Indices of innovation: application of Data Envelopment Analysis and Malmquist Index Analysis in the assessment of R&D efficiency in R&D-critical sectors

Maintaining or increasing R&D efficiency and productivity is a constant challenge for R&D-driven businesses, and companies in these sectors often explore strategies seen be effective in related sectors, for example the adoption of ‘open’ innovation by the pharmaceutical sector, based on its observed success in the information technology sector as reported by Chesbrough. The papers in this thesis address two gaps in the research literature: (1) the relative lack of established quantitative measures of the performance of open or other innovation strategies, and (2) the continuing challenge of assessing the effectiveness or otherwise of the OI paradigm outside its original high-tech industry focus. The pharmaceutical industry has been claimed as one of the pioneering industries where the principle of OI has been applied. In view of the limitations of prior research on R&D efficiency and OI in this industry, the question of whether OI is the best or only prescription for innovation in the pharmaceutical industry remains a strategic one. The first paper in the sequence identifies and explores systematic measures of innovation by investigating the adaptation and application of DEA as a candidate technique for analysing the R&D efficiency performance, using data on China’s high-tech industry sectors. The second paper explores how such ‘indices of innovation’ could be used to measure performance in terms of changes in R&D efficiency over time, in a case study of Procter and Gamble, a company widely recognised as an early adopter of OI. The third paper builds on the first two, using DEA and MI as ‘indices of innovation’ to measure whether adopting OI is leading to increased R&D efficiency in the pharmaceutical sector. Taken together, these papers explore (a) the feasibility if DEA and MI as new quantitative econometric ‘indices of innovation’, (b) their correlation with a known case of open innovation, and (c) to test the hypothesis that open innovation is increasing R&D efficiency in the pharmaceutical industr

Stimulating innovation on social product development: an analysis of social behaviors in online innovation communities

With firms' increasing adoption of social product development strategies, such as mass collaboration and crowdsourcing, online users are actively participating in the development of new products and services via social media platforms. Online innovation communities (OICs), one representative of such social media platforms, have been used by large firms to collect ideas from their users and facilitate the product development process. While it is extensively studied that product ideas with high popularity on OICs are of great importance to the product development, research on what social behaviors of online users lead to the high popularity is largely unclear. This paper conducts an empirical study by collecting a large-scale, quantitative dataset from an OIC between 2014 and 2018. With the analysis of users' online idea posting and commenting behaviors, our results reveal that the idea contribution experience, together with comment diversity positively in uence the overall popularity of an individual's ideas, while the motivation of providing comments is negatively related. Moreover, user's innovation capability poses a positive effect on both overall and average popularity of ideas. These findings can help firms better incentivise their users on OICs to improve the effectiveness and efficiency of social product development

Stimulating innovation: managing peer interaction for idea generation on digital innovation platforms

This study investigates user behaviours in online innovation communities which are enabled by digital technologies, to obtain an understanding of the relationship between user’s social interaction and their innovation contribution. The new type of innovation communities enable firms to crowdsource ideas from their users for developing new products and improving existing ones, and to facilitate the interactions among users. From an empirical study which collects a large-scale, quantitative data set from Microsoft’s Idea platform of Business Intelligent products, this paper focuses on the amount and diversity of users’ social interaction particularly their commenting behaviours on the platform, and uses the number of posted ideas and the number of implemented ideas to capture users’ contribution to the firm’s innovation development. The findings indicate that the amount of user interaction is positively related to the number of implemented ideas, but has an inverted U-shaped relationship with idea number. Moreover, diverse user interaction encourages idea posting, but is negatively associated with the number of implemented ideas. The findings should provide managerial guidance to firms on incentivizing and managing user interaction in online communities in order to improve firms’ innovation development

Revealing industry challenge and business response to Covid-19: a text mining approach

Purpose This study aims to conduct a ‘real-time’ investigation with user-generated content on Twitter to reveal industry challenges and business responses to the Covid-19 pandemic. Specifically, using the hospitality industry as an example, the study analyses how Covid-19 has impacted the industry, what are the challenges and how the industry has responded. Design/methodology/approach With 94, 340 tweets collected between October 2019 and May 2020 by a programmed web scraper, unsupervised machine learning approaches such as structural topic modelling are applied. Findings The results show that: (1) despite the adverse consequences from the pandemic, the hospitality industry has shown increasing interests in finding ways to survive, such as looking into novel technologies and adopting new business strategies; (2) the pandemic has created an opportunity for organisations to jump out from their daily business operations and rethink about the future development of the industry; (3) the Covid-19 impact is not only shown on the reduction in the job demand but also a change in the demand structure of the job market; (4) the use of novel text mining approaches on unstructured social media data is effective in identifying industry-level challenge and response to public emergencies. Originality This study contributes to the literature on business response during crises providing for the first time a study of utilising unstructured content on social media for industry- level analysis in the hospitality context

Research on the influence mechanism of organic food attributes on customer trust

Based on the quality level that consumers can discover at various stages, the literature summary divides organic food attributes into three categories: trust, search, and experience. This paper deeply analyzes the internal relationship among the search attribute, trust attribute, and per-ceived quality and the mechanism of effect on customer trust. After distributing and collecting 310 consumers’ valid questionnaires, the research hypotheses were empirically tested utilizing a structural equation model and mediation effect test. The research results indicate that: (1) The food safety attribute and nutritional content attribute in the organic food trust attribute have positive effects on the perceived quality and customer trust. (2) The price and label in the organic food search attribute positively affect the perceived quality, i.e., the price harms customer trust, while the label has no significant effect on customer trust. Perceived quality plays a mediating role be-tween the trust attributes, search attribute, and customer trust, i.e., the price and label indirectly affect customer trust through perceived quality. (3) The perceived quality of organic food positively affects customer trust. The results provide an important theoretical basis for enterprises to implement effective strategies to enhance consumers’ trust in organic food

The ups and downs of open innovation efficiency: the case of Procter & Gamble

Open innovation (OI) has become increasingly popular as an enterprise strategy in both industry and academia, and has been adopted, at least in part, by many companies. Despite this popularity, there is a dearth of evaluation of OI efficiency and a lack of suitable quantitative indices. In this study, we used both Data Envelopment analysis (DEA) and Malmquist techniques to compare the pre- and post-transition levels of performance achievement of Procter&Gamble (P&G), a widely recognised and public early adopter of OI, with a group of its main competitors. Most detailed analysis of the time-course revealed that the innovation efficiency of P&G improved rapidly and substantially after its embracing of OI, an effect we term the ‘open rise’. However, there is also a transient decline in R&D efficiency at the beginning of OI adoption (‘open dip’) and an unexpected and marked decline (‘open drop’) after the peak positive effect

Exploring user-generated content related to vegetarian customers in restaurants: an analysis of online reviews

The purpose of this research is to explore and evaluate factors that impact the dining experience of vegetarian consumers within a range of vegetarian-friendly restaurants. To explore the factors and understand consumer experience, this study analyzes a vast number of user-generated contents of vegetarian consumers which have become vital sources of consumer experience information. This study utilizes machine learning techniques and traditional methods to examine 54,299 TripAdvisor reviews of approximately 1,008 vegetarian-friendly restaurants in London. The study identifies 20 topics that represent a holistic opinion influencing the dining experience of vegetarian customers. The results suggest that “friendly staff” is the most popular topic, and has the highest topic percentage. The results of regression analyses reveal that six topics have a significant impact on restaurant ratings, while thirteen topics have negative impacts. Restaurant managers who pay close attention to vegetarian aspects may utilize the findings of this paper to better satisfy vegetarian consumer demands and formulate appropriate training courses

Big data, big challenges: risk management of financial market in the digital economy

Purpose– The purpose of the research is to assess the risk of the financial market in the digital economy through the quantitative analysis model in the big data era. It’s a big challenge for the government to carry out financial market risk management in the big data era. Design/methodology/approach– In this study, a generalized autoregressive conditional heteroskedasticity-vector autoregression (GARCH-VaR) model is constructed to analyze the big data financial market in the digital economy. Additionally, the correlation test and stationarity test are carried out to construct the best fit model and get the corresponding VaR value. Findings– Owing to the conditional heteroscedasticity, the index return series shows the leptokurtic and fat tail phenomenon. According to the AIC (Akaike Information Criterion), the fitting degree of the GARCH model is measured. The AIC value difference of the models under the three distributions is not obvious, and the differences between them can be ignored. Originality/value– Using the GARCH-VaR model can better measure and predict the risk of the big data finance market and provide a reliable and quantitative basis for the current technology-driven regulation in the digital economy

Simulation of manufacturing scenarios’ ambidexterity green technological innovation driven by inter-firm social networks: based on a multi-objective model

The mechanism of the impact of inter-firm social networks on innovation capabilities has attracted much research from both theoretical and empirical perspectives. However, as a special emerged and developing complex production system, how the scenario factors affect the relationship between these variables has not yet been analyzed. This study identified several scenario factors which can affect the firm’s technological innovation capabilities. Take the manufacturing scenario in China as an example, combined with the need for firms’ ambidexterity innovation and green innovation capability, a multi-objective simulation model is constructed. Past empirical analysis results on the relationship between inter-firm social network factors and innovation capabilities are used in the model. In addition, a numerical analysis was conducted using data from the Chinese auto manufacturing industry. The results of the simulation model led to several optimization strategies for firms that are in a dilemma of development in the manufacturing scenario

Evaluating R&D investment efficiency in China's high-tech industry

Research and development (R&D) investment activity plays a crucial role in developing high-tech industries. In recent decades, China has made sustained investments in its domestic high-tech industries, with the goal of increasing their productivity. This paper investigates the effect of this investment on relative R&D efficiency across China's high-tech sectors. Data Envelopment Analysis (DEA) was used to generate quantitative indices for sector comparisons. The analysis of this study indicates that overall R&D investment efficiency did not increase from 1998 to 2009, despite R&D expenditure increasing by 2188%. Over the same period, most sectors suffered from decreasing returns to scale (DRS), presumably also reflecting the inefficient R&D investment. Most of the sectors showed significant fluctuation on R&D investment efficiency. This research result indicates that the problem of China's high-tech industry may be from the inefficiency of its technology commercialization processes, and therefore represents a critical parameter for policy makers and managers