One of our Top 2017 MBA report – The impact of technological change on jobs and workplace structures

by Chandon Bezuidenhout

Significant advancement in technologies, such as artificial intelligence, machine-learning, and robotics, has sparked broad debate amongst economists, futurists, and current business leaders regarding the future of jobs. The purpose of this research was to determine the impact of technological change on jobs and workforce structures. The study involved a structured collection, classification, and analysis of secondary data. It aimed to: (i) determine a relationship between futures and labour economics literature, (ii) identify occupational groups with higher susceptibility to job automation, and (iii) project changes in workforce structure for various industries.

This study found that there is alignment between predicted probabilities of job automation and parameters of task routineness and task complexity from the routine-task-biased and complex-task-biased technological change models. Routine-simple occupations are more susceptible to job automation, followed closely by nonroutine-simple occupations. Complex occupations are least susceptible. Stratum I occupations were more susceptible to job automation than occupations in higher strata of work. The projected change in workforce structures is higher for large hierarchical industries, such as machine bureaucracies and divisionalised forms (Type 1 and 2 industries). Technological change will bring about both productivity improvements and technological anxiety. Business in affected industries must develop appropriate innovation and workforce strategies to manage this disruption.

Supervisor: Prof. Albert Wöcke

Click here to access the research report

Avoid these five digital retailing mistakes

By: Prabuddha De, Yu Jeffrey Hu, and Mohammad S. Rahman


In a world where customers are shifting a significant portion of their purchases from off-line to mobile and online channels, the mantra for retailers is to embrace the change and capitalize on the virtues of digital commerce. But rather than haphazardly implementing various website features, retailers should adopt a data-driven view — with the goal of understanding how different types of information that consumers collect via the website affect their behavior.

We researched the effects of web technologies on a retailer’s critical performance metrics such as sales and returns. To study these effects, we needed to measure consumers’ actual web technology usage and match it with their transactions. Toward this end, we partnered with a women’s clothing retailer that has a large online presence and offers the type of web technologies that consumers typically encounter in e-commerce. Overall, we studied 7 million purchases made by approximately 1 million unique customers of this medium-size company over three years, and focused primarily on two months’ worth of data, consisting of 183,000 transactions and 52 million lines of server logs that tracked consumers’ web activities. Detailed findings from our research were published in the academic journals Management Science and Information Systems Research.

Our findings suggest that managers should encourage consumers to embrace innovative technology features like different types of web technologies, personal assistants, and apps, because such usage is generally associated with a higher level of sales. But our research also indicates that it’s critical for retailers to take steps to avoid five common digital retailing mistakes.

Mistake 1: Letting a Consumer Get Lost in a Sea of Products When consumers do generic searches on the web, a retailer should not just present a large set of products to them. Rather, the company should guide the consumer through a process to narrow the search results. This is important because a large set of potential options can confuse consumers and lead them to abandon the purchase process.

Some companies already do this. For example, Nordstrom Inc. has “Nordstrom Style Boards” enabling store salespeople (called stylists) to offer product recommendations to customers via the internet, and J. Crew’s website offers the “Very Personal Stylist,” a service that gives customers a way to connect with a personal shopper 24/7. But for many companies, significant improvements are still needed in this area. In the near future, shopping assistants driven by artificial intelligence (AI) should help deliver those improvements.

Mistake 2: Recommending Only Popular Products Inc.’s recommendation system (for example, “Customers Who Viewed This Item Also Viewed”) is an example of how website features can be used to steer sales. Our research found that a recommendation system can increase sales by more than 5.5%. This is because it lets consumers learn about products in unprecedented ways.

Recommendation systems enhance sales of both popular, well-known products and products that are not so well-known yet. However, our research found that their effect is more prominent for the latter group of products. Popular products typically have a higher sales volume and a lower margin because of competition, whereas less-known products are likely to command a higher margin.

Therefore, retailers should carefully choose a mix of both types of products in their recommendations. Similarly, a retailer should not just promote popular products to consumers who are using the store app. Starbucks Corp.’s Digital Flywheel — which is an AI-driven recommendation engine that goes beyond just simplifying a customer’s favorite order — is, not surprisingly, utilizing consumers’ prior transaction history and other types of digital traces left by them.

Mistake 3: Fostering Unrealistic Customer Expectations While collecting information on a product online, consumers typically gain two types of intel: factual and impression-based. Factual information relates to concrete facts, whereas impression-based information is the perception one forms by looking at a product. For example, when collecting information about a dress, details like what the fabric is made of and how the buttons are sewed offer factual information. On the other hand, the customer’s perception about the dress based on looking at a model wearing it is predominantly impression-based information.

Consumers generally have an expectation about a product before buying it, and their satisfaction with the product depends on how well that expectation matches with their post-purchase experience. Typically, factual information helps a consumer form a realistic prepurchase expectation, which, in turn, leads to a better match between this expectation and the post-purchase experience. In contrast, impression-based information may result in an unrealistic prepurchase expectation in customers’ minds that their post-purchase experience can’t usually match.

Mistake 4: Focusing on Sales Rather Than Net Sales Retail executives are often just keen on increasing sales. High product returns, however, could negate the effect of high sales — after all, returns amount to about $260 billion per year in the United States alone, according to the National Retail Federation. Hence, retailers should focus on net sales (that is, sales minus returns), rather than sales alone.

Consequently, it is important to carefully consider what types of information consumers are gathering when they use technologies made available by retailers. In particular, many retail websites and apps now have product-oriented technologies that are geared toward helping consumers collect information.

While impression-based information may increase sales, it increases returns as well. In fact, our study of the women’s clothing retailer found that the use of alternative photos — a technology that presents images of models wearing the product from different angles, often in an unrealistic scenic environment — primarily provides impression-based information and not only leads to more returns but also decreases net sales.

In contrast, our research found that factual information reduces returns significantly. As a result, the overall effect of adding technology facilitating factual information — such as the ability to zoom in to view a product’s features more closely — is typically positive.

Retailers need to proactively ensure that the technologies on their websites and apps are leading to desired results. For example, one way to mitigate the negative effect of alternative photos is to allow consumers to upload their own photos and videos showing the product in use. Then future potential customers can form more realistic expectations about the product by seeing how other consumers look wearing the product or how they use it. Not surprisingly, a number of major retailers now encourage their consumers to upload photos or videos. These consumer-uploaded pictures arguably balance any unrealistic expectations potential consumers may form by looking at the retailer-provided photos.

Mistake 5: Not Keeping Pace With Technology Advances AI is going to be a critical part of the next wave of technological advancements affecting e-commerce. The increasing use of digital personal assistants such as Siri or Google Now; adoption of smart home devices such as Amazon Echo; and developments like Apple opening up Siri to third-party developers will significantly influence many tasks consumers regularly do, including shopping.

As a result, retailers must ensure that their apps and websites are ready to serve consumers using AI-driven digital assistants. For example, a consumer may ask Siri to find a pair of jeans. A retailer needs to utilize its data about that consumer to present a set to Siri that fits the consumer’s needs. At a time when Siri is, in effect, a platform through which different retailers supply options to be featured in front of the consumer, only the retailers that present the best options are likely be retained, and others would be removed from the consideration set.

Considering a digital assistant’s overarching focus on gaining efficiency, increasingly through machine learning techniques, retailers face a serious threat of being thrown out of the consideration set, which could put them in a downward spiral with obviously grave consequences. After all, these digital assistants are programmed to collect data and constantly improve their services. As a result, it will become evermore important for retailers to take advantage of their data to offer consumers options that are well-targeted to their needs.

5 issues that will shape the future, according to the experts

By: Mark Jones November 12, 2017


Our future’s bright: new technology promises solutions to the world’s biggest problems. But the future’s also frightening: accelerating change is disrupting every aspect of life

Seven hundred experts from the World Economic Forum’s “Future Councils” just met in Dubai to plot a path through these competing forces. Here are some of the key talking points and a small selection of the ideas proposed …

1. The quantity of information is growing at a dizzying speed.

Now’s the time to focus on quality. With more data available than ever before you’d think that the specialists behind these disciplines would be riding high. But many say that those involved in news, information, science – almost anything involving expertise – are having to re-justify themselves.


Proposal: What if we rose to the challenge of “fake news” with a universal standard in media and digital literacy with education on the rights and responsibilities of citizens?


2. Data isn’t enough. It needs to be relatable and actionable.

Big data is enabling the designers of products and services to discover things about human behaviour never spotted before. But might “small data” be an even more powerful agent of change?


Proposal: What if personal health monitors on mobiles lead to behavioural changes in diet and activity that all our research and education have so far failed to achieve?


3. Blockchain could manage everything

Confused about blockchain? Just think of it as a smart kind of database that can track anything. That makes it super-useful for previously intractable global problems.


Proposal: What if blockchain can help us feed a more populous world by conquering the fear of genetically modified crops and lab-grown food?


4. Look at the big picture before you decide what problems need solving

The Forum has just made public its “Transformation Maps” to underline the close links between our biggest global challenges. Zooming out to look at the world in terms of “systems” – how things are linked rather than how they are separated – frees specialists of all kinds to look at challenges afresh. Here’s Robert Muggah talking about how cities are the vital link in the chain when it comes to finding global solutions.

Proposal: What if instead of building more and better types of roads with new technology, we invested in collaborative platforms such as Uber to use current roads much more intensively?

5. It all comes back to trust

Experts tend to be optimists. Yet there’s a clear understanding that the wider public has deep anxieties about the pace of change. If you are worried about losing your job, having to re-skill, or not being able to keep up with the pace of change, it’s easy to start to feel the general system is not acting in your favour and that breeds mistrust. Society is struggling to adapt as fast as technology is moving, leading to suggestions we need to slow the pace of change through regulation, or spread its benefits via things like taxes on robots.

Proposal: What if we can’t adapt as fast as technology and we have to find ways of slowing the pace of change like taxes on robots or other forms of regulation?

AI Gains Ground With IT and Business Leaders

By: Samuel Greengard September 27, 2017


The opportunities to put AI to work include high-level strategic decision-making, customer service, product development, marketing, cyber-security and much more.

Operating smarter and cost-effectively is the goal of every organization. But gaining deep insights into activities, events and processes is often a daunting task—particularly as the data deluge grows and making real-time decisions becomes a critical requirement. As a result, business and IT leaders across a wide swath of industries are increasingly tapping artificial intelligence (AI) to take insight and performance to an entirely new level.

A 2017 Economist Intelligence Unit report, “Artificial Intelligence in the Real World,” noted that 75 percent of business executives surveyed believe AI will be actively used in their company within the next three years.

“The technology provides answers to important questions, and it delivers transformative capabilities for organizations,” observes Nicola Morini Bianzino, global lead of AI for Accenture. “It helps organizations meet objectives and move to new digital business models.”

Over the past couple of years, AI technology—including cognitive computing, deep learning and other components of machine learning—has taken a giant leap forward. Far more sophisticated features and capabilities have emerged, and the ecosystems of business and IT platforms using AI have expanded dramatically.

“We have gotten to the point where if you don’t move forward with AI—and move fast—you’re at the risk of being left behind,” Bianzino warns.

“AI helps organizations take action quicker and in a more agile manner,” adds Victor Thu, global head of product marketing at Digitate, an AI division of consulting firm TCS.

Diving Into Disruption

The opportunities to put AI to work aren’t lost on today’s business and IT leaders. The technology has ramifications for high-level strategic decision-making, customer service, product development, marketing, cyber-security and much more. Market research firm IDC found that 40 percent of all digital transformation initiatives—and 100 percent of all effective internet of things (IoT) efforts—will plug in cognitive or AI capabilities by 2019.

Yet, it’s tempting for business and IT leaders to think of AI in somewhat monolithic terms. The reality, however, is that it’s a vast array of technologies and solutions that infuse software and systems with greater smarts.

The field spans cognitive computing (an IBM-created term that refers to technology that generally addresses human problems), deep learning (which taps complex artificial neural nets to spot correlations and other patterns), and machine learning (which allows computers to learn and adapt algorithms without human intervention and explicit programming). These fields increasingly overlap and intertwine.

Accenture’s Bianzino says that discussions about AI are rapidly moving away from specific topics such as computer vision, natural-language processing and specific machine learning algorithms—all important and valuable pieces of the puzzle—to a more holistic, nuanced and multidimensional view of the space.

“The focus is on the engagement of a strategy to transform processes and create greater overall value,” he explains. Moving forward, “There’s less of a technology specific view—such as what Alexa can do or how an image processing chip can deliver benefits—than thinking about how to transform a process or solve a major business challenge.”

There are also opportunities to take IT systems and cyber-security to a more advanced level through artificial intelligence. Digitate’s Thu says that the technology can help organizations manage internal IT resources more effectively, pinpoint system problems more quickly, and reduce the risk of breakdowns or outages that could cripple the enterprise.