E-Learning for Teachers and Trainers : Innovative Practices, Skills and Competences

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Understanding Entrepreneurship Process and Growth in Emerging Business Ventures under Market Socialism in China

Objectives: This paper aims to provide an insightful view of the entrepreneurial process and growth in different types of Chinese entrepreneurial enterprises under market socialism in China. This issue is explored by examining the organisational characteristics of three emerging business ventures under market reforms and institutional changes. It addresses the interactive effect of key contingency factors in entrepreneurship process and explains its impact on growth or failure outcomes in a particular ‘China type’ of market economy. Prior work: China’s hybrid economic system represents a mixed political economy with both socialist and capitalist characteristics (Lichtenstein, 1992; Morphy et al, 1992; Opper, 2001). Despite a growing body of research on Chinese small business practices alongside the economic reforms (Shen, 1994; Child, 1994; Naughton, 1994; Schlevogt, 2001; Warner, 2004; Yang, 2007; Kshetri, 2007; Yang and Li, 2008), more empirical studies are required to provide a critical insight into the emerging business practices. This research adopts a contingency model of entrepreneurship(Wickham, 2006) to examine entrepreneurship process and growth in different types of business venture. It reveals the interactive relationships among key variables such as strategy, ownership, culture and management process. Approaches: This research is undertaken through the empirical analysis of three case study companies in the textile industry. This fieldwork was conducted in 2006 and 2009 respectively. Multiple sources of data were collected including 21 open-ended interviews of owners and key managers in three case study companies. Results: The study offers an explanation on how entrepreneurship takes different forms and features in different organisational contexts. Empirical evidence supports four hypotheses: (1) The type of ownership is a key contingent factor that moderates particular entrepreneurial outcomes. (2) Leadership and knowledge accumulation capability are critical factors in learning process, significantly affecting the strategic choices in either high value or low value added products strategy. (3) The broadening of product portfolios and increased production capacity will improve survival chances and increase the likelihood of firm growth. (4) Management capability and consistency have greater impact on the outcome of entrepreneurship process than the resource and strategy factors. Implications: The findings have significant implications for a conceptual understanding of Chinese entrepreneurship dynamics. It addresses important considerations on government policy making and promotion strategies for entrepreneurship development in different forms of business venture. Value: The textile sector has pioneered the government reforms in restructuring and creating entrepreneurial enterprises. It offers a perfect case for assessing the entrepreneurship processes in a rapidly changing market environment. It emphasizes the important ownership effect on entrepreneurial outcomes. Drawing upon Wickham’s contingency model of entrepreneurship, it provides an improved understanding of this concept under particular circumstance and different contexts

Towards Design Principles for Data-Driven Decision Making: An Action Design Research Project in the Maritime Industry

Data-driven decision making (DDD) refers to organizational decision-making practices that emphasize the use of data and statistical analysis instead of relying on human judgment only. Various empirical studies provide evidence for the value of DDD, both on individual decision maker level and the organizational level. Yet, the path from data to value is not always an easy one and various organizational and psychological factors mediate and moderate the translation of data-driven insights into better decisions and, subsequently, effective business actions. The current body of academic literature on DDD lacks prescriptive knowledge on how to successfully employ DDD in complex organizational settings. Against this background, this paper reports on an action design research study aimed at designing and implementing IT artifacts for DDD at one of the largest ship engine manufacturers in the world. Our main contribution is a set of design principles highlighting, besides decision quality, the importance of model comprehensibility, domain knowledge, and actionability of results

Initiating e-learning by stealth, participation and consultation in a late majority institution

The extent to which opportunities afforded by e-learning are embraced by an institution can depend in large measure on whether it is perceived as enabling and transformative or as a major and disruptive distraction. Most case studies focus on the former. This paper describes how e-learning was introduced into the latter environment. The sensitivity of competing pressures in a research intensive university substantially influenced the manner in which e-learning was promoted. This paper tells that story, from initial stealth to eventual university acknowledgement of the relevance of e-learning specifically to its own context

Analytics and complexity: learning and leading for the future

There is growing interest in the application of learning analytics to manage, inform and improve learning and teaching within higher education. In particular, learning analytics is seen as enabling data-driven decision making as universities are seeking to respond a range of significant challenges that are reshaping the higher education landscape. Experience over four years with a project exploring the use of learning analytics to improve learning and teaching at a particular university has, however, revealed a much more complex reality that potentially limits the value of some analytics-based strategies. This paper uses this experience with over 80,000 students across three learning management systems, combined with literature from complex adaptive systems and learning analytics to identify the source and nature of these limitations along with a suggested path forward