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This paper contributes to the theory of the relationship between human resource management (HRM) and innovation at small- to medium-sized enterprises (SMEs) by conducting a conceptual analysis of the question why Germany boasts by far the highest number of “Hidden Champion” SMEs. This is done by case studies from the army and public financial management of aid disbursal in developing countries. Implications for HRM at SMEs are discussed.

Conceptual analysis using case studies.

Contributing towards filling the gap concerning theoretical underpinnings of the link between HRM and innovation, we suggest that interdisciplinary work from relevant organisational case studies indicates that the concept of institutional design to provide motivational incentives may be relevant, especially concerning high performance systems with bundles of HRM practices. Specifically, the fundamental principle of subsidiarity is found to be important.

The research is broadly applicable to organisations of all kinds, as the diverse case studies indicate. We point towards tentative implications for the firms that account for the majority of the work force, namely SMEs, and among them the most successful ones, the so-called “Hidden Champions”.

HR managers can improve motivation, performance and innovation by decentralising decision-making as far as possible, while ensuring the overall organisational goals are well understood and shared, and resources are dedicated to train and educate staff. Additionally, the conception of rank-order competitions complements the institutional design.

Greater productivity and material performance as well as greater job satisfaction via larger autonomy and decision-making power on the local level can be achieved by the application of subsidiarity as key HRM configuration. This can be employed at SMEs, as discussed, but also other organisations. Further, the principle of subsidiarity and the greater emphasis on staff training and education may help reduce inequality.

Our paper contributes towards filling the gap in the literature on the link between HRM and innovation, by identifying the role of subsidiarity. We introduce an interdisciplinary perspective, with contributions from economics and psychology, among others. We also contribute to the history of HRM.

Currently, China’s economy is in the critical phase of transforming economic development patterns and replacing old growth drivers with new ones. Whether it can successfully overcome the “middle-income trap” has become a significant issue attracting wide attention.

Driven by underlying digital technologies such as artificial intelligence, blockchain, cloud computing and big data, the fourth industrial revolution featuring the booming digital economy has provided significant opportunities for China’s economy to “overtake” and overcome the “middle-income trap”. The transformation of economic development pattern, the optimization of industrial structure, and the change of growth drivers, brought by the deep integration of digital and real economies are the keys to leaping over the “middle-income trap”.

From the supply side, the digital economy can improve the quality and efficiency of the supply side and promote the supply-side structural reform and economic growth from the following three aspects: First, promote the quality, efficiency and diversification of the supply system; second, promote networking, opening-up and synergy in the innovation system and third, promote the socialization, modularization and flexibility of production pattern. From the demand side, the digital economy can boost the new drivers of the “troika” of economic growth consisting of consumption, exports and investment by changing the market investment direction, promoting consumption upgrade and fostering export strengths. However, once these two attributes interact with each other, especially when data is combined with capital, the most adhesive factor in the market economy, a series of new social relations will then be produced based on the technical attribute, resulting in significant adjustments in social relations, involving both positive and negative externalities.

To overcome the “middle-income trap”, it is necessary to adapt to the laws of economic evolution and promote a fundamental change in economic growth drivers; boost the high-quality development of the digital economy by strengthening the support role of data in the digital economy; and accelerate digital industrialization and industrial digitalization to realize the integration of digital and real economies.

China’s national carbon market will be officially launched in 2020, when it will become the world’s largest carbon market. However, China’s carbon market is faced with various major challenges. One of the most important challenges is its impact on the social and economic development of arid and semi-arid regions. By simulating the carbon price trends under different economic development and energy consumption levels, this study aims to help the government can plan ahead to formulate various countermeasures to promote the integration of arid and semi-arid regions into the national carbon market.

To achieve this goal, this paper builds a back propagation neural network model, takes the third phase of the European Union Emissions Trading System (EU ETS) as the research object and uses the mean impact value method to screen out the important driving variables of European Union Allowance (EUA) price, including economic development (Stoxx600, Stoxx50, FTSE, CAC40 and DAX), black energy (coal and Brent), clean energy (gas, PV Crystalox Solar and Nordex) and carbon price alternatives Certification Emission Reduction (CER). Finally, this paper sets up six scenarios by combining the above variables to simulate the impact of different economic development and energy consumption levels on carbon price trends.

Under the control of the unchanged CER price level, economic development, black energy and clean energy development will all have a certain impact on the EUA price trends. When economic development, black energy consumption and clean energy development are on the rise, the EUA price level will increase. When the three types of variables show a downward trend, except for the sluggish development of clean energy, which will cause the EUA price to rise sharply, the EUA price trend will also decline accordingly in the remaining scenarios.

On the one hand, this paper incorporates driving factors of carbon price into the construction of carbon price prediction system, which not only has higher prediction accuracy but also can simulate the long-term price trend. On the other hand, this paper uses scenario simulation to show the size, direction and duration of the impact of economic development, black energy consumption and clean energy development on carbon prices in a more intuitive way.

The purpose of this paper is to identify the key drivers and challenges to supply chain collaboration in the humanitarian sector; to appraise the relationships between international non-governmental organizations (INGOs) and local non-governmental organizations (LNGOs) during disaster relief; and to explore the humanitarian context in regard to supply chain collaboration.

Literature from both the commercial and humanitarian sectors is discussed in the context of vertical partnerships. A Jordanian study spanning a network of 26 international and LNGOs is explored via semi-structured interviews.

The research provides valuable insights on the challenges facing LNGOs and INGOs when developing partnerships. Contextual factors, including host governmental policies and the social-economic setting of a disaster directly affect the motivations for supply chain collaboration between LNGOs and INGOs.

The research is built on interviewees with 30 humanitarian professionals working in one country during an extended crisis. The majority of the empirical data are only from one actor’s perspective, thus further research into dyadic and network relationships is required. Approaches to addressing the diverse cultural and decision-making perspectives of LNGOs and INGOs warrant further investigation.

Recognizing the motives and challenges to vertical partnerships between LNGOs and INGOs will assist the managers, both at the strategic and operational levels, to find solutions and evolve strategies to build effective partnerships. Compromise and consideration for partner’s drivers and cultural views are essential for effective joint humanitarian relief initiatives.

This paper extends supply chain collaboration to a humanitarian context. Overcoming the challenges facing collaborative efforts and complementary nature of the drivers provide a means to achieve effective partnerships. Despite the uniqueness of the humanitarian context, such as the secondary nature of cost and dynamic demand, the core principles of collaboration still hold.

The purpose of this paper is to examine whether cross-country differences in pensionable age explain such differences in economic activity of people at near-retirement age.

The empirical study uses regression models for macro-panel encompassing 21 European countries in the period 2008–2014.

Empirical results indicate that pensionable age is a determinant of cross-country differences in employment rate in the near-retirement age group, and less a factor differentiating average effective retirement age. It turns out that other factors matter, including salaries and wages as percentage of GDP (treated as a proxy for the occupational composition of populations across the countries studied), self-employment, participation in education and training, or self-perceived health.

The problem of economic activity at the near-retirement age is complex and cannot be limited to legal regulations concerning pensionable age. The policy aiming at stimulating the economic activity of the near-elderly should include actions on many sides including labour market, pension system, education, training, or health care.

The results complement studies based on the single-country approach and demonstrate that pensionable age does not account for cross-country differences in terms of average effective age of retirement when controlling for other factors. Moreover, factors differentiating effective retirement age and employments rates across countries studied are not similar.

The purpose of this paper is to present recent empirical results concerning offshoring and backshoring of manufacturing from and to Sweden, to increase the understanding of manufacturing relocation in an international context. In particular, extent, geographies, type of production, drivers, and benefits of moving manufacturing in both directions are investigated.

The study is based on survey data from 373 manufacturing plants. The same set of questions is used for both offshoring and backshoring between 2010 and 2015, which allows similarities and differences in decision-making and results between the two relocation directions to be identified.

There are many significant differences between offshoring and backshoring projects. Labour cost is the dominating factor in offshoring, as driver and benefit, while backshoring is related to many drivers and benefits, such as quality, lead-time, flexibility, access to skills and knowledge, access to technology, and proximity to R&D. This is also reflected in the type of production that is relocated; labour-intensive production is offshored and complex production is backshored.

Plants that have both offshored and backshored think and act differently than plants that have only offshored or backshored, which is why it is important to distinguish between these plant types in the context of manufacturing relocations.

The experience of Swedish manufacturing plants reported here can be used as a point of reference for internal manufacturing operations.

The survey design allows a unique comparison between offshoring and backshoring activity. Since Swedish firms in general have been quite active in rearranging their manufacturing footprint and have experience from movements in both directions, it is an appropriate geographical area to study in this context.

This study examines the adoption drivers of scale-appropriate mechanization in Nepal's maize-based farming systems. The authors also assess the contribution of scale-appropriate mechanization to the United Nations Sustainable Development Goals (SDGs) of zero hunger (SDG2) and no poverty (SDG1).

Propensity score matching and doubly robust inverse probability-weighted regression adjusted methods were applied to estimate the effects of mini-tiller adoption. These methods control the biases that arise from observed heterogeneities between mini-tillers users and nonusers.

The study findings show that farm size, labor shortages, draft animal scarcity, market proximity, household assets and household heads' educational level influence the adoption of mechanization in Nepal. Mechanized farms exhibited enhanced maize productivity, profits and household food self-sufficiency. Reduced depth and severity of poverty were also observed. Nevertheless, these effects were not uniform; very small farms (≤0.41 ha) facing acute labor shortages benefited the most.

The study results suggest that policymakers in developing nations like Nepal may wish to expand their emphasis on scale-appropriate mechanization to improve farm productivity and household food security, reduce poverty and contribute to the SDGs.

This first-of-its-kind study establishes the causal effects between scale-appropriate farm mechanization and SDG1 (no poverty) and SDG2 (zero hunger) in a developing nation.

The objective of this study is to reevaluate the correlation among pharmaceutical consumption, per capita income, and life expectancy across different age groups (at birth, middle age, and advanced age) within the OECD countries between 1998 and 2018.

We employ a two-step methodology, utilizing two independent approaches. Firstly, we con-duct the Dumitrescu-Hurlin pairwise panel causality test, followed by Machine Learning (ML) experiments employing the Causal Direction from Dependency (D2C) Prediction algorithm and a DeepNet process, thought to deliver robust inferences with respect to the nature, sign, direction, and significance of the causal relationships revealed in the econometric procedure.

Our findings reveal a two-way positive bidirectional causal relationship between GDP and total pharmaceutical sales per capita. This contradicts the conventional notion that health expenditures decrease with economic development due to general health improvements. Furthermore, we observe that GDP per capita positively correlates with life expectancy at birth, 40, and 60, consistently generating positive and statistically significant predictive values. Nonetheless, the value generated by the input life expectancy at 60 on the target income per capita is negative (−61.89%), shedding light on the asymmetric and nonlinear nature of this nexus. Finally, pharmaceutical sales per capita improve life expectancy at birth, 40, and 60, with higher magnitudes compared to those generated by the income input.

These results offer valuable insights into the intricate dynamics between economic development, pharmaceutical consumption, and life expectancy, providing important implications for health policy formulation.

Very few studies shed light on the nature and the direction of the causal relationships that operate among these indicators. Exiting from the standard procedures of cross-country regressions and panel estimations, the present manuscript strives to promote the relevance of using causality tests and Machine Learning (ML) methods on this topic. Therefore, this paper seeks to contribute to the literature in three important ways. First, this is the first study analyzing the long-run interactions among pharmaceutical consumption, per capita income, and life expectancy for the Organization for Economic Co-operation and Development (OECD) area. Second, this research contrasts with previous ones as it employs a complete causality testing framework able to depict causality flows among multiple variables (Dumitrescu-Hurlin causality tests). Third, this study displays a last competitive edge as the panel data procedures are complemented with an advanced data testing method derived from AI. Indeed, using an ML experiment (i.e. Causal Direction from Dependency, D2C and algorithm) it is believed to deliver robust inferences regarding the nature and the direction of the causality. All in all, the present paper is believed to represent a fruitful methodological research orientation. Coupled with accurate data, this seeks to complement the literature with novel evidence and inclusive knowledge on this topic. Finally, to bring accurate results, data cover the most recent and available period for 22 OECD countries: from 1998 to 2018.

Although networked hospitality businesses as Airbnb are a recent phenomenon, a rapid growth has made them a serious competitor for the hospitality industry with important consequences for tourism and for tourist destinations. The purpose of this paper is to analyse the nature of the phenomenon, its potential further development in the next five years and the impact this developments will have on tourism, on hotels and on city destinations.

A literature study, combined with scenario workshops and a Delphi panel, were used to map current trends and uncertainties. With this input, future scenarios were elaborated using the Global Business Network (“scenario cross”) method.

Network platforms as Airbnb are often classified under something called the “Sharing Economy”, a denomination that obscures their true nature. Airbnb is a challenging innovation to which traditional hospitality will have to respond. Its impact has at the same time led to a call for regulatory policies. The definition of these policies and the evolution of tourism are variables that determine future scenarios. Attempts to ban the phenomenon mean a disincentive to innovation and protect oligopolistic markets; more receptive policies may have the desired results if tourism grows moderately but in booming destinations they may lead to a harmful commercialization.

Until now, Airbnb has been described in conceptual studies about the so-called “Sharing economy”, or more recently in empirical studies about isolated effects of holiday rentals. This paper contextualizes the evolution of networked hospitality and seeks to synthesize the sum of its impacts, thus enabling businesses and local governments to define positions and strategies.

Fewer researchers have investigated the climatic and economic drivers of land-use change simultaneously and the interplay between drivers. This paper aims to investigate the nonlinear and interaction effects of price and climate variables on the rice acreage in high-latitude regions of China.

This study applies a multivariate adaptive regression spline to characterize the effects of price and climate expectations on rice acreage in high-latitude regions of China from 1992 to 2017. Then, yield expectation is added into the model to investigate the mechanism of climate effects on rice area allocation.

The results of importance assessment suggest that rice price, climate and total agricultural area play an important role in rice area allocation, and the importance of temperature is always higher than that of precipitation, especially for minimum temperature. Based on the estimated hinge functions and coefficients, it is found that total agricultural area has strong nonlinear and interaction effects with climate and price as forms of third-order interaction. However, the order of interaction terms reduces to second order after absorbing the expected yield. Additionally, the marginal effects of driven factors are calculated at different quantiles. The total area shows a positive and increasing marginal effect with the increase of total area. But the positive impact of price on the rice area can only be observed when price reached 50% or higher quantiles. Climate variables also show strong nonlinear marginal effects, and most climatic effects would disappear or be weakened once absorbing the expected rice yield. Expected yield is an efficient mechanism to explain the correlation between crop area and climate variables, but the impact of minimum temperature cannot be completely modeled by the yield expectation.

To the best of the authors’ knowledge, this is the first study to examine the nonlinear response of land-use change to climate and economic in high-latitude regions of China using the machine learning method.