E-Business Dynamics and Ethical Challenges in Technology Management

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E-Business Dynamics and Ethical Challenges in Technology Management

Business Essentials · Chapter 18: Technology and Ethics in BusinessSection 18.3 · lesson 83 of 10514 min · free with a free account

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In this lesson

How digital businesses earn, and the ethical questions their technology raises.

  • Distinguish e-business from e-commerce
  • Describe e-business and revenue models
  • Identify the ethical questions technology raises
  • Apply governance questions to a data decision

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Everything said in the video, section by section (2,048 words). Read along, or search it.

Technology and Ethics in Business

E-Business Dynamics and Ethical Challenges

Business now runs on digital channels, and that raises two kinds of question. The first is commercial. How do e-businesses make money, and what has to be true for their models to work? The second is ethical, and it's the real substance of this section. We'll distinguish e-commerce from e-business, and lay out models by who transacts with whom. We'll set six revenue models against what each requires, and the risk each carries. Then the ethics: data use and consent, algorithmic bias, personalization, monitoring, security, and intellectual property. We'll close with the governance a business needs to approve, review, and challenge its own data uses. And a fitness app weighing an offer, one that would use its users' data in a way they never expected.

2 Terms

E-Commerce Is Part of E-Business

Two terms get used interchangeably, and they shouldn't be. E-commerce is transacting electronically: buying and selling through digital means. E-business is broader. It's the conduct of business processes electronically. Commerce is one part of that. Others include internal coordination, supplier management, and customer service. So the two can come apart. A business can run its operations and supplier relationships digitally, without every transaction being e-commerce. And a business can sell online, without being a fully digital e-business in how it runs itself. Why does the distinction matter? Talk only about online selling, and you miss the wider digital transformation, in how a business is actually run.

Models by Participant

Who Transacts With Whom

E-business models are classified by who is transacting with whom. A business-to-consumer model sells directly to individual customers. A business-to-business model sells to other businesses, often in larger volumes, with more complex, negotiated terms. A consumer-to-consumer model lets individuals transact with one another, typically through a platform that connects them. And a platform model intermediates between two or more groups, like buyers and sellers, or riders and drivers. Crucially, the platform doesn't own what changes hands. Platforms have distinctive economics, built on the network effect. A network effect means a product becomes more valuable to each user as more people use it. More buyers make a platform more attractive to sellers, because that means more potential sales. More sellers make it more attractive to buyers, because that means more choice, and more competitive pricing. That mutual reinforcement makes an early leader very hard to displace. A latecomer has neither the buyers nor the sellers. It also makes the earliest period the hardest, before either side gets much benefit from joining.

Revenue Models

How the Business Gets Paid

Separate from who transacts with whom is how the business gets paid. Six revenue models are common. A sale model charges a price at the point of purchase. It works only if the good costs less than that price to produce or source. A subscription charges a recurring fee for ongoing access. It needs enough ongoing value to keep customers renewing. An advertising-supported model offers the product free, or cheap, and sells access to users' attention to advertisers. It needs a large audience, because revenue per user is typically small. A transaction fee model earns a percentage or flat fee on transactions between other parties. It needs real volume. A freemium model offers a basic version free, and charges for a premium version. It works only if enough free users convert to paying ones, to cover the cost of serving everyone. And a licensing model charges other businesses for the right to use intellectual property, technology, or a brand. What's licensed must be valuable enough, and protected enough, that others pay rather than replicate it. One caution. A revenue model on paper is the easy part. Any model involving physical goods depends on fulfillment: warehousing, packing, shipping, and returns, working reliably at volume. Design an elegant model without planning how to deliver a product, or take one back, and you've designed half a business.

Table 18.3.1

Relationship and Risk, Model by Model

Each revenue model also creates a different relationship with the customer, and carries its own principal risk. A sale is transactional, renewed only if the customer buys again. The risk is margin pressure from rivals selling something similar. A subscription is continuing, and needs active retention. The risk is cancellation once perceived value falls below the fee. Advertising creates an indirect relationship. The paying party is the advertiser, not the user. So the risk is dependence on advertiser demand, and on the size of the audience. A transaction fee is facilitative, between two other parties. The risk is those parties dealing directly, to bypass the fee. Freemium is layered: most users free, a minority paying for more. The risk is free users costing more than paying users cover. Licensing is business-to-business, often long-term and contractual. The risk is the property's value or protection eroding over time. So choosing a revenue model means choosing what has to be true, about customers and volume, for the business to work. A business should be explicit about which of these assumptions it's making. That turns a hope into something that can be checked.

Data Ethics

Lawful Isn't the Same as Expected

The rest of this section turns to the ethical questions technology raises for business. They're the substance here. Start with data ethics, and one central distinction. There's what a business may lawfully do with data it holds. And there's what the people the data describes would reasonably expect. Those are different questions. A use can be entirely lawful, and still surprise or trouble the people it concerns. That happens when it departs from what they understood they were agreeing to, when the data was first collected. Primary use is use consistent with the purpose of collection, like using a shipping address to deliver the product ordered. Secondary use is use for a different purpose. Using purchase history to build a marketing profile is secondary. So is using it to sell an offering to another party. The further a use drifts from the original purpose, the more it needs justifying.

Figure 18.3.1

Consent Weakens Along the Data Flow

Consent sits at the center of data ethics, and it has limits. A person can meaningfully consent to a use that's disclosed, and clearly explained, at the time. Consent becomes strained when a use wasn't contemplated when the data was collected. Take a customer who shared an address for delivery. They didn't necessarily agree to it being used for something they never considered. That's true even if a broadly worded policy technically covers it. They might well have objected. This figure follows the data from one online purchase. The consent basis is clearest at collection and primary use. It weakens at secondary use, and is weakest at third-party use. So the governance checkpoint has to become more demanding at each step. Apply the same light scrutiny everywhere, and the transfers furthest from what the customer agreed to get far too little. De-identification, removing what would identify a specific person, is sometimes offered as the fix. But it isn't absolute. Data that looks anonymous can sometimes be linked back to an individual, when combined with other information. So it's never a guarantee that no further ethical or legal obligation applies.

Algorithmic Decisions

Accurate Isn't the Same as Fair

Businesses increasingly use algorithms to screen applications, price offers, or recommend actions. Here, an algorithm is a defined procedure a system follows to reach a decision or a recommendation. Bias can enter such a system in at least two ways. The first is through the training data, the historical examples a system learns from. If that history reflects past unequal treatment of some group, the system can reproduce the pattern, with nobody intending it. The second is through the definition of the outcome being predicted. Suppose a system predicts who will default on a loan, using a measure that itself reflects unequal past access to credit. It can allocate unfairly, while performing exactly as its designers intended, on the measure they chose. That's why a technically well-performing model can still allocate outcomes unfairly. Accuracy against a flawed or biased target is not the same as fairness in the real world. Two further questions follow. The first is transparency and explainability. How clearly can, and should, a business explain a system's decision, to the person affected and to those overseeing it? A system whose reasoning can't be explained is hard to check for bias. The second is accountability. When a recommendation proves wrong or unfair, who answers: the builders, the deployers, or the organization? This course doesn't settle that allocation. It's a governance choice each business makes.

Personalization and Monitoring

Where Tailoring Becomes Exploitation

Targeting and personalization use data about a person to tailor what's offered to them. A boundary runs through that practice. On one side is informing a decision, offering someone something genuinely relevant to their situation. On the other is exploiting a known limitation. That means using detailed knowledge of a person's circumstances, like a vulnerability or a moment of stress, to press an offer. It's an offer they'd be less likely to accept with a clearer head. The same targeting capability works either way. The difference lies in what's being used, relevant interest or exploitable weakness. Employee monitoring came up in the first section of this chapter, and both positions still stand at full strength. It can improve safety, fairness, and accountability. It can also raise stress, and narrow the discretion that makes work bearable. A governance approach asks three things. Do employees have clear notice of what's monitored, and why? Is the monitoring proportionate to a legitimate purpose, and no broader? And is there a stated policy on how monitoring data is used, and who may access it?

Obligations and Open Questions

Security Owed, Ownership Unsettled

Earlier sections treated security as a governance obligation, and as a business risk. It's also, directly, an obligation to people. A customer or employee who shares personal information does so on an implicit understanding: the business will take reasonable care of it. A business that fails at that hasn't only exposed itself to risk. It has failed a duty it owed to people who trusted it. Intellectual property and attribution raise questions that are still live. They're stated here as questions, not as a legal position, because the law keeps developing. A business should seek current legal advice. What does it mean to own, or have rights in, content that can be copied and distributed at effectively no cost? What attribution is owed when content builds on, or incorporates, the work of others? And how should a business treat content it didn't create, but that's readily available to it? This section names those questions, without resolving them.

Governance Architecture

Approve, Review, Challenge

Everything in this section points to one requirement: a defined governance architecture for data and algorithmic systems. It has to answer three questions. Who approves a new use of data, particularly a secondary or third-party use, like the transfers in the data flow? Who reviews an algorithmic system, before and during its use, to check for the kinds of bias we've described? And how can someone affected by a data use, or an algorithmic decision, challenge it? That has to be through an accessible process, not merely a theoretical right. A business without clear answers to all three hasn't built the governance its data and algorithms require. That holds whatever technical safeguards it may separately have in place. Technical protection and governance are different things, and one can't stand in for the other.

Case Vignette

The Offer That Used the Wrong Data

Here's a composite case, built for instruction. Priya manages partnerships at a regional fitness app. It collects users' workout schedules and locations, to provide its core service. A wellness insurance partner makes an offer. It will pay a fee for access to users' location and activity patterns. The partner would use that data to adjust the premiums it offers those same users. Before taking it to leadership, Priya works through the governance questions. What was the consent basis? Users shared data for fitness tracking and coaching. Sign-up said nothing about insurance partners. Is this primary or secondary use? Clearly secondary, and one a typical user likely wouldn't have anticipated. Has this transfer been reviewed and separately approved? No. No review process for this kind of sharing exists at the company. The trade-off is a new, substantial revenue stream, against a use far outside what users agreed to. And the existing terms of service may be broad enough to make the deal technically lawful. Priya's reasoning is that lawful and reasonably expected are separate questions. And the absence of a governance process for exactly this decision is itself the finding to report. That comes first, before anyone decides on the specific offer.

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FAQ

Questions students ask us

About E-Business Dynamics and Ethical Challenges in Technology Management

What is the difference between e-commerce and e-business?
E-commerce is buying and selling through digital means, while e-business is the broader conduct of business processes electronically, including internal coordination, supplier management, and customer service as well as commerce. The two can come apart: a company can run its operations and supplier relationships digitally without every transaction being e-commerce, or sell online without being a fully digital business in how it runs itself.
What is the network effect in platform business models?
A network effect occurs when a product becomes more valuable to each user as more people use it. On a platform that connects two groups, more buyers attract more sellers by offering more potential sales, and more sellers attract more buyers by offering more choice and competitive prices. That reinforcement makes an early leader hard to displace, but it also makes a platform's earliest period the hardest, before either side gains much from joining.
What is the difference between primary and secondary use of customer data?
Primary use of data is consistent with the purpose for which it was collected, while secondary use applies it to a different purpose. Using a shipping address to deliver an order is primary; using purchase history to build a marketing profile, or selling it to another party, is secondary. A secondary use can be lawful yet still surprise customers, so the further a use drifts from the original purpose, the more justification and review it needs.
How can an accurate algorithm still be biased?
An algorithm can be accurate against its chosen target yet still allocate outcomes unfairly. Bias can enter through training data that reflects past unequal treatment, which the system then reproduces without anyone intending it. It can also enter through the outcome being predicted: a loan-default model built on a measure shaped by unequal past access to credit may perform exactly as designed while treating groups unfairly. Explainability and clear accountability help catch such problems.

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