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This paper is concerned to show how the Danish political elite interpreted and responded to the consequences of the 2008 financial crisis for the Danish economy. In particular, the paper describes how this interpretive construction focused primarily on three features of the Danish context to the exclusion of other perspectives; the first was an emphasis on the problems of the financial sector, of interest rates and state finances; the second was that Danish productivity increases were falling behind other comparable countries and part of the solution required new strategies towards labour and unemployment benefits; thirdly, the adverse effects of the crisis were causing an increase in government expenditure and a decline in government revenues which was rapidly becoming unsustainable. As a consequence, the Danish elite fell into the broader interpretation of the crisis embedded in the dominant view within the EU institutions as well as among the international financial institutions such as the IMF and the World Bank, that a period of austerity and fiscal consolidation was the required remedy, even though this was likely to be pro-cyclical in its effects. However, the paper shows that alternative data which is more reflective of Denmark’s position in the global economy and the trajectory and form of its growth over the last decade reveals that the interpretation of the Danish elite has been too narrow and neglects the distinctive roots of Denmark’s competitive strengths. Indeed, by responding in the way which they have, the Danish elite is in danger of undermining the very conditions of Denmark’s competitiveness.

The use of digital technology in firms has drawn attention of innovation management scholars and policy-makers, especially the imitation of digital technology and competition among peer firms. Drawing on dynamic competition theory, this paper examines how firms react to their peers' digital innovation behavior and the effect of external environment mechanisms on the magnitude of peer effects.

This paper utilizes a text mining method to construct a baseline model with a Tobit estimator using data obtained for Chinese listed firms.

The findings suggest that peer effects on digital innovation behavior are robust and significant positive in China. Moreover, peer effects on digital innovation participation are positively magnified by firms' strong social network and high Fintech development. However, peer effects are relatively higher in non-state-owned enterprises (non-SOEs), low-profitability and high R&D firms.

The authors' findings contribute to the digital management literature by showing that firms need digital technological imitation and diffusion of innovations in the digital era.

Managers should provide insights into firms' imitation of their peers' acts to preserve competitive parity. Besides, firms should integrate employees within the organization and communicate digital innovation concepts and behaviors to external peer firms.

First, this paper contributes to explaining how firms change their digital innovation strategy through the influence of peers' digital innovation behavior. Second, this paper fills the literature gaps related to the moderating effects of external environment factors in peer effects of digital innovation behavior.

The learning outcomes are as follows: business model pivot, minimum viable product, strategic alliances, return on equity and burn-rate.

HMSolution’s (HM’s) mission is removing arsenic from drinking water. The case tells how HMS pivoted its business model between 2014 and 2015 and its challenge when faced with several growth opportunities. The first possible partner company proposed adopting HMS’s technology through either an alliance or outsourcing. The second company wanted to acquire HMS. However, Margaret – the founder and CEO – managed to find a third option in the form of an important sanitation sewage treatment company in Chile with international presence, with which she could reach a wider territory in her country of origin, as well as in other countries where that company had a presence. This case study presents Margaret’s dilemma of deciding the best course to follow and finding the best fit for her product and the needs of the market.

The instructor can adapt the requirements and depth of the topics addressed, ranging from an undergraduate audience to an executive training audience. Undergraduate courses, namely, entrepreneurship, business creation, administration and strategy. For students of business careers, administration, commercial engineering, industrial civil engineering and industrial engineering. Continuous training, namely, entrepreneurship, business creation, administration and strategy.

Teaching Notes are available for educators only.

CSS 3: Entrepreneurship.

Introduction: The world is passing through a technology explosion phase where one technology is being replaced by another very quickly. Emerging technologies play more important roles in the insurance sector directly or indirectly. These technologies have a high potential to change the insurance paradigm.

Purpose: In this chapter, we discuss emerging technologies such as artificial intelligence (AI), big data, blockchain, the internet of things (IoT), mobile technology, predictive analytics, social media, telematics, chatbots, low codes, and drones in the context of the insurance industry.

Methodology: To carry out our analysis, we searched for data using the keywords for each technology from the Web of Science (WoS) coral database. Certain inclusion and exclusion criteria were followed to select the articles for further analysis. R-studio was used for the data analysis and visualisation.

Findings: It was found that the highest number of research articles published are related to big data, followed by AI and social media. The first article on AI in insurance appeared in 1975. Social media is the highest cited new technology, whereas the low codes are the undiscovered paradigm for the insurance sector with no published research. Research on the impact of chatbots, drones, and mobile technology in the insurance industry is still at a nascent stage. We also noticed that the United States is leading the research on emerging technologies in the insurance sector.

Implications: This chapter audits the emerging technologies in the insurance sector and identifies technological areas with the highest, least, or no research, dominant journals, authors, and countries. This holistic overview empowers managers and academicians to decide the future course of action.

The general aim is to represent managerial thinking on strategy choice in a context other than steady‐state growth. The model has the following features: (i) Strategy choice is defined as the adoption of rules governing investment choice; (ii) given its strategy, management sees growth in terms of a probability distribution of growth‐paths of expected dividend; (iii) managment's valuation model closely matches its probabilistic view of growth prospects; (iv) the managerial utility function has an extended horizon. Discussion of strategy choice yields no general presumption that managment senses a conflict between its own preference and its commonsense interpretation of investors' preferences.

Divergence in the development of East Asian and Latin American NICs is catching the attention of a growing number of political economists. This divergent development has sparked debates over THEORY between advocates of neo‐liberal and neo‐dependency approaches (Biersteker; Stallings: 370) in accounting for the regional divergence: does the East Asian success confirm modernization theory (neo‐liberalism) generally, or does each region require its own theory? (see Barrett and Whyte on Taiwan; Alschuler: chap. 4 and Lanzarotti: chap. 5 on Korea; Evans, 1987). East Asian “miracles” have led to equally bitter controversies over PRACTICE with regard to policy recommendations for third world nations: is the East Asian model exportable and is this desirable? (see Amsden; Fishlow; Broad and Cavanagh).

This article identifies serious weaknesses in various commonly used methods of measuring “average” growth rates over several time periods. An alternative method is proposed which satisfies two essential criteria: firstly it smoothes the data to remove “exceptional variation” in the time series, and secondly, it incorporates a compounding process which, it is suggested, is an essential requirement of a “correct” average growth rate.

Constructing and evaluating behavioral science models is a complex process. Decisions must be made about which variables to include, which variables are related to each other, the functional forms of the relationships, and so on. The last 10 years have seen a substantial extension of the range of statistical tools available for use in the construction process. The progress in tool development has been accompanied by the publication of handbooks that introduce the methods in general terms (Arminger et al., 1995; Tinsley & Brown, 2000a). Each chapter in these handbooks cites a wide range of books and articles on specific analysis topics.