The Role of Technology in Creating Organizational Competitiveness

Introduction

Technology is widely regarded as a stimulus for the development of business entities. According to Schwab (2009), competitiveness can be defined as the ability of a given company to offer products and services that are effective and efficient than the key players, competitors. This paper appraises the significance of technology and how its innovation has helped to change business models. Technologies of big data analysis, artificial intelligence, robotics, Internet of Things (IoT) and blockchain technology are discussed with emphasis put on “How technology can disrupt Business models” and “Relevant case study examples”. The key theories discussed during the module is done separately for all the technologies to understand how it relates to them. Thereafter, a critical reflection over what I have studied in this model is done. How have I understood what was taught? How best can I use innovation and the spirit of competitiveness in technology in an establishment I oversee? These are some of the important questions guiding how the reflective section is tackled, with support from business dissertation help where necessary.

IoT

IoT has revolutionized this new era. An emphasis on Industrial Internet of things shows how there has been a shift machine-to-machine model where interconnected devices and the concept of IoT allows express access of any machine by use of another in the same network. Despite the difference is Machine-to-Machine technology and IoT, the former forms the founding principles of development of the latter. IoT has contributed greatly to the development of M2M and it is projected that by 2020, more than 30 billion devices will be interconnected thereby further affecting business models as constituted (Shafia et al., 2016). Safety and efficiency have been improved considering the safety of business data and other assets. Interconnection of cameras and sensors has improved surveillance. Safety has been improved through continuous real-time tracking and prompts subsequent mitigating actions to safeguard processes or reduce loss incurred (Carayannis & Wang, 2012). In addition to tracking, adoption of both enhanced real-time and predictive analyses helped economize on the cost of maintenance. Data obtained by sensors are processed in algorithms to probe the integrity of systems and establish efficiency whilst knowing whether or not to adjust operating conditions. Solutions which have been adopted have necessitated the hiring of appropriately skilled personnel or train the current employees. Despite being costly to train personnel, the exposure goes a long way in greater productivity in the industry (Maier et al., 2014). The streaming data feed on sensors has been helpful in enabling decision making bodies make both more immediate and informed decision. Ability to skip lengthy and expensive research reports to help with time-sensitive decisions have been made possible with Business Intelligence IoT solutions. It depends on establishing patterns and relaying these trends with the help of a friendly graphic user interface (GUI). Tracking and management of Inventory have been made easier by IoT. Connection of devices has helped with logging transactions of goods and services produced, transported and stored instantly (Mok, 2015). Information from production, distribution and consumption is recorded in a system of Smart inventory management. Operation of this database is considerably cheap and less prone to errors. Closely related is better management of interconnected machines in the IoT. As such, it has significantly transformed the business model and in the process availed a variety of smart tools which can be used to find the best operation strategy for any kind of establishment (Dunning, 2013). A case study of the German automaker, Daimler, which is driven by the goal of using IoT to improve qualities of vehicles produced and roads used “accident-free”. Through IoT innovations, the truck maker is concentrating on designing driverless or aided driving experience that depends on the driver’s comfort. Innovations that have already been implemented include emergency brake assist, lane-adherence assist, proximity regulator, 3-D maps and stop-and-go assist. They ensure trucks keep to their lane; maintain a safe distance from other vehicle and use of emergency brake when necessary. These innovations heavily depend on data feed from cameras and sensors that report on the road and traffic environment (Boycheva, 2017). Such innovations improve driver fitness hence reduce their sleepiness to about 75%. Constant communication with other trucks improves traffic flow and reduces fuel consumption by taking note of the road structure.

Robotics

The impact of the imminent danger posed by robots on job security has prompted Bill Gates to propose taxation of robots that replace human labour. Adoption of industrial robots can be viewed as an attempt by industry policymakers to tackle competition so as to stay afloat. Competitiveness is achieved by adopting technological robotics advancement (Fu et al., 2011). This has resulted in adoption at a relatively fast rate when pitting it against other innovations. For instance, a rise in minimum wage costs in some industries in China has resulted in an increase in the cost of production and response of one of the fastest robots use in the world is seen. Robots have a direct competition against humans in creativity, effectiveness and economy. Humans have a slow predictable rate of acquiring new skill because it is linear. Robots, on the other hand, have a pace described as exponential (Schwab, 2009). This technical advantage they have over humans is due to the technical advancement of constitutive elements and other concepts of machine learning and deep learning to advance the quality of their codes. An example is 3D printing which has revolutionized fields of engineering and surgical medicine through a blend of mechanical concepts and other related digital technologies. The more robotics applications and tools are developed, the greater the optics and possibilities of creating more tools following slow step of first using innovated tools in existence before later shifting to more personalized and advanced ones created through projects (Aleksejeva, 2016). Effects of robotics are projected to be felt faster because these tools are designed to learn quickly. Their pace of improvement cannot be matched by humans whose concerns are justified. A comparison in demand between service market and the industrial market shows how much global service industry has increased its competitiveness. Personal robots have been known to help people overcome their personal weaknesses and robotics developers have taken note of this. They are largely referred to as the service industry (Hansen et al., 2013). Robots have been adopted to deploy surveillance crafts other auxiliary services which may be considered unsafe in the military because of their expendability and cheap cost of acquisition and development. Their accuracy has made them be used in remote surgeries (Rasheed & Sagagi, 2015). A case study of safe Human-Robot Interaction in Medical Robotics understands that constitutive elements, system and robot-surgeon interaction are important for best surgical practice. Use of these robots is not a guarantee of absolute safety during surgeries and can actually cause death when not monitored (Oh & Philips, 2013). Through 13 years, database of Da Vinci system report about 4798 of accidents which analysis showed that were mainly caused by little safety and lack of warning parameters to help surgeons with understanding or even incompetency of surgeons. Medical Robots are designed with several complexities to try and understand how best to handle the human body during surgery. Surgeons and clinical staff are faced with the task of being conversant with them so that when coupled with seamless human-robot interaction, surgical safety can be guaranteed (Zhang & London, 2013). This example shows how the ability of robots to be designed and trained can be adopted to simplify and accomplish complex tasks.

Blockchain

Primarily, this technology is used to develop virtual currencies. Blockchain technology which is used to do this has other alternative uses such as actualizing transactions that need authentication (Bryson & Rusten, 2010). Authentication may be needed in forgeries involving sensitive material. Here, three blockchains are mentioned. Digital currencies are termed blockchain 1.0, digital finance blockchain 2.0 and blockchain 3.0 refers to a digital society. Value of the most common blockchain, bitcoin, rose steadily from $1 at the beginning of 2011 to about a range of $800- $1070 in early 2017 (Amoros et al., 2012). They will be adopted by more entrepreneurs in future due to their promising growth. Blockchains are used to raise investment capital by companies using initial coin offering but are not regulated by any financial authority in any country. They are speculative investments thus. This aside, there has been reporting of some 10 stock exchange in some countries working to include them in their trades. They include Deutsche Borse and NYSE. Blockchains’ main contributor in its success is trust found within the system as maintains the security of transactions. They, however, have little help when it comes to the idea of the central operation and trust from people who are not conversant with it. Blockchains have helped increase the effectiveness of services and smooth the progress of compliance of regulations (Neuhofer et al., 2012). Benefits have been seen through cross-border transactions. Ripple successfully developed an application to help with interbank transactions with the help of blockchain technology in partnership with about 100 banks. The promise of real-time payments with this solution will phase out the SWIFT system and other correspondent banking. Execution of smart contracts will cause major effects on the market. They are contracts which after being defined, will be actualized once a set of set ultimatums are achieved. They are relevant in finance where the need for speedy transactions and high occurrence of fraud is seen. Despite being contracts, their legality is not expressly enforceable. Other important mentions which do not fall within finance are computing and carpooling as sharing service. A case study of IBM Blockchain World Wire. IBM has launched this platform to help with instant real-time clearing and settling of transactions. Also referred to as a financial rail, this product helps with avoidance difficulties associated with cross-border transactions (Block & Keller, 2015). In contrast with other methods which involve multi-institutional dependency as intermediaries for the transaction, this alternative is cheaper, faster and simpler. The Wire operates by attachment of digital assets with equivalent actual value in the real world so that these assets are used to complete transactions. They act as a store of wealth to be transferred to another party when some given services have been rendered. An integration of weight put on such digital assets and guiding steps using messages into one system enables IBM Blockchain World Wire to offer transaction services at an economical price and time (Zhang & London, 2013).

Artificial Intelligence

Artificial intelligence is the induction of processes of human intelligence by machines, particularly computer systems (Hansen et al., 2013). Such processes include reasoning (use of rules to reach a definite or approximate conclusion), learning (the acquisition of rules and information for using the information) and self-secretion. The specific application of artificial intelligence includes speech recognition, machine vision and expert systems (Mok, 2015). AI stimulates a significant amount of both fear and excitement as well as media coverage. Notably, AI is not a new concept, by 1980s it was already adopted by some companies. In a study conducted by the Gartner, it was denoted that each of the company surveyed showed their interest in incorporate-driven solutions, 40 per cent of the companies surveyed are already in the final stages of adoption. The manufacturer is the UK are slowly but surely observing the importance of adopting the use of a digital strategy. The research denoted that 70 per cent of the business leaders acknowledge the instrumentation of AI towards organizations' competitiveness (Shafia e al., 2016). Companies that have already adopted the use of AI in their system are already observing significant progress in terms of higher competitiveness, the agility of the business as well as improved customer relationships. Machine learning has substantially become embedded in a lot of new solutions and technologies, producing in-depth insight into metrics of the business and enhancing decision making based on data (Dunning, 2013). In consideration of supply chain management, greatly seasoned professionals are prone to over or under-stocking. However, machine-learning engines use hierarchies and algorithm to predict future needs with the utmost accuracy (Boycheva, 2017). A case in point is a food giant Nestlé that applies chain forecasting to enhance the accuracy of forecasting on a global level. The company has more than 450 branches operating in other countries globally. Notably, this strategy has enabled Nestlé to improve its sales precision by 9 % in Brazil alone. Piramal glass is another leading glass manufacturing company in India which has incorporated the use of IA into its system in order to drive operation effectively, generate new models of revenue and improve customer experience. Notably, their, RTMI, in-house solution, provided insights in real-time leading to a reduction in defects by 5 percent, 25 percent enhancement in productivity of the employee and 40 percent manual data reduction (Calabrese et al., 2013).

Disruption of Artificial Intelligence (AI):While the recent research shows that disruption majorly occurs on workplace roles, disruption can also happen at the departmental, organisational and team level (Zhang & London, 2013). For instance, as the driverless vehicles tend to disrupt the role of the individual, trucking and taxi company are also displaced, and entirely the industry itself. Such kind of displacement poses a significant disruption to the business models, thereby resulting in an automated service delivery business.

The Big data analytics

The big data analytics is an intricate process of studying varied and large data sets in order to reveal information such as unknown correlations, hidden pattern, and customer preference and market trends that can assist companies in making an informed decision (Oh & Philip, 2013). The application of Big data, which entails a large amount of data that can be easily combined as well as analysed for the need to find the facilitation and patterns in decision making, essentially form the basis of business growth as well as competition (Amoros et al., 2012). This improves productivity and add value to the business orgsnistions through the reduction of waste and elevation of overall quality products and services (Hansen et al., 2013). The use of Big Data is slowly and surely becoming an important factor for the top businesses to supersede their competitors. In many businesses, new entrants and established competitors are taking advantage of the data-driven strategies in order to capture value, innovate and remain at the brim of the competition (Schwab, 2009). It has been denoted that many organisations have shown great interest in the use of Big Data to improve their competitive edge. In healthcare, the organisations are carrying out analysis of health outcome and benefits that could not be proven initially during the clinical trials. Other early adopters of this technology are currently making the good use of data from sensors that are fixed in their products from daily wearable to industrial goods to determine how these different products are applied in the real world (Fu et al., 2011). Knowledge of this kind can create awareness on the creation of new services and the design of upcoming products. Big Data helps in the creation of new growth opportunities and new business categories in order to enable the organisations to integrate and analyse the industrial data (Rasheed & Sagagi, 2015). According to Boycheva (2017), market experts in various business sectors are becoming aggressive in the creation of Big Data potential to their companies. There are a number of big companies that have incorporated Big data analytics into their system in order to keep abreast with the competitive market. For instance, General Electric (GE) is applying sensors data on machinery such as jet engine and turbines to identify ways of reliability and working process. The outcome reports are then submitted to the GE team in order to develop tools and enhancement for increased efficiency. It has been estimated that the data could boost productivity by 1.5 per cent. Another example is the Next Big Sound which has projected ways on how to utilize the Big Data from Facebook likes, Youtube hits, Soundcloud plays and iTunes sales to predict the next valuable thing in music (Zhang & London, 2013).

The Disruption of the Big Data analytics: It is of no doubt that Big Data analytics is a disruptive force on business models. This implies that people should acquire new skills, technologies and tools in order to interact well with the new innovation. People need an open mind that will enable them to rethink about the procedures and process they have for a long time followed and change the manner in which they operate. It should, however, be noted that it not that easy to coerce this type of transformation on employees that have worked for the company for a long time (Fu et al., 2011).

Theories of Disruption

Demand-side theories of disruption

The demand side theory comes into play when established organisations fail to notice the dangers of new entrant innovation technologies, as such, they resolve to ignore the view that their clients would find the new innovations offers to be appealing (Amoros et al., 2012). Just as one may expect the disruption to happen due to underestimation of the new entrant innovation by already established organizations. In fact, in many instances, the emergence of inferior products is greatly welcomed due to the fact that unprofitable customers take the chance to adopt the products that are offered by the new entrant, and this will turn the attention of the established organisation to focus all their resources to the most profitable customers (Hansen et al., 2013). This moves well until the very point when iterative improvement process when the new entrant product promptly moves up the new innovative technology S curve and rapidly accomplish performance-parity with the established organisation's product at an essentially more appealing price-point (Shafia et al., 2016). At this stage, the mainstream customers suddenly abandon the established organisation in favour of the new entrant firm (attacking company) in cascading waves, thereby causing abrupt failures to the once-dominant organizations. According to Mok (2015), the key fundamental aspect of understanding the theory of demand-side disruption is mainly driven by a change in consumer expectations and tastes. According to Christensen, the demand side tends to 'blind sightedness' thereby making it hard for the established organization to restore its leading position. In the context of demand-side theory, innovations have the following features in common:

They perform dismally as compared to an established company's products that are serving mainstream customer's need.

They are likely to appeal to a niche segment. (Such niche is normally underserved by established companies, thereby opening doors for new entry).

Innovations can pose threats by improving promptly along dimensions that real customers care about.

This is an outgrowth theory of Henderson-Clark School. It comes into play when the new entrant makes it difficult for the established organisations to respond due to the fact that they have

achieved success through a given way that connects them in a particular architectural knowledge foundation from which they cannot afford to abandon even if their core business is compromised (Fagerberg & Srholec, 2017). To respond, the established organisations are coerced to set up an entirely new system of carrying out its operation. Notably, the established organisations will find it difficult to implement such changes because there is no guarantee that the new system will perform better than the already existing architecture which has for a long time formed the basis of historical success for the incumbent organizations (Tejinder, 2010). Uncertainties drag established organizations into making difficult choices that they must make in order to adapt and survive, assuming that they are not oblivious of the changes to be made. It is important to note that the incumbent, established organisations, is not aware of the architectural knowledge that forms the cornerstone on which the attacker uses to enter the market, and the attendant uncertainty renders an already frightening task to become more difficult (Fu et al., 2011).

My organization adopted the use of Natural Language Processing in order to remain abreast with other key market players. Natural Language Processing (NLP) is a component of artificial

intelligence that assist computers to internalize, interpret and control human language (Block & Keller, 2015). There has been an advancement in Al-based conversational tools, however, when technology depend on artificial languages such as C++ or Java, there is an automatic limitation to literal translation. It should be noted that human language is intricate and filled with subtleties, as such, there can be a misunderstanding. In contrast, NLP solutions are capable of learning to speak organically through constant practice just as humans do. According to Scwab (2009), these tools are able to discern different emotions and acknowledge the differences in frustration, anger and fear. The potential of NLP is limitless at the workplace and therefore I would employ the use of NLP technology to realize the full potential of the company through various business models. First, I will use the technology to deploy semi-structured and open-ended surveys to employees, in order to uncover their feelings and thoughts. It is with no doubt that this will help the organisation to resolve into prompt actions that will elevate their experience thus enhancing business performance. Secondly, in the analysis of digital footprint (emails, browsing behaviour, search keywords and social media) I will apply NLP to assist the company in identifying potential customers that are interested in the company's products. Through the performance of simple keyword routine, the Natural Language Processing software assists in broadening the channels range for the placement of the adverts. This, in turn, will help the company to effectively spend the ad budget whistle reaching out to potential customers. While sense ambiguation feature that helps in detecting the word used in a particular context is still far from perfection, NLP has consistently proved valuable in cutting down the advertisement cost (Hansen et al., 2013). Additionally, I would use NLP to derive and summarize information, this synthesized information from various text sources such as user manual, news reports among others can then be applied by the company to make a decision or rather take action based on algorithms.

Machine Translation (MT): One of the biggest challenges of this innovative technology is machine translation. It is nearly impossible for the machines to translate all languages without any modification. The alignment and modelling of language remain to be a major challenge for researchers to enhance machine translation.

Machine Learning: ML likely to underperform if it is fed with wrong or incomplete data. Additionally, ML does not possess the knack to stop over learning. This kind of skills is found in humans who have the ability to object excess information. The human capacity is able to acknowledge the fact that excess information can result in confusion or clouding of matters. This instinctive human knack is not found in machine learning. The consequence is the contamination of decision that can occur very fast hence difficult to detect until it is very obvious that damages have been made (Dunning, 2013).

Levels of accuracy: While perception poses a high accuracy level that can top 95 per cent for organizations, studies denote that the level can be much lower (Oh & Philip, 2013). This research brings many issues into question. How can product users make an informed decision on how to use the product without knowing the actual rates? How can they confirm the accuracy without turning back to the old methods of comment-coding? Even if the accuracy stands at 95 per cent, how does a leader coherently explain to the other employee (1 out of 20) that their comments may have been mis-tagged and therefore ignored?

New Discovery

NLP adopts systematic iteration to grammar to make out the context of an attribute fed into it. This value associated with the ability to search using natural language is highly appreciated by customers. Suppose a query like "add for me all G$96 data" is given, the program must actually comprehend all words and characters used so that "add for me" can be interpreted as gather, a sum then report back. "All" could prompt computation in rows and or columns. Finally. "G$96 data" could be a pointer to the database to be processed.

Conclusion

The continuous evolution of innovation and technology of the business environment have compelled corporations to consistently search for tactics and strategies to gain merits and stay competitive over other key players in the market. The integration of communication and information technologies has significantly transformed the effectiveness of the company, competitive environment and operation efficiency. Web-based technologies and the internet enables employees and managers of various corporations to operate the business and transfer data world-wide (Zhang & London, 2013). The role of innovative technologies presented in this paper provides a unique opportunity for organisations to prevent strategic jeopardy. Investment in such technologies has enabled different companies to remain competitive in the world market.

References

Carayannis, E. G., & Wang, V. W. L. (2012). Competitiveness model—a double diamond.

Journal of the Knowledge Economy, 3(3), 280-293.

Maier, A., Brad, S., Nicoară, D., & Maier, D. (2014). Innovation by developing human resources,

ensuring the competitiveness and success of the organization. Procedia-Social and Behavioral Sciences, 109, 645-648.

Mok, K. H. (2015). Higher education transformations for global competitiveness: Policy

responses, social consequences and impact on the academic profession in Asia. Higher Education Policy, 28(1), 1-15.

Shafia, M. A., Shavvalpour, S., Hosseini, M., & Hosseini, R. (2016). Mediating effect of technological innovation capabilities between dynamic capabilities and competitiveness of research and technology organisations. Technology Analysis & Strategic Management, 28(7), 811-826.

Sedziuviene, N., & Vveinhardt, J. (2010). Competitiveness and innovations: role of knowledge management at a knowledge organization. Engineering economics, 21(5).

Fagerberg, J., & Srholec, M. (2017). Explaining regional economic performance: the role of competitiveness, specialization and capabilities. In Handbook of Regions and Competitiveness. Edward Elgar Publishing.

Dunning, J. H. (2013). Multinationals, Technology & Competitiveness (RLE International Business). Routledge.

Boycheva, C. (2017). Innovation and competitiveness in the context of the Bulgarian tourism industry. Economic alternatives, 1, 137-148.

Carayannis, E., & Grigoroudis, E. (2016). Quadruple innovation helix and smart specialization: Knowledge production and national competitiveness. Форсайт, 10(1 (eng)).

Tejinder, S. (2010). Role of innovation in growth of countries. Perspectives of Innovations, Economics and Business, PIEB, 4(1), 15-17.

Dwyer, L., Dragićević, V., Armenski, T., Mihalič, T., & Knežević Cvelbar, L. (2016). Achieving destination competitiveness: an importance–performance analysis of Serbia. Current Issues in Tourism, 19(13), 1309-1336.

Zhang, P., & London, K. (2013). Towards an internationalized sustainable industrial competitiveness model. Competitiveness review: an international business journal, 23(2), 95-113.

Fu, X., Pietrobelli, C., & Soete, L. (2011). The role of foreign technology and indigenous innovation in the emerging economies: technological change and catching-up. World development, 39(7), 1204-1212.

Calabrese, A., Campisi, D., Capece, G., Costa, R., & Di Pillo, F. (2013). Competiveness and innovation in high-tech companies: an application to the Italian biotech and aerospace industries. International Journal of Engineering Business Management, 5, 40.

Petkovska, T. (2015). Role and importance of innovation in business of small and medium enterprises. Економски Развој-Economic Development, 17(1-2), 55-74.

Block, F. L., & Keller, M. R. (2015). State of innovation: the US government's role in technology development. Routledge.

Bhawsar, P., & Chattopadhyay, U. (2015). Competitiveness: review, reflections and directions. Global Business Review, 16(4), 665-679.

Costantini, V., & Mazzanti, M. (2012). On the green and innovative side of trade

competitiveness? The impact of environmental policies and innovation on EU exports. Research policy, 41(1), 132-153.

Oh, D. S., & Phillips, F. (Eds.). (2013). Technopolis: Best Practices for Science and Technology Cities. Springer Science & Business Media.

Neuhofer, B., Buhalis, D., & Ladkin, A. (2012). Conceptualising technology enhanced destination experiences. Journal of Destination Marketing & Management, 1(1-2), 36-46.

Rasheed, A. A., & Sagagi, M. S. (2015). Nigeria’s Competitiveness in a Knowledge-Based Economy: The Changing Role of Government in a Triple Helix. Association of Business Schools (WAiBS) 2015: Enhancing Prductivity and Sustainability, 22.

Rodionov, D. G., Guzikova, L. A., & Rudskaya, I. A. (2014). Innovation potential of regions as a factor of national economy competitiveness. Актуальні проблеми економіки, (8), 215-223.

Amorós, J. E., Fernández, C., & Tapia, J. (2012). Quantifying the relationship between entrepreneurship and competitiveness development stages in Latin America. International Entrepreneurship and Management Journal, 8(3), 249-270.

Bryson, J. R., & Rusten, G. (2010). Design economies and the changing world economy: Innovation, production and competitiveness. Routledge.

Hansen, J. M., McDonald, R. E., & Mitchell, R. K. (2013). Competence resource specialization, causal ambiguity, and the creation and decay of competitiveness: The role of marketing strategy in new product performance and shareholder value. Journal of the Academy of Marketing Science, 41(3), 300-319.

Aleksejeva, L. (2016). COUNTRY'S COMPETITIVENESS AND SUSTAINABILITY: HIGHER EDUCATION IMPACT. Journal of Security & Sustainability Issues, 5(3).

Schwab, K. (Ed.). (2009). The global competitiveness report 2009-2010. World Economic Forum.

Continue your journey with our comprehensive guide to The Role of Technological Advancements in Molecular Biology.