ScienceDirect Labour Productivity and Possibilities of ...

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ScienceDirect Procedia - Social and Behavioral Sciences 109 (2014) 1088 – 1093

2nd World Conference On Business, Economics And Management - WCBEM 2013

Labour Productivity and Possibilities of its Extension by Knowledge Management Aspects Vladimír Bureš a *, Andrea Stropková b a

Faculty of Informatics and Management, University of Hradec Králové, Rokitanského 62, 50003 Hradec Králové, Czech Rpublic b Vysoká škola manažmentu/City University of Seattle, Bezručova 64, 911 01 Trenčín, Slovakia

Abstract Productivity represents a phenomenon that is investigated by researchers, who have already created several productivity models, and applied by practitioners who pursue models’ usability. Various aspects and issues are incorporated in and several disciplines contribute to existing models. All these activities are conducted at three basic levels which are not always clearly distinguished. The aim of this discussion paper is to provide with review of labour productivity within the context of knowledge society. The paper covers theoretical frame of productivity, knowledge management and discusses if and how labour productivity may be positively influenced by the existence of the knowledge society in general and knowledge management programmes in particular. © 2014 The Authors. Published by Elsevier Ltd. Open access under CC BY-NC-ND license. Selection and peer review under responsibility of Organizing Committee of BEM 2013. Keywords: Productivity, Knowledge economy, Knowledge Management, Levels;

1. Introduction In general, productivity measures output relative to input and it is a core factor of economic growth (OECD, 2001) or an enabler of ensuring strategic advantage (Porter, 1980). The economic terminology finds productivity, as an indicator of efficiency of resources use, to present a ratio of amount of goods produced within certain timeline and amount of work necessary for goods production within the same timeline. Thus, labour productivity determines amount of goods produced within a labour unit. However, every field or industry sector uses its own modifications, specification or level of details focused on their particular needs (Song & AbouRizk, 2008). For instance, project managers and construction professionals define productivity as a ratio between earned work hours and expended work hours, or work hours used (Hanna et al., 2005). While there are several input resources in a transformation process, labour productivity plays a particular role. A deeper comprehension of the factors influencing labour productivity can enable managers to more effectively allocate limited resources, provide workers with better support, or increase workers' motivation (Marešová et al., 2011). Recent studies have indicated the value of production labour or non-production labour (e.g., engineers, product designer, quality inspectors, and administrators) to a manufacturing plant's productivity (Wacker et al., 2006). However, research that has been presented up to date faces two major defects. First, studies mostly investigate the effects of one of the influencing factors on the labour productivity (e.g. (Jeanneney & Hua, 2011)) and they are not able to account for the effect of

* Corresponding Author: Vladimír Bureš, PhD. Tel.: +420-49-333-2259 E-mail address: [email protected]

1877-0428 © 2014 The Authors. Published by Elsevier Ltd. Open access under CC BY-NC-ND license.

Selection and peer review under responsibility of Organizing Committee of BEM 2013. doi:10.1016/j.sbspro.2013.12.592

Vladimír Bureš and Andrea Stropková / Procedia - Social and Behavioral Sciences 109 (2014) 1088 – 1093

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all the influencing factors. In reality, the labour productivity is influenced by many other factors which have complex interactions among each other. Rarely do research studies incorporate systems approach that should be applied (Bureš, 2006). Second, most research fails to reflect current progress in knowledge-related disciplines. Knowledge technologies, knowledge management, or knowledge economies remain unconsidered in particular models. For instance, Nasirzadeh and Nojedehi (2012) tried to bridge this gap and investigated labour productivity as a systemic phenomenon and described mutual interrelationship among several factors such as worker’s motivation, fatigue, skilfulness, availability of materials, adverse weather conditions, or time per task. Their conceptual model of labour productivity contains almost fifty factors, however, explicit expression of knowledge is missing. The only closely related factor is training, which is only influenced by the “deficiency in financial resources” and affects the “skilfulness”. The last hint more or less related to knowledge is related to the factor “unfamiliarity with new technique”, which is again causally connected with only one cause and one effect. Due to the above, the aim of this paper is to provoke a discussion on incorporation of knowledge aspects into the labour productivity indicators. 2. Knowledge and labour productivity – what do they have in common? Basically, there are two predominant intersections of knowledge-related issues and labour productivity. First, both phenomena can be described at three basic levels. Whereas Demeter et al. (2011) depict operational, business, and macro level of labour productivity, Bureš (2009) distinguishes basic level of knowledge oriented activities, namely management of knowledge level, organisational level, and national level. Not surprisingly, when analysed deeper, these levels correspond with each other. Second, the rapid growth of output and labour productivity across countries has largely been driven by advances in Information and Communication Technology (ICT) (Ceccobelli et al., 2012). Establishment and quick spread of knowledge-related issues within all there aforementioned levels is also closely related to deployment of ICT (Bureš, 2006b). 2.1. National level At the national level, labour productivity is studied from numerous sources and perspectives such as elasticity of substitution between capital and labour (Makin & Strong, 2013) or convergence behaviour of labour productivity (Herrerias, 2012). Over the past years many studies have attempted to show and explain the importance of labour productivity in practice. It seems clear that labour productivity plays vital role in growth in per capita income and rising living standards. Steindel and Stiroh (2001) compared the productivity growth in the US to the “golden age” of the 1950s and 1960s: if labour productivity were to grow at 1.5% (the average rate from 1973 to 1995), output per hour would rise by 35% after 20 years. Growth of 2.7% (the average for 1995-99) implies that it would be 70% higher after 20 years. Although this conclusion comes from 2001, the importance of productivity improvement has remained up to date. It is the aim of knowledge society to seek for drivers and sources of productivity sustainability and knowledge transfer within the rapidly growing integration of the global economy. The Global Competitiveness Report defines drivers of productivity and prosperity nation-wise. The one for 2012-2013 investigated within 144 countries, thus remaining to be the largest assessment of economies competitiveness worldwide. Having reviewed all of them, the report concludes that global growth remains historically low for the second year with two main centres of economic activities: emerging markets growing faster than advanced economies and developed markets steadily closing the income gap (The Global Competitiveness Report 2012-2013, 2012). The direct relationship between labour markets and labour productivity is evident. In the knowledge society, the traditional concept of productivity faces both old and new challenges (Tuomi, 2004). Productivity remains to be a key point of interest of knowledge not only due to motivation to understand sources and development of economic growth but also due to productivity problems explored by knowledge society trying to capture issues that theoretically and empirically difficult and central (Tuomi, 2004).

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2.2. Organisational level Labour productivity in particular companies is considered at this level, e.g. Morariu and Bostan (2012) investigated the decision-making issue based on typical dichotomy – investing in new technologies and competencies, or diminishing the number of jobs – in steel companies. From the knowledge-based perspective knowledge management (KM) programmes are taking place at this level. Definitions of KM, articulated by those who do believe in it, vary. For instance, Bukowitz and Williams (2002) define KM as the process where the organisation generates wealth from its knowledge or intellectual capital. Key four processes within KM include creating, storing & retrieving, transferring, and applying knowledge. Since relationship of KM to labour productivity is apparent, the one of the earliest KM adopters, who pioneered knowledge-based programmes in the business realm, was the American Productivity & Quality Center. Nevertheless, investigation of connection between KM and labour productivity cannot rely solely on apparent links. Therefore, Steindel and Stiroh (2001) claim that one should take into account factors that usually stay left out when talking about what fosters it. Usually mentioned are measurable factors, such as change in education, experience of workforce, or structure of capital, however, there is remaining portion of productivity growth referred to as total factor productivity, that remains unaccounted for, such as general knowledge, the advantages of particular organisational structures or management techniques, reductions in inefficiency, and reallocations of resources to more productive uses. Not all the researchers are convinced about the real value of knowledge management. Some even claim that KM shows immaturity. For instance, in 2004 OECD recognised that “knowledge management practices seem to have a far from negligible effect on innovation and other aspects of corporate performance. But there is little systematic evidence of just how great an effect knowledge management has. Among the various categories of knowledge related investments… knowledge management is one of the areas about which little is known in terms of quality, quantity, costs and economic returns” (OECD, 2004). Wilson (2002) also questions in his work and adds that writing in the area of knowledge management comes from both academic and practitioner environments within which to large extent work is driven by consultancy companies rather than academic research, thus, resulting into disconnect between the theory and practice. Nevertheless, debates about knowledge management and its importance have had their ups and downs. While OECD view of knowledge management presented earlier sounds rather resistant, it is necessary to mention that macro-level indicators developed by OECD may not be sufficient enough to explain complex knowledge activities on an organisational level. In spite of that, OECD has acknowledged the importance of knowledge assessment and the fact that organisations are dependent on production, distribution and use of knowledge than ever before. Although in this respect some may have a ‘déjà vu’ or feeling of reinventing the wheel, what is definitely new in knowledge management is the potential of using modern information technologies, such as Internet, intranets, browsers, data warehouses, data filters and software agents to handle knowledge organisation-wide (Zilber, 2007). They have proved to bring more collaboration, greater speed, lower costs and more satisfaction thanks to customer – supplier integration and self-services. Thus, ICT has moved from its supporting role up to process oriented systems (Mohamed et al., 2006). ICT technologies have become increasingly important drivers of economic growth and major source of productivity growth in 1990s in many developed countries. They are often referred to as ‘core technologies of the emerging knowledge-based economies’ (Tuomi, 2004). Several examples of ICT tools can be mentioned in relation to sharing, dissemination, use, application and adoption of knowledge at the organisational level (Čech & Bureš, 2009). For instance, in case of corporate portals “centralising information, formatting business processes, and connecting people for a mutual cooperation, the corporate portals can increase the operational efficiency, reduce costs and build up loyalty” (Voth, 2002). Research conducted by Eric Brynjolfsson, an economist at the Sloan School of Management at the Massachusetts Institute of Technology, shows that companies that use “data-directed decision-making” (defined “not only by collecting data, but also by how it is used—or not—in making crucial decisions”) enjoy a 5-6% boost in productivity (Big data Harnessing a game-changing asset, 2011). There is no doubt that big data; cloud and mobility have changed business value solutions as they are now enabling intelligent businesses, solutions and most of all – innovation.


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2.3. Individual level Issues related to personal or economic aspects of particular jobs or individuals are studied at the individual level. For instance, Kampelmann and Rycx (2012) investigated the relationship between pay differences among occupations, or McCarthy and Palcic (2012) explored how large-scale employee share ownership plans affect labour productivity. From the knowledge perspective the productivity at this level is closely related to training, creativity and proactivity. In order to share knowledge and work with new ideas, employees need to be trained. Polishing skills in out-of- the-box thinking, abilities to think critically, be able to differentiate root problems from symptoms and implement suggested solutions – all that contributes to better generation of new ideas and innovation. In some companies ICT managers work with people to create a uniform language for project specifications, aim of which is to smooth communication among divisions (Violino, 2002). People also need to be trained on abilities to realise differences between correlations and causalities, problem solving and willingness to disseminate and adopt new inputs. All that helps employees turn tacit knowledge into explicit knowledge and save it, for example for other employees, successors, or replacements. Blankenship (2008) illustrates how different methods work or do not work in knowledge retention. While some managers try to capture knowledge by simple ‘write it down’, the process of recording knowledge is more complex and “requires interaction-based and learning culture-based methods”. That is how tacit knowledge is captured; often referred to as ‘know-how’ or even ‘know-who’, yet remaining to be the highest-value knowledge. The top list approaches how to transfer knowledge, according to Blankenship include document repository, incorporating retirees, mentoring and organisational learning and training. Levine and Gilbert (1998) believed that in order to support generation of new ideas, management should consider incentive pays for new ideas or improvements. Later, it has become common that managers provide monetary rewards or recognition to employees who contribute by valuable ideas. The company environment should be open enough to promote such initiative, or even create opportunities for experiments that may result into great ideas or innovations. Forms vary: some companies use voluntary suggestion program where ideas are evaluated on a regular basis, others include new ideas generation into monthly shift assessments. However, the experience shows that paying for each posted idea usually does not prove to be very efficient because that produces quantity, rather than quality. There are also formal employee involvement structures that help to generate and share ideas. They include brainstorming sessions, quality circles, self-directing teams and other. It is critical that companies learn from past experience, learn from it and take it into consideration successes, failures, analyse their causes and record learned lessons in an accessible place. 3. Case study: automotive industry One of the most descriptive ways how to depict interrelation of all three levels in terms of productivity and knowledge-based approach is by a thorough looking at the automotive industry. This environment is highly challenging and tense in terms of pace and costs. Innovation, reduction of development time and costs are main business goals driving decision making within the automotive industry. It contains huge amount of information, therefore information integration within this sector has become the major challenge over the last decade (Zilber, 2007). The most prominent inhibitors and risk factors include all kinds of documents, including those in the form of simple e-mails up to complex project descriptions developed by process engineers. Dissemination of corporate knowledge runs throughout the company in both unstructured ways (text, draft and office documents, etc.) and structured formats (projects, relational databases, etc.). Following the invention of Ford’s Assembly Line Production as well as Toyota’s lean production concept, McKinsey was in 2003 forecasting a “third revolution” when customers expect “more car” for the same money (McKinsey, 2003). Thus, focus is turned to transformation in the structure of the automotive supply chain (Zilber, 2007) and gaining competitive advantage via knowledge management system, which, in automotive, is largely based on the following (Ferreira, 2007): • Real time Collaborative virtual environments,

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• Fast and smart retrieval, • Manipulation of past programs knowledge, and • Increasing full product lifecycle management. When it comes to knowledge management and its influence on performance, one can argue that tools have been around since long time ago. Daily or weekly activities in automotive companies include quality circles, improvement measures (Audits 5S or Kaizen practices); workforce skills and training needs are determined using job evaluation processes and multi-skills matrices, key performance indicators integrate maximum emphasis on short development cycles and minimum quality failures. All that is blended by strong visual management tools (boards, posters, or models) and efforts to meet both customer and society expectations as well as environmental sustainability (waste management). Applying all the techniques and methods fostering use of best knowledge turns effectively into smoother operations with less quality issues and quicker returns. Knowledge management is used to improve productivity along with needs for a new paradigm of productivity measurement. 4. Conclusions This paper discusses labour productivity and its relationship to knowledge-based movement in the current society. The line of reasoning is grounded in the existence of three basic levels that both phenomena have in common. Results evoke two paths or directions how to integrate knowledge management into boosting labour productivity: first, by focus on better integration of human capacities and second, by developing technology tools that help information integration within the companies. Organisations do a lot of research and spend a lot of money to promote creativity and innovation but in many cases innovative and progressing ideas exist already in some form. The key is the knowledge transfer to capture know-how existing within organisations and pass it along for further adoption. As Levine and Gilbert (1998) say, in order for an organisation to be a true “learning organisation”, it must acknowledge the importance of all phases of knowledge creation and transfer and endeavour to create a culture of sharing and continuous improvement. Rarely would anybody argue against the major role of knowledge in all aspects related to production management. This is especially true in the automotive industry, as this industry is considered to be the flagship of industrial sectors in relation to the complexity of management topics (Stocchetti, 2007). Application of knowledge management benefits companies in numerous ways and contributes to better organisational innovation, leveraging of expertise across organisations, or organisational learning. On a business level, knowledge management contributions include achieving shorter new products development cycles, applicability of the project to every transport enterprise, not only automotive, and ensuring customers satisfaction with high quality transportation products (Ferreira, 2007). Justification for necessity to understand labour productivity within the knowledge-driven economies is rather obvious. The indicator as such does not reveal any information about the structure or quality of employment, however, labour productivity statistics, trends and estimates can help us in better definition and development of labour market policies. The knowledge aspects serving as a value added to classical labour productivity include vibrant combination of effective identification of labour force, education, training needs and information and communication technologies and solutions.

Acknowledgements This paper is supported by the specific research project Analysis of Influential Factors on Information and Knowledge Sharing within Organisations and the Measurement of its Efficiency. References Blankenship, L., & Brueck, T. (2008). Planning for knowledge retention now saves valuable organisational resources later. Awwa, 100(8), 1-5. Bukowitz, W., & Williams, R. (2002). Manual de Gestao do Conhecimento: Ferramentas e Tecnicas que Criam Valor para Empresa. Porto Alegre: Bookman.


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