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Archive for the 'Intelligent Communication Technology' Category

Preparing students for contemporary workplace practices has been a hallmark of Bovée and Thill textbooks from the beginning, and this emphasis often involves technology. Thirty years ago, our tech coverage involved reminding students to use the high-resolution setting when faxing a résumé. Today, it involves helping them get ready for résumé-reading bots and automated video interviews that are evaluated with artificial intelligence—and these are just two of the many communication technologies that graduates can expect to encounter.

Teaching communication technology presents some intriguing challenges, such as deciding when a particular technology has gone mainstream enough to warrant coverage in the course. As we’ve been researching the latest wave of smart communication tools, we realized that this presents a great opportunity to discuss the conundrum of business innovation with your students.

Change in Business—From Gradual to Disruptive

Some changes in the business environment happen gradually and often predictably, such as when an aging consumer population increases or decreases demand for particular goods and services. Companies need to anticipate and respond to such changes, but they don’t fundamentally alter the way businesses operate. Similarly, individual brands and products move in and out of fashion, but the overall market sector often remains more or less the same.

Other types of changes, however, can be downright traumatic—or exciting, depending on whether you’re benefiting from a change or getting steamrolled by it. Online retailing, digital music, mobile communication, and social media are examples of changes that permanently shifted the way many consumers behave and many businesses operate. Each of these is a disruptive innovation, a development so fundamentally different and far reaching that it can create new professions, companies, or even entire industries while damaging or destroying others.

Disruptive innovation is an important phenomenon that all business students should understand, and it presents some intriguing questions that you might want to discuss with your students, particularly as they relate to business communication.

Three Questions to Discuss with Students

First, predicting whether a new technology will be truly disruptive is difficult. In many cases, multiple other forces from the technological, economic, social, and legal regulatory environments need to converge before an innovation has a major impact. For instance, without broadband wireless networks, a digital communication infrastructure, data encryption methods, a vast array of free and low-cost apps, mobile-friendly web services, and more computing power than actual computers used to have, a smartphone would just be an expensive way to make phone calls. With the combined impact of all these innovations, mobile phones have changed the way many people live and the way many businesses operate. Encourage students to keep this in mind if they’re considering joining a company with a promising new product that hasn’t caught on yet—what other changes need to occur before the product and the company will succeed?

Second, predicting when the disruption will happen is just as difficult. Many promising technologies can take years to have an impact. Mobile phones and handheld computers had been around for two or three decades before all the pieces fell into place and the smartphone era took off. Intriguing new inventions can generate a lot of interest, press coverage, and “hype” long before they have any real impact on business, and expectations sometimes outpace what the technology can deliver. This pattern repeats so often that the management consulting firm Gartner famously modeled it as a five-stage roller-coaster curve that it calls the Hype Cycle.

Third, predicting the eventual impact of a disruption is also challenging. Artificial intelligence (AI) is finally going mainstream as a business—and business communication—tool after many decades of hopes and hype, but its long-term impact is difficult to gauge at this point. Millions of jobs involve tasks and decisions that AI could conceivably do (and is now doing in many cases), but it’s impossible to pin down how disruptive it will be to the job market. AI will redefine many jobs, eliminate some, and create some—and people in most professions should be prepared to learn new skills and adapt as opportunities and expectations change.

The best advice for students as they move forward in their careers is to keep their eyes and ears open to innovations that could affect their professions and their companies. Encourage them to carefully consider the predictions they hear, but before they make any major career decisions, ask themselves what will have to happen for those predictions to come true. Predicting the future is always a dicey proposition, but with a skeptical approach, they have a better chance of separating reasonable projections from pie-in-the-sky wishful thinking.


Adapted from Courtland L. Bovée and John V. Thill, Business in Action, 9th ed. (New York: Pearson 2020), 22.

You’ve probably experienced both these frustrations with search engines: You’re not quite sure which terms to use, so you poke around hoping you’ll find something relevant, or you get lots of irrelevant results that happen to include your search terms but have nothing to do with what you are looking for.

Text mining, also known as text analytics, promises the ability to find meaning and patterns in mountains of textual material by going far beyond conventional search capabilities. Unlike simple word and phrase searches that require exact or near-exact matches, text mining systems can find relevant material even if you don’t know the specific terminology the sources use, or if they use different words to express the same concepts. By applying linguistic principles through natural language processing, text mining systems can recognize meaning in context. This capability also helps text mining tools filter out irrelevant material that uses the same terms, such as excluding material about biological reproduction if you are searching for material about document or file reproduction.

Another major benefit of text mining is the ability to copy all the searched material and reorganize it into consistent records, even if it came from a variety of sources in different formats. For example, a system could be instructed to pull in social media posts, emails, and text messages and “clean” and merge them into a single data set for easier analysis.

Text mining is a potential solution whenever a business needs to analyze hundreds, thousands, or even millions of text records. Examples of current applications include product research and development (such as searching patent records for similar designs), sentiment analysis (finding trends of satisfaction or dissatisfaction in public tweets, customer emails, and other sources), competitive intelligence (finding out what competitors are up to by analyzing their document and social media output), and risk management (such as analyzing financial news and reports in search of potential risks).

Class activity ideas

  1. Natural language processing applies the same linguistic rules and concepts that humans use to encode and decode language. Ask students if they think computers will ever be able to understand text the way that humans can. Why or why not?
  2. How do students feel about their public social media posts being available for companies and other organizations to analyze?


Sources: “About Text Mining,” IBM Knowledge Center, accessed 7 April 2018,; “What Is NLP Text Mining?” Linguamatics, accessed 7 April 2018,; Text Mining Applications: 10 Examples Today,” Expert System, 18 April 2016,