Do Awards Incentivize Non-Winners to Work Harder on CSR?

Curated on Posted on by Stefaan Verhulst

Article by Jiangyan Li, Juelin Yin, Wei Shi, And Xiwei Yi: “As corporate lists and awards that rank and recognize firms for superior social reputation have proliferated in recent years, the field of CSR is also replete with various types of awards given out to firms or CEOs, such as Fortune’s “Most Admired Companies” rankings and “Best 100 Companies to Work For” lists. Such awards serve to both reward and incentivize firms to become more dedicated to CSR. Prior research has primarily focused on the effects of awards on award-winning firms; however, the effectiveness and implications of such awards as incentives to non-winning firms remain understudied. Therefore, in the article of “Keeping up with the Joneses: Role of CSR Awards in Incentivizing Non-Winners’ CSR” published by Business & Society, we are curious about whether such CSR awards could successfully incentivize non-winning firms to catch up with their winning competitors.

Drawing on the awareness-motivation-capability (AMC) framework developed in the competitive dynamics literature, we use a sample of Chinese listed firms from 2009 to 2015 to investigate how competitors’ CSR award winning can influence focal firms’ CSR. The empirical results show that non-winning firms indeed improve their CSR after their competitors have won CSR awards. However, non-winning firms’ improvement in CSR may vary in different scenarios. For instance, media exposure can play an important informational role in reducing information asymmetries and inducing competitive actions among competitors, therefore, non-winning firms’ improvement in CSR is more salient when award-winning firms are more visible in the media. Meanwhile, when CSR award winners perform better financially, non-winners will be more motivated to respond to their competitors’ wins. Further, firms with a higher level of prior CSR are more capable of improving their CSR and therefore are more likely to respond to their competitors’ wins…(More)”.

Towards Efficient Information Sharing in Network Markets

Curated on Posted on by Stefaan Verhulst

Paper by Bertin Martens, Geoffrey Parker, Georgios Petropoulos and Marshall W. Van Alstyne: “Digital platforms facilitate interactions between consumers and merchants that allow the collection of profiling information which drives innovation and welfare. Private incentives, however, lead to information asymmetries resulting in market failures both on-platform, among merchants, and off-platform, among competing platforms. This paper develops two product differentiation models to study private and social incentives to share information within and between platforms. We show that there is scope for ex-ante regulation of mandatory data sharing that improves social welfare better than competing interventions such as barring entry, break-up, forced divestiture, or limiting recommendation steering. These alternate proposals do not make efficient use of information. We argue that the location of data access matters and develop a regulatory framework that introduces a new data right for platform users, the in-situ data right, which is associated with positive welfare gains. By construction, this right enables effective information sharing, together with its context, without reducing the value created by network effects. It also enables regulatory oversight but limits data privacy leakages. We discuss crucial elements of its implementation in order to achieve innovation-friendly and competitive digital markets…(More)”.

Beyond good intentions: Navigating the ethical dilemmas facing the technology industry

Curated on Posted on by Stefaan Verhulst

Report by Paul Silverglate, Jessica Kosmowski, Hilary Horn, and David Jarvis: “There’s no doubt that the technology industry has achieved tremendous success. Its ubiquitous products and services power our digital society. Prolonged ubiquity, scale, and influence, however, have forced the industry to face many unforeseen, difficult ethical dilemmas. These dilemmas weren’t necessarily created by the tech industry, but many in the industry find themselves at a “convergence point” where they can no longer leave these issues at the margins.

Because of “big tech’s” perceived power, lagging regulation, and an absence of common industry practices, many consumers, investors, employees, and governments are demanding greater overall accountability from the industry. The technology industry is also becoming more introspective, examining its own ethical principles, and exploring how to better manage its size and authority. No matter who first said it, it’s widely believed that the more power you have, the more responsibility you have to use it wisely. The tech industry is now being asked to do more across a growing number of areas. Without a holistic approach to these issues, tech companies will likely struggle to meet today’s biggest concerns and fail to prepare for tomorrow’s.

Five dilemmas for the tech industry to navigate

While these aren’t the only challenges, here are five areas of concern the technology industry is currently facing. Steps are being taken, but is it enough?

Data usage: According to the UN, 128 of 194 countries currently have enacted some form of data protection and privacy legislation. Even more regulation and increased enforcement are being considered. This attention is due to multiple industry problems including abuse of consumer data and massive data breaches. Until clear and universal standards emerge, the industry continues to work toward addressing this dilemma. This includes making data privacy a core tenet and competitive differentiator, like Apple, which recently released an app tracking transparency feature. We’re also seeing greater market demand, evident by the significant growth of the privacy tech industry. Will companies simply do the minimum amount required to comply with data-related regulations, or will they go above and beyond to collect, use, and protect data in a more equitable way for everyone?…(More)”.

A real-time revolution will up-end the practice of macroeconomics

Curated on Posted on by Stefaan Verhulst

The Economist: “The pandemic has hastened a shift towards novel data and fast analysis…Does anyone really understand what is going on in the world economy? The pandemic has made plenty of observers look clueless. Few predicted $80 oil, let alone fleets of container ships waiting outside Californian and Chinese ports. As covid-19 let rip in 2020, forecasters overestimated how high unemployment would be by the end of the year. Today prices are rising faster than expected and nobody is sure if inflation and wages will spiral upward. For all their equations and theories, economists are often fumbling in the dark, with too little information to pick the policies that would maximise jobs and growth.

Yet, as we report this week, the age of bewilderment is starting to give way to greater enlightenment. The world is on the brink of a real-time revolution in economics, as the quality and timeliness of information are transformed. Big firms from Amazon to Netflix already use instant data to monitor grocery deliveries and how many people are glued to “Squid Game”. The pandemic has led governments and central banks to experiment, from monitoring restaurant bookings to tracking card payments. The results are still rudimentary, but as digital devices, sensors and fast payments become ubiquitous, the ability to observe the economy accurately and speedily will improve. That holds open the promise of better public-sector decision-making—as well as the temptation for governments to meddle…(More)”.

Consumers Are Becoming Wise to Your Nudge

Curated on Posted on by Stefaan Verhulst

Article by Simon Shaw: “The broader question, one essential to both academics and practitioners, is how a world saturated with behavioral interventions might no longer resemble the one in which those interventions were first studied. Are we aiming at a moving target?

This was the basis for a research project we completed in February 2019 examining reactions of the British public to a range of behavioral interventions. We took a nationally representative sample of 2,102 British adults, and undertook an experimental evaluation of some of marketers’ most commonly used tactics.

We started by asking participants to consider a hypothetical scenario: using a hotel booking website to find a room to stay in the following week. We then showed a series of nine real-world scarcity and social proof claims made by an unnamed hotel booking website.

Two thirds of the British public (65 percent) interpreted examples of scarcity and social proof claims used by hotel booking websites as sales pressure. Half said they were likely to distrust the company as a result of seeing them (49 percent). Just one in six (16 percent) said they believed the claims. 

The results surprised us. We had expected there to be cynicism among a subgroup—perhaps people who booked hotels regularly, for example. The verbatim commentary from participants showed people see scarcity and social proof claims frequently online, most commonly in the travel, retail, and fashion sectors. They questioned truth of these ads, but were resigned to their use:

“It’s what I’ve seen often on hotel websites—it’s what they do to tempt you.”

“Have seen many websites do this kind of thing so don’t really feel differently when I do see it.”

In a follow up question, a third (34 percent) expressed a negative emotional reaction to these messages, choosing words like contempt and disgust from a precoded list. Crucially, this was because they ascribed bad intentions to the website. The messages were, in their view, designed to induce anxiety:

 “… almost certainly fake to try and panic you into buying without thinking.”

“I think this type of thing is to pressure you into booking for fear of losing out and not necessarily true.”

For these people, not only are these behavioral interventions not working but they’re having the reverse effect. We hypothesize psychological reactance is at play: people kick back when they feel they are being coerced….(More)”.

World Bank Cancels Flagship ‘Doing Business’ Report After Investigation

Curated on Posted on by Stefaan Verhulst

Article by Josh Zumbrun: “The World Bank canceled a prominent report rating the business environment of the world’s countries after an investigation concluded that senior bank management pressured staff to alter data affecting the ranking of China and other nations.

The leaders implicated include then World Bank Chief Executive Kristalina Georgieva, now managing director of the International Monetary Fund, and then World Bank President Jim Yong Kim.

The episode is a reputational hit for Ms. Georgieva, who disagreed with the investigators’ conclusions. As leader of the IMF, the lender of last resort to struggling countries around the world, she is in part responsible for managing political pressure from nations seeking to advance their own interests. It was also the latest example of the Chinese government seeking myriad ways to burnish its global standing.

The Doing Business report has been the subject of an external probe into the integrity of the report’s data. On Thursday, the bank released the results of that investigation, which concluded that senior bank leaders including Ms. Georgieva were involved in pressuring economists to improve China’s 2018 ranking. At the time, she and others were attempting to persuade China to support a boost in the bank’s funding….(More)”.

New York City to Require Food Delivery Services to Share Customer Data with Restaurants

Curated on Posted on by Stefaan Verhulst

Hunton Privacy Blog: “On August 29, 2021, a New York City Council bill amending the New York City Administrative Code to address customer data collected by food delivery services from online orders became law after the 30-day period for the mayor to sign or veto lapsed. Effective December 27, 2021, the law will permit restaurants to request customer data from third-party food delivery services and require delivery services to provide, on at least a monthly basis, such customer data until the restaurant “requests to no longer receive such customer data.” Customer data includes name, phone number, email address, delivery address and contents of the order.

Although customers are permitted to request that their customer data not be shared, the presumption under the law is that “customers have consented to the sharing of such customer data applicable to all online orders, unless the customer has made such a request in relation to a specific online order.” The food delivery services are required to provide on its website a way for customers to request that their data not be shared “in relation to such online order.” To “assist its customers with deciding whether their data should be shared,” delivery services must disclose to the customer (1) the data that may be shared with the restaurant and (2) the restaurant fulfilling the order as the recipient of the data.

The law will permit restaurants to use the customer data for marketing and other purposes, and prohibit delivery apps from restricting such activities by restaurants. Restaurants that receive the customer data, however, must allow customers to request and delete their customer data. In addition, restaurants are not permitted to sell, rent or disclose customer data to any other party in exchange for financial benefit, except with the express consent of the customer….(More)”.

Social welfare gains from innovation commons: Theory, evidence, and policy implications

Curated on Posted on by Stefaan Verhulst

Paper by Jason Potts, Andrew W. Torrance, Dietmar Harhoff and Eric A. von Hippel: “Innovation commons – which we define as repositories of freely-accessible, “open source” innovation-related information and data – are a very significant resource for innovating and innovation-adopting firms and individuals: Availability of free data and information reduces the innovation-specific private or open investment required to make the next innovative advance. Despite the clear social welfare value of innovation commons under many conditions, academic innovation research and innovation policymaking have to date focused almost entirely on enhancing private incentives to innovate by enabling innovators to keep some types of innovation-related information at least temporarily apart from the commons, via intellectual property rights.


In this paper, our focus is squarely on innovation commons theory, evidence, and policy implications. We first discuss the varying nature of and contents of innovation commons extant today. We summarize what is known about their functioning, their scale, the value they provide to innovators and to general social welfare, and the mechanisms by which this is accomplished. Perhaps somewhat counterintuitively, and with the important exception of major digital platform firms, we find that many who develop innovation-related information at private cost have private economic incentives to contribute their information to innovation commons for free access by free riders. We conclude with a discussion of the value of more general support for innovation commons, and how this could be provided by increased private and public investment in innovation commons “engineering”, and by specific forms of innovation policymaking to increase social welfare via enhancement of innovation commons….(More)”.

Looking Under the Hood of AI’s Dubious Models

Curated on Posted on by Stefaan Verhulst

Essay by Ethan Edwards: “In 2018, McKinsey Global Institute released “Notes from the AI Frontier,” a report that seeks to predict the economic impact of artificial intelligence. Looming over the report is how the changing nature of work might transform society and pose challenges for policymakers. The good news is that the experts at McKinsey think that automation will create more jobs than it eliminates, but obviously it’s not a simple question. And the answer they give rests on sophisticated econometric models that include a variety of qualifications and estimates. Such models are necessarily simplified, and even reductionistic, but are they useful? And for whom?

Without a doubt, when it comes to predictive modeling, the center of the action in our society—and the industry through which intense entrepreneurial energy and venture capital flows—is artificial intelligence itself. AI, of course, is nothing new. A subdiscipline dedicated to mimicking human capacities in sensing, language, and thought, it’s nearly as old as computer science itself. But for the last ten years or so the promise and the hype of AI have only accelerated. The most impressive results have come from something called “neural nets,” which has used linear algebra to mimic some of the biological structures of our brain cells and has been combined with far better hardware developed for video games. In only a few years, neural nets have revolutionized image processing, language processing, audio analysis, and media recommendation. The hype is that they can do far more.

If we are—as many promoters assert—close to AIs that can do everything a human knowledge worker can and more, that is obviously a disruptive, even revolutionary, prospect. It’s also a claim that has turned on the spigot of investment capital. And that’s one reason it’s difficult to know the true potential of the industry. Talking about AI is a winning formula for startups, researchers, and anyone who wants funding, enough that the term AI gets used for more than just neural nets and is now a label for computer-based automation in general. Older methods that have nothing to do with the new boom have been rebranded under AI. Think tanks and universities are hosting seminars on the impact of AI on fields on which it has so far had no impact. Some startups who have built their company’s future profitability on the promise of their AI systems have actually had to hire low-wage humans to act like the hoped-for intelligences for customers and investors while they wait for the technology to catch up. Such hype produces a funhouse mirror effect that distorts the potential and therefore the value of firms and all but guarantees that some startups will squander valuable resources with broken (or empty) promises. But as long as some companies do keep their promises it’s gamble that many investors are still willing to take….(More)”.

Data ownership revisited: clarifying data accountabilities in times of big data and analytics

Curated on Posted on by Stefaan Verhulst

Paper by Martin Fadler and Christine Legner: “Today, a myriad of data is generated via connected devices and digital applications. In order to benefit from these data, companies have to develop their capabilities related to big data and analytics (BDA). A critical factor that is often cited concerning the “soft” aspects of BDA is data ownership, i.e., clarifying the fundamental rights and responsibilities for data. IS research has investigated data ownership for operational systems and data warehouses, where the purpose of data processing is known. In the BDA context, defining accountabilities for data is more challenging because data are stored in data lakes and used for previously unknown purposes. Based on four case studies, we identify ownership principles and three distinct types: data, data platform, and data product ownership. Our research answers fundamental questions about how data management changes with BDA and lays the foundation for future research on data and analytics governance….(More)”.