Boosting Competitiveness Through Technology and Innovation

Technology is transforming the way organizations compete.?With its many forms as the internet of things, automation, artificial intelligence, big data analytics etc., technology is revolutionizing organizational growth and competitiveness. It is believed to disrupt current business models and have a huge impact on the five forces that shape competitiveness in organizations.?

Technology is regarded as an intangible asset of a firm which forms the basis of a firm’s competitiveness (Wahab et al. 2012), and a process through which ideas move from the laboratory to the market (Phillips 2002). Technology is the means to gain competitive advantage and the only way to sustain the firm’s competitive advantage (Walsh et al. 1996). The neoclassical production function Q=Af (K,L) explains how technology scales production;?Q is the output, K is the capital, L is labor, and A is technology and as the factor A is in exponential growth so is the total output (Solow 1957). Among the forms of technology that is reshaping organizations and production is Artificial Intelligence powered by big data and machine learning.

AI can be identified as the ability of a computer system to sense, reason, and respond in complex contexts (Hughes et al. 2019). The well-known example of the AI system is AlphaGo, which beat the Korean world champion in “Go” in 2016. Then in 2017, a new version of AlphaGo beat 60 professional Go players along with its first version that beat the world Go champion. Then in October same year came Zero, its latest version, that defeated AlphaGo and its previous version, but what was different about it is that it was not given any input from previous games, but through machine learning it learned and invented new moves (Gentsch 2018).

At the core of AI is machine learning, which uses algorithms; advanced statistical and computing techniques; to analyze big data to detect useful patterns.?Among the many developments in the machine learning is the deep-learning neural networks which is based on multiple layers of neurons that discover complex attribute representations and learn patterns across data. Machine learning has three types. The first is the supervised learning which is used in classification and regression analyses to discover the relationship between input and output data (Gentsch 2018). It deals with labeled data and its algorithms are trained to map the inputs to correspond to known outputs (Kim et al. 2018).?The second is the unsupervised learning which uses unlabeled data and no designed output. It is used to autonomously detect meaningful patterns that are difficult for humans to figure out and to form clusters of data. The third is the reinforcement learning which aims at controlling a system to maximize performance. Reinforcement learning depends upon experimenting, and trial and error (Szepesvári 2010) in which only a slight feedback is given about the predictions.

With the ubiquity of smart devices and their connection through the internet a surge occurred in the amount of data collected. This big amount of data can only be analyzed through big data analytics. It is estimated that the amount of data collected over the 5 millennia until 2003 is close to five exabytes while since 2013 this same amount of data is generated by people and stored on a daily basis (Kelleher and Tierney 2018). Big data is best defined in terms of its 5Vs: ‘Volume’, refers to the huge amount of data generated and stored, ‘Variety’ refers to the many types of data generated and collected, ‘Velocity’ refers to the high speed at which data is generated, ‘Value’ refers to the worth of the data collected, and ‘Veracity’ refers to the good quality of the data and that it is trustworthy in order to lead to accurate and useful results not misleading ones. Big data analytics can analyze the big amount of data in its structured, unstructured and semi-structured forms using its new developed data-processing frameworks that are able to process large data distributed across multiple databases calculating partial results on their server then merge them into a final outcome. An example to that is the MapReduse framework on Hadoop where data is mapped (distributed) across multiple servers then calculated on each server then reduced (merged) together (Kelleher and Tierney 2018). There are three categories of analytics. The first is predictive analytics that gives future predictions along with their reasons through the use of algorithms (Demirkan and Delen 2013).?This type of analytics can predict the customer’s theoretical value and thus helps firms to prepare their revenue budgets and inventory planning (Akter and Wamba 2016). The second is descriptive analytics that is used to discover problems and opportunities within existing processes and functions. The third is prescriptive analytics that helps in providing different decisions in order to improve performance as in the use of simulations (Wang et al. 2016).

Big data analytics can help in dealing with the VUCA age. VUCA describes the characteristics of the current age that it is an age of volatility, uncertainty, complexity and ambiguity. One way to concur uncertainty is through the use of information. Big data can help with multiple scenarios as well as possible responses for each one. For example in a case study predictive analytics was used to give probabilities about students who were likely to leave in the part time undergraduate distance learning. Through identifying those students the university was given a chance to offer them supportive programs to help retain them before they leave (Calvert 2014).?Also, descriptive analytics was used in a study to compare between a current energy system that depends on fossil fuels and a planned system that would depend on other energy resources to predict the probable scenarios and challenges expected during the switch (Mahbub et al. 2016).

Big data analytics is also used in the field of e-commerce and marketing to determine the clients’ Sentiment Analysis (SA) or Opinion Mining (OM). Both terms are sometimes used interchangeably to define the analytical study of people’s opinion towards an object (Jena 2020). Yet, there is a slight difference between both terms; Opinion Mining discovers people’s opinion of an object while Sentiment Analysis identifies the sentiment implicit in texts and then explains whether it is positive, negative or neutral and to what degree (Alsaeedi and Khan 2019). Sentiment Analysis helps improve Customer Relationship Management (CRM). CRM is vital to establish long-term relationship with customers which in return increases organisation profit (Coltman et al. 2011).?SA depends on extracting text features like parts of speech, negation, phrases and opinion words through feature selection methods (Medhat et al. 2014). In a case study sentiment analysis was applied on twitter data to identify people’s opinion about Adidas and Nike and compare their positive and negative attitude towards each brand (Rasool et al. 2019). In another case study, ‘TalkWalker’, an algorithm-based tool, was used to analyze published posts from different social media platforms to discover the main variables that influence student’s choice of universities (Troisi et al. 2018).

However, organizations face challenges when using big data analytics. ?To process big data a high-performance computing (HPC) device is needed.?That requires an expensively built IT infrastructure that is capable of processing the exponential volume of data with its variety and high velocity to give useful insights. ?Another challenge facing many organizations as well is locating and hiring of skilled data scientists.?Data scientists are required to supervise the different stages of the analytics process; they frame the problem, design and prepare the data and select a suitable machine learning algorithm. Moreover, there is shortage in talented managers who are able to understand data analytics and reach decisions using their outcome (Kelleher and Tierney 2018). It is estimated that the United States in 2012 needed around 140,000 people with deep analytical skills and over a million managers who understood the data (Ahmadi et al. 2016). This all leads to the risk of drawing poor quality insights which may mislead the organization’s decision making. ?Another challenge facing companies is the privacy and security issues related to intentional or unintentional breach of personal privacy.?The scattering of data sources in many places makes securing it a challenge and as data is gathered from multiple locations to be processed and analyzed that renders the following of rules and regulations set by different countries in different locations a real challenge.

AI, big data and the rest of technology are believed to create competitiveness among organizations. Competitiveness is a versatile word and it would be more accurate to define it in a certain field.?Yet, competitiveness in a way is linked to welfare and prosperity. It is also seen as the key to countries’ prosperity (Krugman 1996) and the nation’s ability to create welfare (Aiginger 2006).

In industry, competitiveness refers to the capacity of the country’s firms to compete against their foreign competitors (Porter 1998); while enterprise competitiveness refers to the firm’s ability to excel in production, efficiency and improve and obtain competitive advantage while increasing its economic performance (Ahmedova 2015).?According to Michael Porter competitive advantage is a value that a firm creates for its customers and can be achieved either through cost leadership, focus, or differentiation strategies and technology can scale them all (Porter 1985).

Porter notes that from 1960s to the 1990s two technological waves drastically shaped competition and strategy. They gave rise to huge productivity gains and growth.?They changed value chains but products themselves were hardly affected.?Yet, currently the world is witnessing the third technological wave where technology has become an integral part of the product.?The wave is marked by products with embedded sensors, processors, software and connectivity to the cloud where data is collected, stored and analyzed along with applications that automate products performance. Porter notes that the third wave of technology is affecting the five forces that drive competition and determines profitability in any industry: bargaining power of customers, rivalry among competitors, new entrants, substitute products, and bargaining power of suppliers (Porter et al. 2014).

The first force is the bargaining power of buyers. Powerful customers can force down prices, request better quality and services and that makes price goes up (Porter 2008).?Yet, technology is lowering the power of buyers through giving more capability for product differentiation which shifts competition away from price alone. Differentiation is done for example through big data analytics that helps in better customer segmentation, product customization, better pricing and service improvement which leads to closer customer relationship. Moreover, the use of platforms and other technologies mitigated the need for intermediaries and distribution services which lowered prices. Yet, technology also increased buyers power through making them more connected and empowered by the transparency technology offers to compare, try and shift from one product to another. It also brought new competition through powering new circular economy business models like “product-as-a-service’ and product–sharing business models that reduce the cost of switching to new manufacturers as customers only pay for the service when they need it (Porter et al. 2014).

Circular economy business models are considered a key driver of competitive advantage and most of these business models would have been impossible without the support of digital technology (Bocken et al. 2014). Many disruptive technologies that fall under three categories: digital (information technology), engineering (physical technology, and hybrids of two, are used by leading circular economy companies (Figure 1). In the European Union it is estimated that every one percent increase in resources efficiency equals 23 billion euro for business and can create up to two hundred thousand jobs. The circular economy powered by technology creates new value chains that separate growth from using scarce raw materials used in linear economy (Lacy et al. 2014).

Figure 1 (Lacy et al. 2014) Disruptive technologies used to launch circular business models

The second force is rivalry among technology has the power to shift rivalry opening the horizon for new types of product differentiation and value-added services like product customization and tailored offerings to specific customer segments. Technology broadens the product’s value proposition through enhancing the offers brought with it. For example the use of AI and IoT enabled Babolat, a tennis rackets company, to enhance the features of its tennis rackets through implanting sensors in their rackets that are linked to a smartphone application to track the ball speed, location and movements of the player (Porter et al. 2014). Technology not only shifts rivalry, but also causes its expansion as products become part of broader systems. Amazon, for example, expanding its rivalry beyond retail with its new healthcare services in which customers could schedule appointments, receive healthcare services and medicine deliveries in a matter of hours; the application could send reminders for patients to take their medicines or monitor their health at home (Ahmad et al. 2020).

The third force is the threat of new entrants. Technology raises barriers to market entry because of the cost associated to complex product design, adopting technology and building its infrastructure (Porter et al. 2014). It also raises barriers because of the data collected in real-time which helps improve products and services therefore increase customer satisfaction and loyalty. Yet, technology in some cases lower barriers to entering a market when incumbents focus on improving the physical side of a product and not adopting the latest technology which opens the door with stronger technological capabilities to leapfrog them and take part of their market share (Porter et al. 2014).

The fourth force is the threat of substitute products or services. Technology reduces the threat of substitute products as it optimizes production, product customization and growth.?However, in some cases technology creates new types of threats when it broadens the capabilities of products that outperform conventional products (Porter et al. 2014). For example, the new fitness tracker by Amazon, Halo fitness band and application that scans the body and voice to make a 3D model of the body and tracks the emotional tone of the voice. This wrist band substitutes the Fitbit wearable fitness device that was itself a substitute to running watches when it first came in market (Bohn 2020).

Another type of substitution is when a new business model powered by technology substitute the need to product ownership. Product-as-a-service business model discussed before makes the customer only pays for the amount of service needed from the product without the need to own it.?An example for product-as-a-service business model is Michelin, the leading tires manufacturer, which leases tires to customers and charges them per miles driven.?That saves the customers the trouble of tires maintenance and repair (Lacy et al. 2014).

The fifth force affecting competitiveness in industries is the bargaining power of suppliers. As smart technology substitutes physical commodities, the need to be supplied by raw materials to upgrade a commodity is no longer needed as a software update could be all that is needed. That weakens the supplier force. However, technology also opened the door for fierce new suppliers namely the suppliers of industry 4.0 technology: software, sensors and connectivity providers like Google, Apple, AT&T etc. who are important suppliers to the manufacturer’s differentiation and cost leadership strategies.?With their access to user data these suppliers have the bigger share of the manufacturer’s product and profitability (Porter et al. 2014).

Although technology is a stimulus of growth in economies and organizations it is also criticized for being disruptive. Influenced by Schumpeter’s theory of creative destruction, Clayton Christensen coined the term disruptive innovation to explain how a fledgling new market entrant with fewer resources could, with the help of technology, serve segments of customers, who were ignored or overlooked by incumbents, and provide them with good-enough products, until it gains a foothold in market then begins to improve until it wins the incumbent’s mainstream customers and dominates its market share. A disruptive technology does not have to be new from the technological point of view but only may have superior “performance trajectories” (Bower and Christensen 1995), or it could be emerging or radically new in nature (Abernathy and Clark 1985).

It is believed that because most business incumbents tend to focus on sustaining innovation they get disrupted by a disruptive innovation.?A sustaining innovation means that the company focuses on bettering its existing products in the eyes of its customers, like adding a third camera to a smartphone, or Bluetooth to a TV screen. Yet, the term has been used loosely to describe anytime a small entrant shakes the throne of a successful incumbent and that is wrong. There are two factors that characterizes a disruptive technology. First, the disruptive innovation has to begin in a low-end or new-market foothold; and second, that the new entrant wins mainstream customers only when the quality of its products rises to their standards (Christensen et al. 2015).

And because disruption is a process, business incumbents hardly see it coming. Disruption is an evolution of a product from fringe to mainstream. This unexpected process takes time and maybe that is why business incumbents overlook disrupters. Peter Senge (1990) explains that an unexpected process is one of the four organizational surprises. He calls it ‘creeping development’ or the boiling frog syndrome. It is when an organisation is surprised by a change that has been slow that it was unable to detect until it was too late, just like when a frog is put in water and the water boils slowly, the frog is unable to detect the slow change in temperature until it boils to death.?For example when Netflix started in 1997 it was not favored by most Blockbuster’s customers who liked to rent new releases on impulse. Netflix had its own inventory of movies and through its website it delivered movies across the U.S.A. The delivery took days but that did not make a problem to the segments of customers they cared to serve.?The segments Netflix served were different from Blockbuster’s, this is why Blockbuster never felt threatened by Netflix. However, with the new technologies Netflix was able to shift to video streaming and became appealing to the main customers of Blockbuster as it offered a wide selection of on demand content with high quality and low price (Christensen et al. 2015).

Disruption also builds new business models. Disruptors use innovative business models that are different from their competitors. ?When iPhone was released in 2007 it was considered a sustaining innovation in the market of smartphones but to the market of laptops it was disruptive as laptops were the main access point to the internet. This happened because Apple built a new business model.?It built a network that connected application developers with phone users.?The iPhone created a new market for users who wanted to access the internet from any place without needing a laptop (Christensen et al. 2015).

Yet, Christensen’s theory of disruptive innovation is criticized for lacking strong evidence. To prove his theory Christensen wrote many case studies in his book “The Innovator’s Dilemma” which critics describe as ‘weak’.?The cases are seen to be ‘handpicked’ to prove the theory.?Moreover, choosing the disk-drive industry as a case-study suitable to be applied on all industries is considered an odd choice especially that Christensen began his book by stating that there is no business in history like the business of disk drives. Yet, he still used that business as a model for understanding other industries (Lepore 2014).

To conclude, technology is influencing world economies and organizations.?It is a means to creating strong, competitive business models as well as it is a source of disruption. Technological change with its high speed of development marks the VUCA age, but its many forms also help in dealing with the uncertainty through analyzing big data. Through AI, big data analytics, machine learning etc.. technology is building circular economies and impacting the five forces that shape competition in industries

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