Technological Forecasting and Social Change最新文献

The rapid increase in population and unsustainable agricultural practices has significantly damaged the environment. Our study explores how to achieve food security through environmentally friendly methods such as controlled environment agriculture (CEA), genetic editing (GE), and farming automation (FA), with an emphasis on the importance of consumer acceptance of these technologies. Analyzing data from 2138 Australians and 1760 New Zealanders, we employ a two-step clustering method—(1) hierarchical clustering and (2) k-means clustering—to categorize consumers on the basis of their demographic and lifestyle preferences into three segments that capture differences in attitudes toward new agricultural technologies: “Green Urbanites,” “Environmentally Unconcerned,” and “Skeptical Foodie.” Our findings reveal that Green Urbanites are the most open to adopting CEA, GE, and FA, while Skeptical Foodies and Environmentally Unconcerned are more reluctant. We contribute to theory by studying a novel facet of consumer acceptance of sustainable technologies and revealing how consumption, living, and lifestyle patterns motivate new technology acceptance in the agricultural sector. To inform practice, we suggest tailored strategies to increase engagement of the identified segments and promote wider acceptance of sustainable agricultural practices for enhanced food security.

This study investigates the factors that influence users' repurchase intentions in online food ordering platforms. The study examines the influence of e-satisfaction on repurchase intention, as well as the mediating effect of attitude and the moderating effects of e-WOM and habit on the relationship between e-satisfaction and repurchase intention. The study collected data online from online food ordering platform users in Turkey and analyzed it by conducting PLS-SEM. The results show that e-satisfaction has a positive and significant influence on repurchase intention and this relationship is mediated by attitude and moderated by e-WOM and habit. The study concludes with theoretical and managerial implications and recommendations for future research.

This study aimed to deepen our understanding of the necessary conditions for the adoption of electric car sharing. Given the identified sufficient factors (should-have), our study focused on identifying those that are necessary (must-have). Specifically, we investigated the role of trust as a critical, necessary factor for the success of an electric car sharing scheme. We conceptualized the intention to use electric car sharing within an extended framework of planned behavior, incorporating trust beliefs. Using survey data from 317 Taiwanese respondents, we employed necessary condition analysis in combination with partial least squares structural equation modelling. Key findings include (1) Trust is not directly associated with adoption intention; however, it plays an indirect role in intention formation and is a must-have factor for the adoption of electric car sharing. (2) Social pressure was found to be neither sufficient nor necessary for usage intention. However, a distinct lack of social pressure appears to guarantee the failure of electric car sharing adoption. (3) Our results confirm both conceptual and empirical claims that positive attitudes toward, and control perceptions about, electric car sharing are both sufficient and necessary for adoption. The implications for both practitioners and researchers are discussed in detail.

Consumer attention is a critical factor in driving consumer-centric innovation characterized by selectivity and dynamism. However, existing literature has emphasized the selectivity but overlooked the dynamism vital for frequent iterative innovation. This study addresses the gap by exploring innovation strategies with dynamic consumer attention. We constructed a data-driven agent-based model (ABM) with two decision-making entities: manufacturers and consumers. Aspect-based sentiment analysis (ABSA) results obtained from consumer reviews are used to shape consumer agents with selective and dynamic attention. A comparative experiment, utilizing manufacturers insights based on various consumer-generated knowledge, was devised to evaluate iterative innovation efficacy. The findings highlight benefits of innovation strategies considering consumer attention drivers, especially consumer self-learning and social influence behaviors. Additionally, we identified consumers' observation bias in product attributes exhibits a notable inverted U-shaped effect on new product adoption, and negatively impacting repeat purchases. Moreover, our results show that as technology for new products matures, incremental iteration based on consumer attention becomes the optimal strategy. However, rapid technological advancements or disruptive changes could make attention-focused strategies less reliable. These insights offer valuable implications for manufacturers to optimize product innovation strategies by understanding consumer characteristics, limited innovation resources, and current technological conditions.

An effective innovation strategy is crucial for firm performance and national economic growth, yet many firms still face the challenge of misalignment between their innovation activities and strategies. While previous studies focused on the mediation role of innovation activities on strategy and performance, research is needed to examine the complex interdependencies between strategies and innovation activities to better understand their impact on firm performance.

From a configurational perspective, this study investigates how different combinations of innovation activities and strategies influence firm performance. Twenty combinative effects of four categories of innovation activities and five strategic types on two firm performance measures (growth and profit) were explored, resulting in the creation of an innovation-strategy alignment map. This study contributes to innovation strategy literature by providing the alignment map, where four configurational clusters with equifinality were identified. It also contributes to competitive strategy literature by offering empirical verification for the “pure” versus “hybrid” debate and identifying two new types of hybrids with more and different strategic dimensions by using an empirical method, thus advancing the call for more detailed hybrid strategy research in this area. In addition, the innovation-strategy alignment map provides options to managers with different resources and capacities for effective planning.

The technological capabilities of Human Resource Analytics (HRA), enhanced by recent innovations in Machine Learning (ML), offer exciting opportunities. However, organisations often fail to realise these potentials because of a limited understanding of why individuals choose to adopt or disregard respective tools. Prior research on innovation adoption offers preliminary insights but fails to aggregate the determinants of individual adoption into actionable suggestions for decisions in the ML adoption process. Our study applies focused interviews to examine non-ML experts' reasoning for using a specific tool tailored to a public sector organisation, which corresponds to the usual end-user perspective of ML-based HRA adoption. By drawing from the HRA adoption framework, provided by Vargas et al. (2018), we contribute to the literature by identifying relevant beliefs and experiences influencing one's intention to adopt ML-based HRA and by qualitatively linking these beliefs to ML characteristics such as transparency, automation and fairness. For practitioners, we provide actionable guidance emphasising the need to ensure fairness proactively, as interviewees do not consider this aspect when deciding to adopt ML-based HRA.

Governing firms' R&D manipulation is crucial amid the prevalent opportunism during innovation in emerging economies. This study employs data on listed companies in China from 2010 to 2021 and uses the Golden Tax Project III as a quasi-natural experiment to estimate the effect of digital tax administration (DTA) on R&D manipulation via a dual machine learning model. Based on the capability, opportunity, and motivation-behavior (COM-B) theoretical framework, we explore the key pathways and identify the heterogeneous effects of DTA on various R&D manipulation motivations and directions. The results indicate the following: (1) DTA can regulate firms' R&D manipulation behavior and incentivize innovation quality and diversity. (2) DTA reduces R&D manipulation by enhancing accounting information quality and transmission efficiency in capital markets (internal and external information channels), strengthening tax audits (external monitoring channels), and curbing managerial self-interest and tax avoidance motives (internal motivation channels). (3) The DTA primarily functions as a governance instrument to address manipulation in various directions driven by tax avoidance and executive self-interest motives. These findings offer valuable insights for exploring governance models to address R&D manipulation in emerging economies.

Additive Manufacturing (AM) was expected to be the next great technological revolution, yet the predicted market growth has fallen short by 40%. Using a proprietary, cross-sectional data set of 350 ventures in the AM field founded between 2000 and 2017, this study analyses AM companies from a business model perspective. Specifically, we explore which value delivery and value capture mechanisms are decisive for AM venture success, measured via their operational and ownership state. We reveal that targeting the B2B market and selling via partner networks significantly increases firm survival likelihood. Further, applying a recurring revenue model increases the likelihood of an IPO. Our main contributions are two-fold: first, our research complements the business model literature by evaluating value delivery and capture in the AM entrepreneurial space, revealing that customer segments, sales channels, and revenue models are key to venture success. Second, we provide business model design guidelines to entrepreneurs as well venture capitalists in the AM field.

The emergence of the metaverse, driven by advancements in transformative technologies, is expected to profoundly influence how individuals interact in the future, potentially disrupting entire business sectors and society at large. Professional football clubs (PFCs) have already initiated explorative forays into the metaverse. Therefore, we examine the impact of the emerging metaverse on the future business of PFCs, both in the medium (until 2026) and long term (until 2030). Building on growing metaverse business literature, this paper presents a Delphi study with quantitative and qualitative assessments from 103 experts. We develop twelve projections clustered around business model innovation (BMI) items and test them with our group of experts. Moreover, we collect information about participants' deep-level characteristics to enhance our analysis and gain additional insights. We identify and discuss two possible future scenarios projected until 2030, both of them emphasizing PFCs' opportunities in the metaverse. Experts concur that underlying metaverse technologies, such as AR/VR equipment and AI, will innovate value creation and value capture in the metaversal future. PFCs will likely expand their value proposition and generate new revenues through innovative models, including perpetual royalties from metaverse activities or platform fees from transactions among fans, commercial partners, and media collaborators.

This paper analyses the impact of the use of industrial robots on labor income shares at both the theoretical and empirical levels. On the theoretical side, the role of induced technological progress, the creation of new tasks, and the penetration of industrial robots on labor income shares are systematically explored by incorporating industrial robots into task models and endogenizing the task induced technological progress. Empirically, industrial robot penetration at the regional level in China is constructed from the industrial robots data released by the International Federation of Robotics (IFR), which is matched with Chinese A-share listed companies in 2011–2019, and the causal strategy of Bartik-style instrumental variables is used to analyze the impact of the penetration of industrial robots and task induced technological progress on the share of labor income. The study shows that the penetration of industrial robots significantly increases the share of labor income, but the task induced technological progress reduces the share of labor income, and that the impact of the penetration of industrial robots varies significantly external financing dependence, ownership, and regions. From the perspective of the impact mechanism, robot penetration increase the levels of inputs complementarity and hence reduce the elasticity of substitution between robots and labor. Meanwhile, robots can effectively increase the wage level of the labor force, thus increasing the labor income share. The findings of this paper provide a decision-making reference for how industrial robots can better serve human beings in the new era so that workers can share the fruits of economic development.