International Journal of Innovation and Technology Management

The dimensions of technology-led, business model-led, and services or product-led disruptions are well established. However, scant literature considers regulatory actions and assesses their disruptiveness. Using the definition of disruption from [], this paper proposes a “Disruption Assessment Framework” for assessing the disruptiveness of regulation. The “Disruption Assessment Framework” considers measures related to redesigning of survival strategies of existing agents and the percentage of the agents in an ecosystem that are disrupted. Using the Disruption Assessment Framework, the authors analyze the disruptiveness of four key regulatory actions from the telecommunications industry in India. The creation of TRAI, the introduction of Calling Party Pay, the permission to use Wireless in a local loop, and the issuance of a 2G license in 2008 are found disruptive. The study reveals that regulatory actions can be disruptive and may negatively impact the market, unlike technology-based innovations. Contrary to regulatory objectives, disruptions due to regulations may impact market and consumer welfare negatively. This paper is a novel attempt to analyze the disruptiveness of regulatory initiatives taken in the telecommunications industry in India. Findings from this study can be incorporated into the regulatory impact analysis and regulatory consultation process to identify risk from regulatory disruption ex-ante.

In contrast to conventional Social Network Sites, BeReal counteracts staged self-portrayal and aims for authenticity by posting a photo at an unannounced time within a short period. This study is the first to investigate drivers and barriers for using BeReal. Furthermore, this study successfully links BeReal usage intention to subjective well-being. Building on the Self-Determination Theory, the Unified Theory of Acceptance and Use of Technology 2, and specific particularities, a research model is validated by n = 657 consumers using partial least square structural equation modeling. The study contributes to the research intersection of SNS, technology acceptance, and effects on well-being.

Venture capital (VC) is essential for the growth of small and medium enterprises (SMEs) in developed e-commerce countries. SMEs play a crucial role in the economic development of both developing and developed countries. This paper proposes the influence of VC on the growth of e-commerce in SMEs using the propensity score matching (PSM) method. PSM is defined as the statistical analysis of observational data, where a statistical matching method attempts to evaluate the impacts of a treatment or policy. The experimental results show that the effect of e-commerce on SMEs who received VC financing experience improves the performance of employment growth and sales. Our findings reveal that VC significantly enhances employment rates and sales performance in e-commerce sectors. The study underscores the critical role of VC in driving innovation and growth within SMEs, providing valuable insights for investors and policymakers aiming to foster economic development.

During Design Thinking (DT) processes, team flow is a vital indicator and driving force behind feeling and performing at peak. In a quasi-experimental, segmented time series study, we examined team flow levels over time using high-resolution, real-time measurements during two multi-day DT workshops with flow-inducing exercises (in short: flow inducers). Two key insights emerged: (1) a second wind effect, where teams naturally increased flow after the initial engagement phase; and (2) a preservation capacity, where teams with greater creative outputs maintained higher flow levels despite interruptions (in short: flow reducers). The careful management of flow-inducing exercises and workshop duration should give teams the flexibility needed to dynamically adjust and stay in the zone. These findings inform the development of a framework for sustaining team flow in multi-day workshops, the core contribution of this study. The framework offers practical recommendations to manage team flow in stages, emphasizing key points of intervention and optimal timing of flow inducers. It is adaptable for various co-creative settings, guiding facilitators in maximizing creative persistence and output.

Smart home devices are a crucial part of the Internet of Things (IoT) industry and offer advantages such as comfort and energy efficiency which can be utilized to improve well-being in home environments. These devices include smart meters, smart watches, remote-controlled lighting, heating and water consumption systems, security systems, home ventilation, door locks, smart TVs, smart speakers, refrigerators, and smart washing machines. This study conducted a bibliometric analysis of published scholarly research papers in the Scopus database from 2000 to 2023. VOSviewer and Biblioshiny R software were employed to map and visualize the documents. The findings reveal that, on a global scale, research on smart home devices has proliferated since 2013, with the USA emerging as a prominent world leader in both publications and citations. Developing domains of expertise in smart home devices include machine learning, deep learning, ambient intelligence, and intelligent sensors.

The growing turbulence of business environments has amplified the need to anticipate technological breakthroughs through systematic processes referred to as Future-oriented Technology Analysis (FTA). FTA practices that the literature reports are diverse and, in various aspects, not univocally defined. Additionally, the empirical evidence concerning the implementation of different FTA practices, concerning various methodological approaches covered by the FTA as a whole, is still scant. This study aims to fill this gap by investigating whether the heterogeneity of processes that have emerged from the literature mirrors real-world business practices and by exploring the relationship between the different characterizing features of FTA. The study employs a multiple case study design, analyzing the FTA processes implemented in 10 Italian companies and examining six main dimensions of analysis: systematicity, strategic nature, object, methodological approach, time horizon and stakeholders involved. The case analysis revealed that the practices adopted by these companies are typically non-systematic, often relying on informal and de-structured processes. The strategic relevance of FTA varies based on industry characteristics. However, in all these cases, the primary focus of FTA was on identifying emerging technologies, rather than predicting and creating future technologies. Finally, a predominant configuration of FTA emerged, involving the usage of FTA to detect existing or emerging technologies using qualitative techniques and the involvement of external stakeholders to enhance the anticipatory performance of the process.

This study examines the impact of environmental, social, and governance (ESG) performance and technological innovation (TI) on corporate financing efficiency (CFE) in Chinese firms. Data from 400 companies listed in the Shanghai and Shenzhen A-share markets, alongside ESG scores, were derived from the Thomson Reuters ESG scoring system spanning 2013–2022. Fixed effects (FE) and quantile regressions were employed to explore this relationship. The findings demonstrate that ESG and TI have significant and positive effects on CFE. Furthermore, TI is a moderator that strengthens the linkage between ESG and CFE. Findings emphasize the critical importance of ESG performance in obtaining cost-effective financing for Chinese firms. This underscores the critical need for businesses and regulators to prioritize and integrate ESG practices into corporate strategies. Such integration facilitates access to lower-cost financing, enhances cost efficiency, and optimizes investments in green technology. The findings emphasize the importance of fostering a sustainable corporate culture that supports the alignment of TI with ESG principles in business operations. These outcomes carry substantial implications for the corporate sector and regulatory frameworks, highlighting the pivotal role of sustainable practices in driving financial performance and long-term prosperity.

As the text is written on a special digitizer or Personal Digital Assistance (PDA) in which a sensor picks up the pen-tip movements along with the pen-up/pen-down switching, its automatic conversion is performed in the online Handwriting Recognition (HR). There are several works related to the online recognition of Devanagari as well as Tamil scripts. Meanwhile, the online recognition works associated with other Indian languages, specifically Telugu, which is complex in its structure together with style, are very few. Our work emphasizes the development of an online handwritten Telugu character recognition system using dominant points with the combination of SVM and performance analysis of various other features. Three classifiers namely, SVM, K-NN and MLP are used to examine the performance of the feature vectors. The proposed research is verified with HP-Lab data available in the UNIPEN format.

In addition to dealing with the dispute between the e-commerce activities of companies and the lack of supplies, the companies had settled, by applying a highly developed cloud technology framework, the difficulties of lack of resources, workforce and necessary technology in e-commerce activities. E-commerce utilizing cloud-based financial instruments is becoming a common strategy for the rise of international growth over the years. Nevertheless, the presence of fake goods on the site endangered the advantages of all investors. Therefore, this paper suggests a Hybridized Multi-special Decision finding with the Anti-Theft Probabilistic (HMDAP) method for making the improvement of the cloud-based model, and it is trained to find fake goods. A multi-special decision finding is used to address the issues and the lack of e-commerce facilities by creating a programming environment for e-commerce provided by the cloud computing system. The Anti-Theft Probabilistic method is used to track fake goods and use the Carlo method to predict possible stolen data in e-commerce. HMDAP enables businesses to reduce expenses through the successful delivery of e-commerce activities and provides assumptions of unsafe data in e-commerce.

The relevance of sustainable practices in businesses is indispensable due to the rising global concerns over climate threats and carbon emissions. Integrating Environmental, Social, and Governance (ESG) factors into business models and operations has become the need of the hour. This radical shift in business operations is propelled by the Sustainable Development Goals (SDGs) set up by the United Nations (UN). The advent of newer technologies, especially Machine Learning (ML) and Deep Learning (DL), has aided firms in data assimilation and evaluating their sustainable practices. However, a notable research gap remains in examining ESG integration in the processes and procedures of corporations through AI algorithms. This is due to the challenges of gathering comprehensive ESG-related data and the need for detailed comparative sectoral analysis. The impact and correlation of ESG scores on a firm’s financial performance have been studied, but a sectoral analysis across the globe needs to be done. The use of ML models for the textual and numerical data accumulation analysis is pertinent. The present study aims to provide a data-driven comparative analysis of three chosen sectors with the help of Artificial Intelligence (AI). This research paper focuses on the environmental domain, as the climate threats and opportunities need an appropriate policy for implementation. This study utilizes the vast repository of ESG disclosure data from the Bloomberg database for the energy, consumer durables, and pharmaceutical sectors, covering global and Indian perspectives for comparative analysis. These three sectors provide pivotal data for addressing sustainability challenges regarding the Climate and Emission categories. The research and interpretation of numerical data are done with the help of the DL model and language model Bidirectional Encoder Representations from Transformers (BERT), which analyzes the textual data from social media. The study highlights the challenges associated with ESG integration among the chosen sectors on a global platform. The findings from this study can contribute significantly to the ongoing sustainability dialogue and policy formulation.

Plant disease identification plays a crucial role in agricultural management, but the traditional methods are time-consuming and imprecise. This work contributes to the ongoing efforts to develop reliable and efficient solutions for automated plant disease diagnosis, ultimately aiding in the timely management and mitigation of agricultural challenges. This study investigated the effectiveness of various deep convolutional neural networks (CNNs) for automated plant disease identification from leaf images. By exploring deep and transfer learning techniques such as CNNs, InceptionNet, DenseNet 121, and ResNet-50, a Deep Convolutional Neural Network (DCNN) is proposed to categorize the leaf disease. Different activation functions, specifically Rectified Linear Unit (ReLU) and Swish, are employed to investigate their impact on model performance. The experiments revealed that the DCNN architecture, when paired with the Swish activation function, demonstrated 96% accuracy in plant disease identification.