Social media impacts and emerging trends

How social technology is changing how businesses operate

Seventy-two percent of companies are using at least one social technology (Bughin, Byers, & Chui, 2011). There have been observed changes in how the business operates.  Previously the role of human resource (HR) is to allay legal risks, but they are also now responsible for fostering a supportive corporate culture, and social technologies are facilitating this (Lauby, 2010).  Social technology can include social networking, video sharing, blogs, microblogging, etc. (Bughin et al., 2011).

However, the power of a negative tweet (a social networking platform) can severely impact a company.  This was the case when then President-elect Trump criticized both Boeing and Lockheed Martin, sending their stocks to plummet within minutes from that tweet (Kilgore, 2016; Lauby, 2010; Li, 2010).  Thus, mitigation of negative sentiment is becoming more prevalent for how a business that is operating in a world with social technology. Bughin et al. (2011), reported that social technology for customer purposes had increased effective marketing, customer satisfaction, and increased marketing cost savings.

Other companies like the H&M retail, had created an H&M alumni group, which allows them to track their previous talent for future rehiring, but also maintain an eye on keeping proprietary data proprietary (Lauby, 2010).   Even though social technology makes it more difficult to keep proprietary data proprietary, it has helped companies to search social technology to generate new ideas, pivoting in projects, etc. (Bughin et al. 2011).

Additionally, Bughin et al. (2011), reported that social technology internally had been used to increase knowledge access, reduce communication costs, increase network connect to internal subject matter experts.  This can help improve employee engagement with the business. Calvin College uses their Facebook page to showcase their employee involvement per their social business strategy, and this gives them the reputation of being more engaged with their employees (Lauby, 2010).

Emerging social technology trends

  • Li (2010), stated that curating consists of spending hours in ensuring well-organized content for their customers/employees as well as participating in discussions with them. Thus, a trend moving forward is to have fewer and improved, but well-curated posts (DeMers, 2016). Taking a page from minimalistic living is consumers of social technology like to be seeing less content from a content provider, but are also starting to expect that the quality of that content should be higher than their corporate competitors. When companies begin to curate high-quality content, they will set themselves apart because they are developing a social brand that is engaging (Li, 2010).
  • Social media platform dynamics are increasing, where Facebook, Twitter, and LinkedIn had similar tools, goals, and applications but serving small differences in niches; but Instagram, Snapchat, Vine are starting to serve a new niche (DeMers, 2016). Thus, this is where a social business strategy is known as a living document, to meet the demands of an ever-changing field of social media (Cohen, 2011).  This strategy should be growing and adjusting to consider the benefits of new platforms and analyzing the current business strategy to maximize these new platforms (Wollan, Smith, & Zhou, 2010).  Each of these niches also means different ways of engaging/curating materials and reaching to potential new employees, new customers, or retain talent (Wollan et al., 2010).


Business Intelligence: Decision Support Systems

Many years ago a measure of Business Intelligence (BI) systems was on how big the data warehouse was (McNurlin, Sprague,& Bui, 2008).   This measure made no sense, as it’s not all about the quantity of the data but the quality of the data.  A lot of bad data in the warehouse means that it will provide a lot of bad data-driven decisions. Both BI and Decision Support Systems (DSS) help provide data to support data-driven decisions.  However, McNurlin et al. (2008) state that a DSS is one of five principles of BI, along with data mining, executive information systems, expert systems, and agent-based modeling.

  • A BI strategies can include, but is not limited to data extraction, data processing, data mining, data analysis, reporting, dashboards, performance management, actionable decisions, etc. (Fayyad, Piatetsky-Shapiro, & Smyth, 1996; Padhy, Mishra, & Panigrahi, 2012; and McNurlin et al., 2008). This definition along with the fact the DSS is 1/5 principles to BI suggest that DSS was created before BI and that BI is a more new and holistic view of data-driven decision making.
  • A DSS helps execute the project, expand the strategy, improve processes, and improves quality controls in a quickly and timely fashion. Data warehouses’ main role is to support the DSS (Carter, Farmer, & Siegel, 2014).  The three components of a DSS are Data Component (comprising of databases, or data warehouse), Model Component (comprising of a Model base) and a dialog component (Software System, which a user can interact with the DSS) (McNurlin et al., 2008).

McNurlin et al (2008) state a case study, where Ore-Ida Foods, Inc. had a marketing DSS to support its data-driven decisions by looking at the: data retrieved (internal data and external market data), market analysis (was 70% of the use of their DSS, where data was combined, and relationships were discovered), and modeling (which is frequently updated).  The modeling offered great insight for the marketing management.  McNurlin et al. (2008), emphasizes that DSS tend to be defined, but heavily rely on internal data with little or some external data and that vibrational testing on the model/data is rarely done.

The incorporation of internal and external data into the data warehouse helps both BI strategies and DSS.  However, the one thing that BI strategies provide that DSS doesn’t is “What is the right data that should be collected and presented?” DSS are more of the how component, whereas BI systems generate the why, what, and how, because of their constant feedback loop back into the business and the decision makers.  This was seen in a hospital case study and was one of the main key reasons why it succeeded (Topaloglou & Barone, 2015).  As illustrated in the hospital case study, all the data types were consolidated to a unifying definition and type and had a defined roles and responsibilities assigned to it.  Each data entered into the data warehouse had a particular reason, and that was defined through interviews will all different levels of the hospital, which ranged from the business level to the process level, etc.

BI strategies can affect supply chain management in the manufacturing setting.  The 787-8, 787-9, and 787-10 Boeing Dreamliners have outsourced ~30% of its parts and components or more, this approach to outsourcing this much of a product mix is new since the current Boeing 747 is only ~5% outsourced (Yeoh, & Popovič, 2016).  As more and more companies increase their outsourcing percentages for their product mix, the more crucial it is to capture data on fault tolerances on each of those outsourced parts.  Other things that BI data could be used is to make decisions on which supplier to keep or not keep.  Companies as huge as Boeing can have multiple suppliers for the same part, if in their inventory analysis they find an unusually larger than average variance in the performance of an item: (1) they can either negotiate a lower price to overcompensate a larger than average variance, or (2) they could all together give the company a notice that if they don’t lower that variance for that part they will terminate their contract.  Same things can apply with the auto manufacturing plants or steel mills, etc.