Evaluating industrial competitiveness at the office of technology assessment

When the Office of Technology Assessment (OTA) began to study the competitive standing of US industries in 1978, no one had a very clear idea of how to do this. Economists tended to rely on output-side indicators — trade balances and market shares, employment and profit levels. In OTA's view, these were often no more than symptoms. The need was to get at causes — to grasp the dynamics of change in industries as varied as microelectronics and steel and to sort long-run competitive trends from short-term fluctuations. This means understanding the internal workings of firms and industries: how business decisions are made, where investment capital comes from, how new products are developed. While retaining an overall comparative advantage framework, OTA has therefore stressed factors that affect competitiveness as inputs. Among these are product and process technologies, choice of business strategies, and labor force characteristics. To some extent, all are sector-specific and firm-specific. Direct and indirect impacts of federal policies — trade measures, taxes, regulations — have also been central concerns, along with foreign industrial policies. OTA's studies of competitiveness have been subject to extensive external reviews. In the end, however, the critical step has been the development of an independent capability within OTA for weighing the factors that affect competitiveness.     

Induced technological change and the timing of public R&D investment in the Japanese electricity sector considering a two-factor learning curve

The UNFCCC has stated that energy policies and measures to address climate change should be cost-effective to ensure global benefits at the lowest possible cost. To mitigate the bulk of carbon emission from the electricity sector, a large market penetration of renewable energy technologies with as low cost as possible is a key research topic. The energy-related R&D policy in Japan aims to achieve a green economy. In our study, based on this context, we demonstrated how to optimize the timing of public R&D investment within the framework of a bottom-up partial equilibrium model. The developed optimization model represents the Japanese electricity sector and minimizes the total system cost subject to an accumulated carbon emission constraint. Our main research focus is the role of R&D activity, especially in the innovation stage of renewable technologies. We employ a two-factor learning curve and quantify the impact of the learning effect on the dynamic diffusion of major renewable technologies. The study shows a dynamic technology transition in the Japanese electricity sector and the optimized R&D investment schedule for each renewable technology. With the first-best energy policy, an R&D budget of more than 2000 million USD would be allocated to PV in 2050, which corresponds to 45% of the energy-related Japanese R&D budget in 2050. Because some have criticized the uncertainty problems with dynamic simulations and learning models, supplemental sensitivity analyses are included.     

Analysis of investments in autonomous maintenance activities

In this paper, we model the situation where operator maintenance activities improve the failure process of equipment. We analyze the business decision to reduce both the mean and variance of the production cycle time and the overall inventory level through an investment in planned autonomous maintenance. We answer: (i) when do optimal autonomous maintenance decisions most improve inventory levels?; and (ii) how do capacity restrictions, equipment characteristics the maintenance response function, and product characteristics impact the autonomous maintenance investment decision? Extensive numerical analyses are performed to develop an approximation to the optimal response for both inventory and autonomous maintenance investments over a wide range of problem parameters. Our solutions provide guidelines on how much time should be invested in autonomous maintenance activities and describe when companies can most benefit from autonomous maintenance programs that increase equipment reliability. We determine the investment in autonomous maintenance activities as a function of available capacity, equipment reliability and demand characteristics.     

Supply chain capacity and outsourcing decisions: the dynamic interplay of demand and supply uncertainty

We study the interplay of demand and supply uncertainty in capacity and outsourcing decisions in multi-stage supply chains. We consider a firm's investment in two stages of a supply chain (Stage 1 models the “core” activities of the firm, while Stage 2 are the “non-core” activities). The firm invests in these two stages in order to maximize the multi-period, discounted profit. We consider how non-stationary stochastic demand affects the outsourcing decisions. We also consider how investment levels are affected by non-stationary stochastic supply when the market responds to the firm's investments. We characterize the optimal capacity investment decisions Tor the single- and multi-period versions of our model and focus on how changes in supply and demand uncertainly affect the extent of outsourcing. We find that as the responsiveness of the market to investments made by the firm increases, the reliance on outsourcing generally increases. While greater supply and greater demand have the expected effect on investments, decreases in variability are not as straightforward. Greater supply uncertainty increases the need for vertical integration while greater demand uncertainty increases the reliance on outsourcing. In the multi-period model, we find that the nature of adjustments in capacity based on changes in demand or supply follows from the comparative statics of the single-period model, although whether outsourcing increases or decreases depends on the costs of adjusting capacity.     

Joint decisions on inventory replenishment and emission reduction investment under different emission regulations

Carbon emission regulation policies have emerged as mechanisms to control firms’ carbon emissions. To meet regulatory requirements, firms can make changes in their production planning decisions or invest in green technologies. In this study, we analyse a retailer’s joint decisions on inventory replenishment and carbon emission reduction investment under three carbon emission regulation policies. Particularly, we extend the economic order quantity model to consider carbon emissions reduction investment availability under carbon cap, tax and cap-and-trade policies. We analytically show that carbon emission reduction investment opportunities, additional to reducing emissions as per regulations, further reduce carbon emissions while reducing costs. We also provide an analytical comparison between various investment opportunities and compare different carbon emission regulation policies in terms of costs and emissions. We document the results of a numerical study to further illustrate the effects of investment availability and regulation parameters.     

The industrial impact of the biological revolution

The author addresses the broad impact biotechnology holds for many traditional industries. He explains how the current biotechnology industry evolved from older technologies, such as fermentation, and blended with new findings in molecular biology. Describing how this merger results in lower costs for raw materials, labor and capital investment, the author then summarizes the near-term and long-range industrial prospects for the current genetic engineering industry. Finally, he speaks to the potential accomplishments the custom design of proteins and enzymes might bring to product development.     

Mining government tweets to identify and predict citizens engagement

The rise of social media offered new channels of communication between a government and its citizens. The social media channels are interactive, inclusive, low-cost, and unconstrained by time or place. This two-way communication between governments and citizens is referred to as electronic citizen participation, or e-participation. E-participation in the age of technology is considered as a mean for citizens to express their opinions and as a new input to be integrated by policy makers to take decisions. Governments and policy makers always aim to increase such participation not only to utilize public expertise and experience, but also to increase the transparency, trust, and acceptability of government decisions. In this research we investigate how governments can increase citizens e-participation on social media. We collected 55,809 tweets over a period of one year from Twitter accounts of a progressive government in the Arab world. This was followed by statistical analysis of posts characteristics (Type, Day, Time) and their impact on citizens' engagement. Then, we evaluated how well can different machine learning techniques predict user engagement. Results of the statistical analysis confirmed that post type (video, image, link, and status) impacted citizens' engagement, with videos and images having the highest positive impact on engagement. Furthermore, posting government tweets on weekdays obtained higher citizens’ engagement than weekends. Conversely, time of post had a weak effect on engagement. The results from the machine learning experiments show that two techniques (Random Forest and Adaboost) produced more accurate predictions, particularly when tweet textual contents were also used in the prediction. These results can help governments increase the engagement of their citizens.     

A new game of information sharing and security investment between two allied firms

In consideration of business information sharing, this paper investigates a new game of information sharing and security investment between two allied firms. Firms’ strategies in three decision models (Nash Equilibrium decision, partially centralised decision and totally centralised decision) are analysed. We provide some quantitative analyses on how some parameters affect firms’ decisions in the three decision models. Our Nash Equilibrium analysis shows that when firms make decisions individually, they will share no information with each other. When information sharing is determined by a social planner, firms will share some information with each other, which increases the risk of information leakage. Thus, firms should increase their security investments to mitigate the higher information leakage risk. However, our analysis shows that instead of investing more in information security, firms will reduce their security investment, which will further aggravate the risk. Hence, a social planner is required to designate the security investments and information sharing levels for both firms. Our theoretical analysis shows that firms’ strategies can achieve global optimality in the totally centralised decision model. Furthermore, a numerical experiment is conducted and the result demonstrates that totally centralised decision model is more efficient than the other two decision models. At last, we propose two compensation mechanisms to help firms coordinate their strategies when making decisions individually.     

Patent analysis of wind energy technology using the patent alert system

Using publicly available information effectively is important to remain competitive in technology related industries. The main difficulty in this is determining how to use the information effectively and in a manner that will yield results that can be acted upon. Several different methodologies are being developed in the Technology Watch area of research including the Patent Alert System (PAS) by Dereli and Durmusoglu. By using two different variations of the Patent Alert System, this paper will analyze two different technologies based on wind energy. These variations include Linear Regression based PAS and Fuzzy Logic based PAS. Each approach uses a different methodology to evaluate the available data and generate a trend that will be used to predict future values of patent counts in the applied area of technology. The results of these different approaches are compared in order to determine if either method produces more reliable results which would then lead to better decisions by the organization. In order to connect the results with real-world events, trend changes will be evaluated against global events which should have an impact on technological development in this area.     

Investing in Quality Under Autonomous and Induced Learning

The reduction of variability in product performance characteristics is an important focus of quality improvement programs. Learning is intrinsically linked to process improvement and can assume two forms: (i) autonomous learning; and (ii) induced learning. The former is experientially-based, while the latter is a result of deliberate managerial action. Our involvement in quality and capacity planning with several major corporations in different industries suggested that it would be instructive to devise a model that would prescribe an optimal combination of autonomous and induced learning over time to maximize process improvement. We thus propose such a model to investigate the optimal quality improvement path for a company given that quality costs depend on both autonomous and induced types of learning experienced on a number of quality characteristics. Several properties of an optimal investment path are developed for this problem. For example, it is shown that decisions maximizing short-term gains may actually lead to suboptimal resource utilization decisions when total costs associated with a longer planning horizon are taken into account. Numerical examples are used to assess the sensitivity of the optimal investment plan with respect to changes in several model parameters.     

双寡头竞争环境下消费者行为对创新产品 销售影响的仿真分析

基于创新产品扩散的Bass模型,构建了双寡头竞争市场环境下产品销售模型。模型考虑了竞争产品扩散过程中的相互影响,并刻画了部分消费者购买决策时“犹豫”行为。通过研究问题的系统动力学建模,仿真模拟了不同市场竞争环境下的产品需求。仿真结果表明,在双寡头竞争的市场环境中,消费者“犹豫”行为对产品的市场需求有负面影响。而且,企业竞争地位的不同会降低处于竞争劣势的企业的产品需求量。因此,竞争中处于劣势的企业应当加强其产品的口碑影响力,如提升用户体验,鼓励用户积极反馈来弥补自己竞争中的不足。     

Knowledge Composition and Its Influence on New Product Development Performance in the Big Data Environment

Product innovation is regarded as a primary means for enterprises to maintain their competitive advantage. Knowledge transfer is a major way that enterprises access knowledge from the external environment for new product innovation. Knowledge transfer may face the risk of infringement of the intellectual property rights of other enterprises and the termination of licensing agreements by the knowledge source. Enterprises must develop independent innovation knowledge at the same time they profit from knowledge transfers. Therefore, new product development by an enterprise usually consists of three types of new knowledge: big data knowledge transferred from big data knowledge providers, private knowledge transferred from other enterprises, and new knowledge developed independently by an enterprise in the big data environment. To find what the influences of different types of knowledge are on new product development (NPD) performance, a model is presented that maximizes the expected NPD performance. The results show that the greater the weight of independent innovation knowledge, the greater the performance of NPD. Enterprises tend to transfer knowledge from the external environment when the research and development (R&D) investment is much higher, and enterprises will speed up independent innovation when independent innovation knowledge is expected to bring a larger market share. The model can help enterprises to determine knowledge composition, the scale of R&D investment and predict the performance of NPD.     

Profit margin,process improvement and capacity decisions in global manufacturing

We study two strategies that a company may employ for competing in global markets: high profit margin; and investment in process improvements. The strategy of high profit margin is associated with aggressive investment in new plants worldwide; and the strategy of process improvements is associated with increasing the effective capacity of existing plants, reducing manufacturing cost and increasing the plant's life cycle. Such plant decisions are complicated by country-specific parameters, e.g. tariff rate, tax rate, transportation cost and economic growth rate, which may vary widely from one country to another. We construct a simulation model that uses non-linear relationships among decision variables to explore insights, e.g.: (i) global conditions that would be synergistic with each of the two strategies; (ii) level of investment that would be justified in newly industrialized countries, in relation to the industrially mature countries; and (iii) shifts in investment in time and their relationship to the competitive strategies.     

The role of the competitive environment in studies of strategic alignment: a meta-analysis

Numerous studies in operations management have investigated alignment between business and functional level strategies. In spite of the considerable amount of research that has been conducted, little consensus exists regarding how the competitive environment affects the relationship between strategic alignment and organisational performance. In this study, we build on prior operations management research that has examined alignment by examining whether the competitive environment mediates or moderates the relationship between alignment and performance. Using meta-analytic procedures, we find evidence that the competitive environment functions as a mediator in the relationship between strategic alignment and organisational performance. The implications of our study are that researchers should carefully consider how the competitive environment may affect their results in strategic alignment studies. Results from our study also suggest that the environment is a salient contributing factor to a firm's performance and that managers need to pay close attention to its competitive environment when make strategic and operational decisions.     

The impact of coronavirus pandemic (COVID-19) on education: The role of virtual and remote laboratories in education

To avoid the spread of the COVID-19 crisis, many countries worldwide have temporarily shut down their academic organizations. National and international closures affect over 91% of the education community of the world. E-learning is the only effective manner for educational institutions to coordinate the learning process during the global lockdown and quarantine period. Many educational institutions have instructed their students through remote learning technologies to face the effect of local closures and promote the continuity of the education process. This study examines the expected benefits of e-learning during the COVID-19 pandemic by providing a new model to investigate this issue using a survey collected from the students at Imam Abdulrahman Bin Faisal University. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed on 179 useable responses. This study applied Push-Pull-Mooring theory and examined how push, pull, and mooring variables impact learners to switch to virtual and remote educational laboratories. The Protection Motivation theory was employed to explain how the potential health risk and environmental threat can influence the expected benefits from e-learning services. The findings revealed that the push factor (environmental threat) is significantly related to perceived benefits. The pull factors (e-learning motivation, perceived information sharing, and social distancing) significantly impact learners' benefits. The mooring factor, namely perceived security, significantly impacts learners’ benefits.     

Who Follows Whom? A Location Study of Chinese Private and State-Owned Companies in the European Union

This paper analyses the location decision of Chinese manufacturing firms for their greenfield investments in Europe. The analysis draws on neo-institutional theory to formulate hypotheses on the importance of mimicry in Chinese firms’ location decisions and how this differs between private and state-owned enterprises. The analysis is conducted at the subnational (regional) level while taking into account economic integration across regions. The results confirm the importance of mimicry and show that Chinese firms not only follow previous Chinese investors in the same sector but also in unrelated sectors. Furthermore, Chinese investors only follow previous investments by Chinese private-owned companies while the results also show that Chinese private-owned companies generally have a higher tendency than state-owned companies do to follow prior investment decisions by compatriot firms. As the empirical evidence demonstrates the importance of bandwagon effects, the implication is that investment decisions can have a lasting influence on the geographical pattern of Chinese investments across regions.     

Polarizing Effects of Early Exporting on Exit

Extant research offers limited and inconclusive findings on the effects of early exporting by new ventures. This longitudinal study examines such effects, taking into consideration the roles of competition and adaptation in international venturing and exiting. The findings alert us to the potentially negative impact of early exporting on exit. Despite the deterrent effect of exporter competition, those new ventures that engage in early international venturing are impelled to keep strategically alert and expedite their learning process, therefore prospering in the highly competitive environment. By attracting foreign investors, new ventures will be able to start exporting early, and endorsed by the knowledge advantages associated with foreign partners the rapid entrants have better continuation chances. At the same time, early exporting in a relatively less competitive environment or without foreign ownership will lead to higher exit likelihood. By highlighting the polarizing effects of early exporting in the life cycle of new ventures, this study reconciles the difference between the process model and theories on international entrepreneurship to some extent.     

基于增强现实的交互式儿童国学机设计

目的探索中国儿童国学教育的创新性学习模式,将实体与数字设计在儿童学习体验中融合,采用增强现实技术实践开发智能儿童国学教育产品。方法围绕用户、产品、技术3个要素,引入访谈、问卷调研、行为观察、竞品分析等方法全面分析儿童学习国学的需求及问题,并从中提取设计的机会点。结论基于增强现实及图像识别技术,创建了多模交互体验国学产品——嘟嘟熊,全面改变了传统国学教育的学习模式,支持提升儿童在学习过程中的认知能力、动手能力及情感交流。同时,帮助儿童体验趣味性、互动性、益智性的学习享乐过程,提高了儿童国学学习的效率与兴趣。设计方案对于儿童国学教育的创新模式研究具有里程碑的意义,为中国儿童国学教育的未来奠定了发展方向。     

How to design a dynamic feed-in tariffs mechanism for renewables – a real options approach

Feed-in tariffs (FITs) are among the most favoured policies with which to drive the deployment of renewable energy. This paper offers insights into quantifying dynamic FITs to realise the expected installed capacity target with minimum policy cost under uncertainties of renewable intermittence and technology learning. We incorporate real options and use stochastic dynamic programming to model the strategic behaviour between policy-maker and investor and extend the one-time investment decision described by Farrell et al. [2017. ‘Specifying an Efficient Renewable Energy Feed-in Tariff.’ The Energy Journal 38: 53–75] to multiple-period decisions. An approach that combines binary tree scenario generation and a least squares Monte Carlo method is used to numerically identify the optimal FITs plan in practice. China’s offshore wind power investment is used as a case study to investigate the relationships among the optimal dynamic FITs level, the total policy cost, the expected capacity target, and the learning effect. The simulation results demonstrate that our proposed dynamic FITs can track the changes in technology learning well and that they can avoid the inefficiency of fixed FITs in stimulating technology adoption in the initial periods, along with overpayment by the policy-maker.