The Culture for Open Innovation Dynamics

Digital Economy, R&D Investment, and Regional Green Innovation—Analysis Based on Provincial Panel Data in China

The digital economy plays an important role in promoting regional green innovation. Based on the panel data of 30 provincial administrative regions in mainland China (except Tibet) from 2011 to 2018, this paper constructs a comprehensive development index of the digital economy and explores the potential path of the digital economy affecting regional green innovation through factor analysis and regression analysis. The results show the following: Firstly, the digital economy can effectively promote regional green innovation capability. The causal relationship is mainly realized through scientific research funds and human resources. Secondly, in terms of regional heterogeneity, the role of the digital economy in promoting green innovation and R&D investment in eastern China is stronger than that in central and western China. Thirdly, further analysis showed that the digital economy has a significant nonlinear influence on regional green innovation capability. This feature is mainly reflected in the influence of R&D personnel on regional green innovation. Therefore, the rational allocation of R&D resources is conducive to the development of regional green innovation. Finally, it is suggested to improve the two mechanisms of R&D funds and personnel investment to actuate regional green innovation development.

DYNAMICS OF ATTRIBUTE-SPECIFIC CUSTOMER REQUIREMENTS IN INNOVATION PROCESSES: A PANEL ANALYSIS CONSIDERING KANO’S THEORY

This study provides new insights into the dynamics of attribute-specific customer requirements in innovation processes. For this purpose, a panel study with three survey time points was conducted based on the Kano model. These time points were localised before and after the launch of a novel scientific online platform. In contrast to previous research, subjects’ responses were linked across the three measurement time points. This matching of the respondents enabled the identification of segments of homogeneous time-dependent shifts in customer requirements and their description based on sociodemographic, acceptance- related, and usage-based characteristics. The results provide academics and managers with practical implications and avenues for future research.

Developing an Open Innovation Attitude Assessment Framework for Organizations: Focusing on Open Innovation Role Perspective and Locus of Activity

From an organizational perspective, open innovation (OI) capability assessments are becoming increasingly important. The authors propose that an organization’s attitude toward interactive OI activities among OI stakeholders can reveal its degree of capability. This paper aims to focus on an organization’s OI attitude measurement scales and develop a framework linked to the role perspectives and loci of OI activities occurring at the organizational level. This research will introduce a practical, theory-based indication of OI assessment by combining a deductive process that identifies organizational OI attitude constructs with an inductive framework development process. First, the authors conducted an extensive literature review of attitude measurement on the execution of OI. Then, they performed empirical data analysis using a large-scale structured attitude assessment survey from individuals in domestic and multi-national corporations (n = 134), which led to the development of questionnaire sets on attitude evaluation. This study contributes to developing an organizational OI attitude assessment scale. Furthermore, based on empirical data analysis, the research framework demonstrated the reliability and validity of the organizational OI attitude measurement scale. Specifically, the scale contains proven questionnaires assessing OI attitudes by interrogating individual actors’ impact, behavior, and cognition regarding their organization’s OI activities. The organization’s three role perspectives (transfer, absorption, and brokerage) and two loci of activities in the OI ecosystem provide six distinct dimensions, suggesting areas of focus for a firm’s strategic OI direction.

Designing for Green and Grey: Insights from Single-Use Plastic Water Bottles

Recognizing modern society’s multiple risks, this study examines single-use plastic water bottles at the intersection of environmental degradation and societal carelessness for the elderly. While prioritising economic profits and plastic waste, we have neglected bottles’ typical poor openability for older people. Thus, we evaluated the openability of bottles with environmentally friendly and socially inclusive designs in South Korea by comparing older and younger adults’ experiences. Integrating different attributes than existing studies that analyse opening torque or one-handed opening, the test results show that older adults experience the poorest two-handed openability when bottles have both a weight thickness lower than 14.42 g and an easily squeezable bottle structure. In South Korea, companies advocate eco-friendliness by valuing lighter weight with less plastic and support user-friendliness by adopting easily squeezed sidewall patterns, while the industry maintains broad opening torque regulations; however, we show this combination exceeds older users’ capabilities. That is, for openability, bottles need to keep a weight thickness greater than 12 g, abandon easily squeezed sidewall patterns or reduce the opening torque regulation range to 100 N-cm. These socially favourable but ecologically adverse measures will be sustainable when the efficient linear economy transitions to an effective circular economy.

Open Innovation Competence for a Future-Proof Workforce: A Comparative Study from Four European Universities

Digital transformation is a key driving force of open innovation to capture and transfer knowledge inside and outside of a company’s bounds. New challenges in organizing multiple knowledge flows imply the need for increased competences related to this paradigm of future employees. In this article, we organize and aggregate the competencies required for open innovation collaboration and develop a competence profile that organizes individual competencies in an open innovation context. Based on elements of the European Entrepreneurship Competence Framework, we delineate an OI Competence Profile and list the core competencies to manage and accelerate the inflow and outflow of knowledge. We explore this profile by comparing data from 2332 students from four European universities to find differences in the distribution of OI competencies between countries. The study contributes to understanding the individual competencies that target the future OI needs of companies necessary in the context of digital transformation. It also introduces an interdisciplinary approach to integrate the research streams of management practice, open innovation, and entrepreneurial education.

Identifying Digital Transformation Paths in the Business Model of SMEs during the COVID-19 Pandemic

The objective of this study was to analyze how small and medium enterprises (SMEs) cope with environmental changes due to the COVID-19 pandemic by pursuing the business model transformation with the support of digital technologies. To achieve the objective, this study used a multiple case study design with qualitative analysis to examine the data obtained from interviews, observation, and field visits. Seven manufacturing SMEs from Indonesia were selected using a theoretical sampling technique, with the purpose of achieving some degree of variation to allow us to undertake replication logic. Our analysis demonstrates that SMEs adopt a different degree of digital transformations, which can be summarized into three paths, depending on the firms’ contextual factors. First, SMEs with a high level of digital maturity who respond to the challenges by accelerating the transition toward digitalized firms; second, SMEs experiencing liquidity issues but a low level of digital maturity who decide to digitalize the sales function only; and, third, the SMEs that have very limited digital literacy but are supported by a high level of social capital. This last group of firms solves the challenges by finding partners who possess excellent digital capabilities. The qualitative case study method allows us to conduct in-depth and detailed analysis, but has thin generalizability. To address this limitation, future research can use a survey covering various industries to test the proposed theory that has resulted from this study, so that the generalizability can be assured.

Corporate Social Responsibility during COVID-19 Pandemic

The health, economic, and social consequences of the SARS-CoV-2 virus have highlighted the need for collaboration among all agents to face a scenario that we have not before seen. The aims of this paper are to analyze the involvement that large Spanish companies have shown during the toughest moments of the epidemic and to determine the objectives these companies have pursued with them. The results show that several firms have shown a great commitment with society, developing actions that alleviate the consequences of the COVID-19 like others have developed several strategies with different objectives. More concretely, three clusters of responsibility have been identified: (i) protecting only the interests of shareholders and investors; (ii) favoring the wellbeing of the Spanish society in general and vulnerable groups in particular; and (iii) combining the previous altruistic actions with commercial interests.

Stock Market Reaction to COVID-19: Evidence in Customer Goods Sector with the Implication for Open Innovation

The coronavirus pandemic has spread all over the world, affecting both the health and economic sectors. The aim of this research was to observe stock prices of customer goods before and after the COVID-19 pandemic using event study and the comparison test. The sample included data of daily closing stock prices and volume of stock trade during the three months before (−90 days) and after (+90 days) the occurrence of the COVID-19 pandemic ongoing, totaling 2670 observation data both before and after the COVID-19 pandemic, for a total of 5340. The research findings indicate a significant difference between the daily closing stock price and volume of stock trade before and after the COVID-19 pandemic. The current research has both theoretical and practical implications: the findings strengthen the efficient market hypothesis, which states that the more complete the provided information, the more efficient the market. The practical implication is that investors should be careful when choosing to invest. Investors should choose customer goods sector companies that provide products that are much needed by customers, for example, pharmacy, food, beverages, etc. Future research is needed to investigate the long-term impact of the pandemic on the economy.

COVID-19 Vaccine Development in a Quadruple Helix Innovation System: Uncovering the Preferences of the Fourth Helix in the UAE

Successful development and uptake of vaccine technology in a Quadruple Helix Innovative health or economic system requires a clear understanding of society’s preferences as the fourth helix. With significant financial commitments to find a safe and effective COVID-19 vaccine still ongoing, this study introduces a random utility theoretic behavioral health model to analyze individuals’ prospective demand for the vaccine in the United Arab Emirates (UAE). To this end, we use a cross-sectional sample of stated vaccine preferences data collected online using the snowball method, between 4 July and 4 August 2020, gathering 1109 responses across all seven Emirates of the UAE. We found that in addition to socio-economic and demographic influences, the factors affecting individuals’ preferences for the prospective COVID-19 vaccine in the UAE include those put forth by the WHO’s SAGE group on immunization. Though the estimated indirect cost, in the form of expected marginal utility of time spent to get the vaccine is not statistically significant, the expected marginal utility of every dirham spent to get the vaccine is −1.76 AED and significant, suggesting a significant expected dis-utility from COVID-19 vaccine seeking/payment by the average person. Our findings also highlight significant perceived financial, temporal and spatial barriers to COVID-19 vaccine uptake in the UAE. Therefore, a set of measures are suggested to help mitigate the adverse effects of these three constraints. Our study thus contributes methodologically to the literature on vaccine demand, hesitancy and development. It also contributes to the nascent empirical evidence on the novel coronavirus disease, by providing significant insights for evidence based policy making that should increase the effectiveness of any prospective COVID-19 vaccination program in the UAE.

Management of Competitiveness of Metropolis Public Transport in the COVID-19 Pandemic Based on Core Consumers’ Values

The aim of the paper focuses on generating insights into the competitiveness of public transport in Moscow City in relation to the use of personal cars, considering individual basic consumer values and environmental factors, including the impact of the COVID-19 pandemic. The research question in this study is: how can we assess the main indicators of public transport’s competitiveness, which can motivate personal car owners with specific basic values to change the transportation mode for personal mobility? This conceptual research model combines the content analysis of publications and large-scale online survey of Moscow City’s car owners—potential consumers of public transport services—on a representative quota sample of 1263 respondents. The methodology includes the axiological approach for the identification of the main factors of consumer behavior of personal car owners in the city with combinations of the modified Schwartz’s methodology of the basic individual values evaluations. Secondary data manifested the changes in the behavior of car owners due to the fact that they value metropolitan environmental degradation and changed their transportation preferences to public transport even during the COVID-19 pandemic. Based on the factor and cluster analysis of the value profile of the individuals, seven clusters of car owners were identified, and two large clusters were selected as target segments for public transport services. The conclusion was made that it is necessary to differentiate social and marketing programs to promote the competitiveness of public transport in the minds of consumers.

Influence of Passenger Flow at the Station Entrances on Passenger Satisfaction Amid COVID-19

Railway stations are centers of mass accumulation of people. Additional regulations change the intensity of the flow of visitors and the time of entering the station. Delays become an essential factor affecting the perceived satisfaction with all services at stations. This paper analyzes the impact of the intensity of passenger flow in the key areas of the station (entrance groups) on the satisfaction of visitors with station functioning (by the example of Moscow railway stations). The authors of the paper used methods of observation to measure the current passenger flows at the entrances to the station and also collected secondary data on transport flows at city railway stations. To predict passenger flow, the authors used statistical methods to assess the relationship between variables and regression. The paper used a survey of passengers to analyze satisfaction with the functioning of railway station facilities and assess the acceptable delay time at the entrance/exit to the station. The article substantiates that in the context of current requirements for transport security, an important factor for perceived satisfaction with station services is the forecasting and management of passenger flows and control of time of entering the station.

Open Innovation 4.0 as an Enhancer of Sustainable Innovation Ecosystems

Innovation matters. Business success increasingly depends upon sustainable innovation. Observing recent innovation best practices, the emergence of a new paradigm is traceable. Creating an innovative ecosystem has a multilayer effect: It contributes to regional digitalization, technological start-up emergence, open innovation promotion, and new policy enhancement retro-feeding the system. Public policy must create open innovation environments accordingly with the quintuple helix harmonizing the ecosystem to internalize emerging spillovers. The public sector should enhance the process, providing accurate legal framework, procurement of innovation, and shared risks in R&D. Opening the locks that confine the trunks of community, academic, industry, and government innovation will harness each dimension exploiting collective and collaborative potential of individuals towards a brighter sustainable future. In this sense, the aim of this study is to present how open innovation can enhance sustainable innovation ecosystems and boost the digital transition. For that, firstly, a diachronic perspective of the sustainable innovation ecosystem is traced, its connection to open innovation, and identification of the university linkages. Secondly, database exploration and econometric estimations are performed. Then, we will ascertain how far open innovation frameworks and in particular the knowledge flows unveiled by the university promote smart and responsible innovation cycles. Lastly, we will propose a policy package towards green governance, empowering the university in governance distributed ecosystem, embedded in the community, self-sustained with shared gains, and a meaningful sense of identity.

Predictive Scenarios of the Russian Oil Industry; with a Discussion on Macro and Micro Dynamics of Open Innovation in the COVID 19 Pandemic

The decrease in the economic activity level around the world due to the COVID-19 pandemic spread has led to a sharp decrease in the crude oil price and provoked an oil war outbreak in the global energy market. The current situation has provoked the need for a total decrease in the crude oil production in the world. Considering that Russia is one of the main oil exporters on the world market, the need to determine the supply and demand levels for Russian oil is becoming relevant. The aim of the paper is to model predictive scenarios of Russian oil industry development, considering the specifics of the current economic environment given the COVID-19 pandemic. The multifactor correlation modeling method was used to form the system of indicators determining the level of demand and supply for Russian oil used and the total level of their influence. The functions determine the probability of implementing various scenarios of oil industry development depending on the predicted values of demand and supply. The three-sigma rule and the fuzzy sets method were used to estimate three scenarios of oil industry development for 2020–2021. Changes in revenues of the industry under the influence of forecast indicators of supply and demand for oil have been assessed and the probability of implementation of each of the scenarios has been reasoned. The results obtained are of a practical nature and can be used by government agencies, financial intermediaries, and scientists to diagnose Russian oil industry development. The results will be useful for oil companies to develop a strategy of open innovations for further design of the scientific information field for the effective functioning of the industry in complete uncertainty conditions.

A Predictive Model for Assessing the Impact of the COVID-19 Pandemic on the Economies of Some Eastern European Countries

The paper seeks to develop a predictive model for assessing the impact of the (COVID-19) pandemic on the economies of Eastern Europe, taking into account quarantine measures. Functions of the dependence on the number of the infected populations in Eastern Europe on pandemic duration were calculated based on trend analysis. Factors affecting the intensity of disease and the number of infected persons have been determined. Integral model of their influence has been built using regression analysis. Based on the values of the factors, the number of infected people and the rate of infection were predicted for each of the Eastern European countries. The prognostic duration of the stage of exponential disease growth and the total duration of quarantine (epidemiological saturation point) are substantiated. The predicted decline in Eastern European GDP due to COVID-19 has been estimated based on the construction of a prognostic regression model. The results obtained can be used by state authorities and economic agents as a tool for active and preventive response. They can also serve as an example of the urgent need to develop, especially in non-standard situations, mechanisms and products of open innovation.

The Challenge of the Energy Sector of Russia during the 2020 COVID-19 Pandemic through the Example of the Republic of Tatarstan: Discussion on the Change of Open Innovation in the Energy Sector

Today the world economy is handling an economic crisis caused by the pandemic of a new coronavirus infection (COVID-19), announced by the WHO, as well as by fluctuations in the international energy market and by the development of “green energy”. The crisis, named the “2020 crisis”, is notable for the divergence of the sectoral dynamics of development, with a declining development trend in some industries and the rapid growth of others. The crisis is etymologically relevant to the energy sector, a sector which is important for the survival and specialization of the Russian Federation. This research is aimed at describing the status and highlighting the socioeconomic effects, constraints (economic and social risks), and “growth points” of the energy sector in the modern realities of the 2020 crisis. Methods. The method of in-depth interview was used. The need for studying expert opinion is based on the fundamental principles of implementation research, according to which the success of state sectoral policy depends on its perception and support by stakeholders. Top managers of energy enterprises acted as the experts (N = 10). The interviews were conducted in April 2020. The method of incremental approximation and coding (open and axial) were used for a high-quality discourse analysis. Results. The conducted study revealed divergence of sectoral dynamics of energy consumption, which is a translation of the immanent features of the 2020 crisis into the energy sector. The detected constraints are reserved development prerequisites of “green energy” and reduced investments in development programs. The potential “growth points” include intensified digitalization of the energy sector and the need for institutional changes in taxation in the energy sector, recognized by experts.

Hybrid Deep Learning Algorithm with Open Innovation Perspective: A Prediction Model of Asthmatic Occurrence

Due to recent advancements in industrialization, climate change and overpopulation, air pollution has become an issue of global concern and air quality is being highlighted as a social issue. Public interest and concern over respiratory health are increasing in terms of a high reliability of a healthy life or the social sustainability of human beings. Air pollution can have various adverse or deleterious effects on human health. Respiratory diseases such as asthma, the subject of this study, are especially regarded as ‘directly affected’ by air pollution. Since such pollution is derived from the combined effects of atmospheric pollutants and meteorological environmental factors, and it is not easy to estimate its influence on feasible respiratory diseases in various atmospheric environments. Previous studies have used clinical and cohort data based on relatively a small number of samples to determine how atmospheric pollutants affect diseases such as asthma. This has significant limitations in that each sample of the collections is likely to produce inconsistent results and it is difficult to attempt the experiments and studies other than by those in the medical profession. This study mainly focuses on predicting the actual asthmatic occurrence while utilizing and analyzing the data on both the atmospheric and meteorological environment officially released by the government. We used one of the advanced analytic models, often referred to as the vector autoregressive model (VAR), which traditionally has an advantage in multivariate time-series analysis to verify that each variable has a significant causal effect on the asthmatic occurrence. Next, the VAR model was applied to a deep learning algorithm to find a prediction model optimized for the prediction of asthmatic occurrence. The average error rate of the hybrid deep neural network (DNN) model was numerically verified to be about 8.17%, indicating better performance than other time-series algorithms. The proposed model can help streamline the national health and medical insurance system and health budget management in South Korea much more effectively. It can also provide efficiency in the deployment and management of the supply and demand of medical personnel in hospitals. In addition, it can contribute to the promotion of national health, enabling advance alerts of the risk of outbreaks by the atmospheric environment for chronic asthma patients. Furthermore, the theoretical methodologies, experimental results and implications of this study will be able to contribute to our current issues of global change and development in that the meteorological and environmental data-driven, deep-learning prediction model proposed hereby would put forward a macroscopic directionality which leads to sustainable public health and sustainability science.