuring standards helped in model validation through internal consistency, validity, reliability, multicollinearity, nonlinearity, effect size, HTMT, R2 and finally model fitness. The empirical prediction of this research gives a clear signal to corporate to redesign their present online marketing models by using marketing analytics and then critically analysing the role of consumer privacy, artificial intelligence, and marketing mix while promoting their products or services through web or mobile apps. Finally, to what extent marketing analyses can help in generating a user-friendly interface for online consumers by capturing their personality traits with a cognitive mindset is still critical to understand and may encourage future work.

A Day Ahead Electrical Appliance Planning of Residential Units in a Smart Home Network Using ITS-BF Algorithm

The concept of energy management in smart homes has received increasing attention in recent years, particularly on issues such as creating a balance between user privacy and reducing energy costs. Accordingly, this article proposes a user-oriented multi-objective approach, which minimizes energy costs and maximizes consumer privacy. In addition, a home energy management system is suggested for smart homes to optimize the energy consumption pattern of appliances. On the other hand, considering challenges in energy management of smart homes, the concept of demand-side management (DSM) is introduced. The objective of the proposed method is to reduce energy consumption to lower consumers’ electricity bills. Also, it improves user comfort (UC) in average waiting time conditions. In this research, a smart home equipped with an energy management system and smart home appliances that can inject electric power into the upstream network is considered the main system. This framework leads to a multi-objective optimization problem in which the two objectives mentioned above are considered two separate dimensions. To solve the problem, an ITS-BF Algorithm is used, which employs a random search to schedule home appliances and batteries based on the application of flexible devices in smart homes. The case studies show that the proposed method can considerably respect and satisfy users’ privacy and reduce the energy cost to an acceptable level. Finally, the numerical results obtained from the simulation have been analyzed to evaluate the proposed method’s efficiency. The simulation results show that an ITS-BF algorithm performs better than the existing methods in reducing costs and waiting time.

What did the digital age mean for privacy in the United States?

Over the course of the last 3 decades, the world has seen monumental shifts in how information is collected, transmitted, and disseminated. Every aspect of our personalities that live on the internet, including our browser history, photos we post to social media, our shopping decisions and our selection of online friends, has been collated, quantified, and assimilated into a digital profile, which has skyrocketing value to an increasing number of businesses. With these developments in technology come the inevitable questions of ownership of such data, its use, misuse and even possible theft. This paper takes a comprehensive and comparative look at the data privacy legislature in the two largest data hubs in the world, namely the United States and the European Union. The paper also seeks to address the shortcomings of certain, past legislative decisions, and makes a recommendation for the future. To do this, we analyze the events of the past, using the 2016 Facebook/Cambridge Analytica data scandal as a focal point. On analyzing the major differences between American privacy law and the preeminent document on data privacy at the time, namely the Global Data Privacy Regulations (GDPR), we conclude that data privacy in the United States is in its nascent stages, in dire need of an overhaul. The California Consumer Privacy Act is the legislature that comes close to mimicking the function of the GDPR, albeit at a much smaller scale. The other remedies include the American Data Privacy and Protection Act (ADPPA), which is already under consideration by Congress, or a state-by-state approach.

Privacy and Competition: Discord or Harmony?

Privacy and competition law pursue different goals that do not always align, but they can be harmonized under the right approach. Recent proposals in Congress, however, put privacy and competition values in direct conflict, likely at the expense of consumer privacy. Rather than diminishing consumer privacy and security through competition legislation, Congress should enact a comprehensive consumer privacy law that protects consumers while leveling the playing field for all competitors. This article addresses recent calls to regulate consumer privacy and the commercial use of data through competition law. First, we address the changing landscape of consumer privacy and calls for additional regulation. We then discuss recent proposals to use competition law to address issues stemming from widespread data collection and aggregation. Finally, we describe the collision course of antitrust and privacy in recent legislative proposals and offer an alternative path forward.

Designing Distributed Ledger technologies, like Blockchain, for advertising markets

Distributed Ledger Technologies (DLTs), like Blockchain, could help improve brand safety, consumer privacy and transparency in digital advertising. However, paid advertisements transfer attention, money, and data between three parties: advertiser, consumer, and publisher. Therefore advertising-focused DLTs face more complex design considerations than currency-focused DLTs. We describe four key DLT characteristics: structure, participation/governance, transparency, and terms of exchange. We survey current advertising-focused DLTs and find they each serve only two of the three contracting parties in advertising transactions. We make design recommendations for future advertising-focused DLTs, including a goal of serving both consumers and publishers in addition to advertisers. We also recommend governance, transparency and terms-of-trade considerations. Advertising-focused DLTs have significant promise but also significant obstacles, including multi-sided “chicken-and-egg” problems in standards adoption.

How Do Privacy Laws Impact the Value for Advertisers, Publishers and Users in the Online Advertising Market? A Comparison of the EU, US and China

Regulators worldwide have been implementing different privacy laws. They vary in their impact on the value for advertisers, publishers and users, but not much is known about these differences. This article focuses on three important privacy laws (i.e., General Data Protection Regulation [GDPR], California Consumer Privacy Act [CCPA] and Personal Information Protection Law [PIPL]) and compares their impact on the value for the three primary actors of the online advertising market, namely, advertisers, publishers and users. This article first compares these three privacy laws by developing a legal strictness score. It then uses the existing literature to derive the effects of the legal strictness of each privacy law on each actor’s value. Finally, it quantifies the three privacy laws’ impact on each actor’s value. The results show that GDPR and PIPL are similar and stricter than CCPA. Stricter privacy laws bring larger negative changes to the value for actors. As a result, both GDPR and PIPL decrease the actors’ value more substantially than CCPA. These value declines are the largest for publishers and are rather similar for users and advertisers. Scholars and practitioners can use our findings to explore ways to create value for multiple actors under various privacy laws.

Does e-retailer's country of origin influence consumer privacy, trust and purchase intention?

PurposeConsumers are worried about sharing their sensitive information during online shopping due to the e-tailer’s unethical practices and hacking-related concerns. Prior research has established the country of origin (COO) as a trust-building cue; however, it requires empirical testing in the online retailing context. The present study aims to examine the e-tailer COO’s effect on consumer privacy, trust and purchase intention.Design/methodology/approachAn online survey floated a seven-point Likert scale questionnaire and invited the receivers to participate in the investigation over e-mails and text messages. A total of 355 usable responses were analyzed using R programming.FindingsThis study empirically validated a proposed conceptual model examining the influence of COO on consumer privacy, trust and purchase intention. The findings suggest that COO influences consumer privacy, trust and purchase intention. This study further found that the privacy practices of online retailers positively impact consumer trust. Trust acts as a mediating factor in influencing purchase intention.Practical implicationsThis study offers valuable insights for advancing the research agenda and actionable inputs to e-commerce managers for alleviating consumer privacy concerns in emerging economies. Future researchers can test the proposed model in other demographic and e-commerce settings.Originality/valueThis study contributes to the present knowledge on consumer privacy in online retailing in the Indian context. This paper also examines the relationship of COO with consumer privacy, trust and purchase intention, an underexplored research area in emerging markets.

Citizens’ Data Privacy in China: The State of the Art of the Personal Information Protection Law (PIPL)

The Personal Information Protection Law (PIPL) was launched on 1 November 2021 in China. This article provides a state-of-the-art review of PIPL through a policy analysis. This paper aims to compare the three main worldwide data privacy paradigms that exist at present: (i) the General Data Protection Regulation (GDPR) in the E.U., (ii) the California Consumer Privacy Act (CCPA) in the U.S., and (iii) PIPL in China. The research question is twofold: (i) how will PIPL affect the data privacy of Chinese citizens and consequently, (ii) how will PIPL influence the global digital order, particularly paralleling the existing GDPR and CCPA? In the first section, this article introduces the topic of data privacy as a global concern, followed in the second section by an in-depth policy context analysis of PIPL and a literature review on privacy that elucidates in particular the impact of the Social Credit System (SCS). In the third section, a comparative benchmarking is carried out between the GDPR, CCPA, and PIPL. Methodologically, policy documents around PIPL will be analyzed. In the fourth section, the case study of Shenzhen will be examined by undertaking a multi-stakeholder analysis following the Penta Helix framework. The article concludes by responding to the research questions, acknowledging limitations, and presenting future research avenues.

Fox in the henhouse: The delegation of regulatory and privacy enforcement to big tech

Abstract The Federal Trade Commission (FTC) requires tech giants to identify and remove apps from their platforms that use deceitful sales tactics or violate user privacy. Tech giants have often resisted FTC orders because policing diminishes their profits. But while some firms have eventually complied with FTC demands, other firms have continued to shirk enforcement at the risk of escalating fines. What accounts for these different responses? Examining Apple, Google and Facebook, we find that tech giants willingly police consumer fraud but not consumer privacy violations. Failures to police fraud have led to public complaints and negative press attention, while failures to police data breaches often go undetected by users, the media and thus the FTC. We conclude that tech giants can act as effective regulatory agents on the government's behalf, but only when they police activities they cannot conceal.

DFTMicroagg: a dual-level anonymization algorithm for smart grid data

The introduction of advanced metering infrastructure (AMI) smart meters has given rise to fine-grained electricity usage data at different levels of time granularity. AMI collects high-frequency daily energy consumption data that enables utility companies and data aggregators to perform a rich set of grid operations such as demand response, grid monitoring, load forecasting and many more. However, the privacy concerns associated with daily energy consumption data has been raised. Existing studies on data anonymization for smart grid data focused on the direct application of perturbation algorithms, such as microaggregation, to protect the privacy of consumers. In this paper, we empirically show that reliance on microaggregation alone is not sufficient to protect smart grid data. Therefore, we propose DFTMicroagg algorithm that provides a dual level of perturbation to improve privacy. The algorithm leverages the benefits of discrete Fourier transform (DFT) and microaggregation to provide additional layer of protection. We evaluated our algorithm on two publicly available smart grid datasets with millions of smart meters readings. Experimental results based on clustering analysis using k-Means, classification via k-nearest neighbor (kNN) algorithm and mean hourly energy consumption forecast using Seasonal Auto-Regressive Integrated Moving Average with eXogenous (SARIMAX) factors model further proved the applicability of the proposed method. Our approach provides utility companies with more flexibility to control the level of protection for their published energy data.

‘Click Here to (Dis)agree’: Australian Law and Practice in Relation to Informed Consent

This article provides a detailed examination of issues with the legislative framework meeting consumer expectations in relation to informed consent, particularly in relation to the Privacy Act 1988 (Cth) and the Australian Consumer Law (ACL). It also discusses two potential areas that might assist in fleshing out the current minimalist legislative definition of consent: namely case law and guidelines issued by the Australian privacy regulator, the Office of the Australian Information Commissioner (the OAIC Guidelines). However the case law available in this area is sparse and provides little guidance. Additionally, the non-binding OAIC Guidelines, while referred to frequently in privacy determinations, appear to have little real effect in influencing the data practices of many firms. The article concludes that the current approach to regulating ‘consent’ has not provided adequate protections for consumers in light of extensive empirical evidence regarding consumer privacy preferences and attitudes regarding data practices. Australia, Data Protection, Privacy, Informed Consent

Paradox or Pressure? Consumer Expectations and the Australian Privacy Act

This article outlines the empirical evidence in Australia regarding consumer privacy expectations as to commercial dealings with their data. Despite widespread disclosure of personal information from consumers to businesses, the evidence indicates that consumers believe that their privacy is important, and their personal information should be protected from misuse. However, this so-called ‘privacy paradox’, where consumer behaviour in ‘consenting’ to substantial data collection of personal information is not, in fact, a paradox. Rather, it is explained by consumers’ expectations and perceptions around data collection and handling by commercial entities: most importantly a perception by many that they have little or no choice or control over their personal information. It also shows that they expect the legal system to protect them against misuse of that data. With these expectations in mind, the article proceeds to introduce key parts of the general framework of the Privacy Act 1988 (Cth) under which digital data practices are currently regulated in Australia. Australia, Data Protection, Privacy, Consumers, Privacy Paradox

Smart Meter Data Obfuscation With a Hybrid Privacy-Preserving Data Publishing Scheme Without a Trusted Third Party

Smart electricity meters as a prominent instance of the Internet of Things (IoT) have driven more efficient energy services in smart grids but also created growing concerns of consumer privacy. Homomorphic encryption of consumption data is a conventional solution for privacy protection, but it incurs a high computational burden to the resource-restrained smart meters (SMs) due to the encryption of high-frequency consumption readings. Perturbation is another major approach in providing the privacy protection of SM readings which is highly efficient. However, most existing perturbation-based works inadequately balance the tradeoff between the need of hiding individual consumption profiles and the need of retaining utility’s quality. This article proposes a hybrid privacy-preserving electricity consumption data publishing scheme without a trusted third party which utilizes the desirable properties of both perturbation and cryptography for better privacy-utility tradeoff and efficiency. The proposed scheme for fine-grained SM consumption data consists of two phases, which are noise generation and noise distribution. In the first phase, a distributed perturbation method is designed to provide differential privacy protection for high-frequency consumption while retaining the accuracy of energy services like regional load forecasting. In the second phase, a private noise distribution protocol, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$nn$ </tex-math></inline-formula> -PND, securely distributes <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> noise elements generated by an energy distribution operator to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> SMs in a semihonest adversarial model. Formal proofs of correctness and privacy of the scheme are provided. Experiments of regional short-term electricity consumption forecast using real-world data sets demonstrate the preservation of the utility over the masked data of this scheme.

Information and Intelligence in Direct Marketing

Companies require collecting information and generating intelligence about their customers to attract, influence, and to develop relationships with them. Companies employ direct marketing channels for collecting information from customers. Although collection of information through direct marketing results in authentic information, it raises concerns about the different unfair practices followed by direct marketers. The study discusses about unfair practices adopted by direct marketers in collection of information, the issues of invasion of privacy and security of individuals, and the need for protection of consumer privacy and security. The study also focuses on the various rules and regulations formulated by regulatory bodies and governments of countries to protect customers. Different initiatives which are taken by companies to protect customers are discussed. Direct marketing channels allow companies to collect information about customers. However, companies should ensure customer privacy and security and should think about their welfare for building long-term relationships.

IT and the many challenges for nonprofits

Security is a concern for companies in any industry. While it is often discussed in the context of governments and private‐sector organizations, nonprofits need to be just as vigilant when it comes to understanding the need for data privacy and implementing solutions to keep critical information safe. Informed donors, as well as government and regulatory agencies, are keenly interested in data security issues, such as personally identifiable information and hardened systems. Penalties for data breaches can be severe both in monetary costs as well as in reputational damage. Furthermore, privacy laws, such as the California Consumer Privacy Act (CCPA), can have an impact on nonprofits. That's because, although nonprofit organizations in California are exempt from the CCPA, they must take due care of their own vendors and providers. Through their actions, they must demonstrate that they are following the law. The fact is that consumers, in general, do expect their private data to be protected by the “steward of the data.” If that steward is the nonprofit, then that is where the perceived and implied obligation to protect data may lie.

Consumer Concerns of Personal Data and Privacy: A Systematic Review

This article provides an overview of extant literature addressing consumer privacy in the digital context over the last decade. The number of cyber-attacks, data leakages, and invasive uses of technological devices has considerably increased in recent years, leading to an increase in consumers’ concerns about their personal information. The objective of the present research was to develop an analysis of the existing literature on consumer privacy, highlighting the state recent research dealing with consumers’ perceptions of threats and risks to their privacy in the digital context. The study developed a sensitivity continuum regarding types of personal consumer data, as well as data use concerns. The results revealed that some data was considered more sensitive than other data; that is, there was a perception of sensitivity and importance attributed by consumers to some data. This fact speaks to the concept of the privacy paradox, in which users voluntarily renounce their data in exchange for services, products, and benefits. Finally, managerial implications about information security, as well as the perceived meanings of privacy. Keywords: privacy; data privacy; consumer privacy; consumer perception; privacy concerns; literature review