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This paper aims to understand how the perceived usefulness of voice assistants (VAs) is affected by the perceived quality of the process (interaction) and the outcome (information). The authors also aim to determine the extent to which the perceived usefulness of VAs improves the perceived privacy associated with their use and increases users’ intention to continue using them. Consumer technology innovativeness is included as a personal trait moderator, to compare the results between tech and nontech innovators. For this purpose, the authors use the framework of the uses and gratifications theory (U&GT).

A survey of 467 VA users was conducted and structural equation modeling was used to analyze the data.

The authors identify two main determinants of the perceived usefulness of VAs that influence users’ intention to continue using this technology, process quality and outcome quality. These two factors influence the continued use of VAs in different ways depending on the technology innovativeness of the consumers. The results show that tech innovators are oriented toward the interactive experience, and therefore, mainly value the process quality. In addition, nontech innovators are oriented toward a satisfactory response from VAs, and therefore, primarily value the outcome quality. In addition, the positive effect of perceived usefulness on perceived privacy is higher for tech innovators.

This study enhances the literature on the perceived usefulness of VAs within the framework of U&GT. It identifies two antecedents (process quality and outcome quality) of perceived usefulness and observes significant differences based on technological innovativeness.

This study enhances the literature on the perceived usefulness of VAs within the framework of U&GT. It identifies two antecedents (process quality and outcome quality) of perceived usefulness and observes significant differences based on technological innovativeness.

Este artículo tiene como objetivo entender cómo la utilidad percibida de los Asistentes de Voz (AV) se ve afectada por la calidad percibida del proceso (interacción) y el resultado (información). Asimismo, busca determinar hasta qué punto la utilidad percibida de los AVs mejora la privacidad percibida asociada con su uso y, consecuentemente, la intención de los usuarios de seguir utilizándolos. La innovación tecnológica se incluye como moderador personal para comparar los resultados entre innovadores tecnológicos y no tecnológicos. Para este propósito, utilizamos la Teoría de Usos y Gratificaciones (U&GT).

Se realizó una encuesta a 467 usuarios de AVs, y se utilizó la modelización de ecuaciones estructurales (SEM) para analizar los datos.

La calidad del proceso y la calidad del resultado son antecedentes claros de la utilidad percibida de los AVs, que afecta a la intención de los usuarios de seguir usándolos. La influencia de ambos factores difiere entre usuarios según su nivel de innovación tecnológica. Los resultados muestran que los innovadores tecnológicos valoran más la experiencia interactiva y la calidad del proceso, mientras que los no innovadores tecnológicos se enfocan en obtener respuestas satisfactorias de los AVs. Además, la influencia positiva de la utilidad percibida en la privacidad percibida es más pronunciada en los innovadores tecnológicos.

Este estudio enriquece la literatura sobre la utilidad percibida de los AVs dentro del marco de la U&GT. Identifica dos factores previos (calidad del proceso y calidad del resultado) de la utilidad percibida y observa diferencias significativas basadas en la innovación tecnológica.

本文旨在了解语音助手（VAs）的感知有用性如何受到过程（交互）和结果（信息）的感知质量的影响。我们还旨在确定语音助手的感知有用性在多大程度上改善了与使用语音助手相关的感知隐私, 并提高了用户继续使用语音助手的意愿。我们将消费者的技术创新性作为个人特质调节因素, 以比较技术创新者和非技术创新者的结果。为此, 我们使用了 “使用与满足理论"（U&GT）框架。

我们对 467 名增值服务用户进行了调查, 并使用结构方程模型（SEM）对数据进行了分析。

我们确定了影响用户继续使用该技术意向的虚拟机构感知有用性的两个主要决定因素：（1）过程质量和（2）结果质量。根据消费者的技术创新能力, 这两个因素以不同的方式影响着虚拟现实技术的持续使用。结果显示, 技术创新者以互动体验为导向, 因此主要看重过程质量。此外, 非技术创新者倾向于从虚拟机构获得令人满意的回应, 因此主要看重结果质量。此外, 对于科技创新者来说, 感知有用性对感知隐私的积极影响更大。

本研究在 U&GT 框架内加强了有关虚拟机构感知有用性的文献。它确定了感知有用性的两个前因（过程质量和结果质量）, 并观察到了基于技术创新性的显著差异。

Porous implant surface is shown to facilitate bone in-growth and cell attachment, improving overall osteointegration, while providing adequate mechanical integrity. Recently, biodegradable material possessing such superior properties has been the focus with an aim of revolutionizing implant’s design, material and performance. This paper aims to present a comprehensive investigation into the design and development of low elastic modulus porous biodegradable Mg-3Si-5HA composite by mechanical alloying and spark plasma sintering (MA-SPS) technique.

This paper presents a comprehensive investigation into the design and development of low elastic modulus porous biodegradable Mg-3Si-5HA composite by MA-SPS technique. As the key alloying elements, HA powders with an appropriate proportion weight 5 and 10 are mixed with the base elemental magnesium (Mg) particles to form the composites of potentially variable porosity and mechanical property. The aim is to investigate the performance of the synthesized composites of Mg-3Si together with HA in terms of mechanical integrity hardness and Young’s moduli corrosion resistance and in-vitro bioactivity.

Mechanical and surface characterization results indicate that alloying of Si leads to the formation of fine Mg2 Si eutectic dense structure, hence increasing hardness while reducing the ductility of the composite. On the other hand, the allying of HA in Mg-3Si matrix leads to the formation of structural porosity (5-13 per cent), thus resulting in low Young’s moduli. It is hypothesized that biocompatible phases formed within the composite enhanced the corrosion performance and bio-mechanical integrity of the composite. The degradation rate of Mg-3Si composite was reduced from 2.05 mm/year to 1.19 mm/year by the alloying of HA elements. Moreover, the fabricated composites showed an excellent bioactivity and offered a channel/interface to MG-63 cells for attachment, proliferation and differentiation.

Overall, the findings suggest that the Mg-3Si-HA composite fabricated by MA and plasma sintering may be considered as a potential biodegradable material for orthopedic application.

Even though 95% of children with neurodevelopmental disorders, including autism spectrum disorder (ASD), live in low- and middle-income countries, there is a dearth of studies from these countries, including the Republic of Georgia. Several ASD screening tools are available, but few are validated for use in Georgian or other smaller countries. This study aims to adapt and validate the autism spectrum screening questionnaire (ASSQ) for use in Georgia.

The ASSQ was administered for all third-grade students in 402 schools in the five main Georgian cities, n = 27,336. Prior to use, the 27-item ASSQ was translated, back-translated and adapted for use in Georgia. A total of 16,556 students (approximately 61%) were assessed by a parent and/or teacher. Optimal cutoff scores were estimated. Randomly chosen children who screened positive (n = 173) and negative (n = 127) were offered comprehensive assessment using standardized diagnostic procedures.

Data from 15,510 parents- and 13,517 teachers-administered ASSQ revealed statistically significant differences in median and cutoff scores between parents and teachers: 7 versus 4 and 9 versus 6, respectively. Cutoff score = 14, on either parent or teacher ASSQ, had sensitivity of 0.94, indicating that it can be used in school settings.

The Georgian adaptation of the ASSQ creates opportunity for further ASD research, while also providing a valid screening tool for clinicians. Data from Georgia will add to the growing understanding of the broader ASD phenotype.

Digital transformation (DT) is a major challenge for incumbent organisations, as research on this phenomenon has revealed a high failure rate. Given this consideration, this paper reviews the literature on DT in incumbent organisations to identify the main themes and research directions to be undertaken.

The authors adopt a systematic literature review (SLR) and computational literature review (CLR) employing a machine learning algorithm for topic modelling (LDA) to surface the themes discussed in 103 peer-reviewed studies published between 2010 and 2022 in a multidisciplinary article sample.

The authors identify and discuss the five main themes emerging from the studies, offering the state-of-the-art of DT in established firms' literature. The authors find that the most discussed topics revolve around the DT of healthcare, the process of renewal and change, the project management, the changes in value performances and capabilities and the consequences on the products of DT. Accordingly, the authors identify the topics overlooked by literature that future studies could tackle, which concern sustainability and contextualisation of the DT phenomenon.

The authors further propose managerial insights which equip managers with a revolutionary mindset that is not constraining but, rather, integration-seeking. DT is not only about technology (Tabrizi B et al., 2019). Successful DT initiatives require managerial capabilities that foster a sustainable departure from the current organising logic (Markus, 2004). This study pinpoints and prioritises the role that paradox-informed thinking can have to sustain an effective digital mindset (Eden et al., 2018) that allows for the building of momentum in DT initiatives and facilitates the renewal process. Indeed, managers lagging behind DT could shift from an “either-or” solutions mindset where one pole is preferred over the other (e.g. digital or physical) to embracing a “both-and-with” thinking balancing between poles (e.g. digital and physical) to successfully fuse the digital and the legacy (Lewis and Smith, 2022b; Smith, Lewis and Edmondson, 2022), enact the renewal, and build and maintain momentum for DTs. The outcomes of adopting a paradox mindset in managerial practice are enabling learning and creativity, fostering flexibility and resilience and, finally, unleashing human potential (Lewis and Smith, 2014).

The authors propose insight that will equip managers with a mindset that will allow DT to fail less often than current reported rates, which failure may imply potential organisational collapse, financial bankrupt and social crisis.

The authors offer a multidisciplinary review of the DT complementing existing reviews due to the focus on the organisational context of established organisations. Moreover, the authors advance paradoxical thinking as a novel lens through which to study DT in incumbent organisations by proposing an array of potential research questions and new avenues for research. Finally, the authors offer insights for managers to help them thrive in DT by adopting a paradoxical mindset.

Social relationships play an important role in organizational entrepreneurship. They are crucial to entrepreneurs’ decisions because, despite the bleeding-edge technological advancements observed nowadays, entrepreneurs as human beings will always strive to be social. During the COVID-19 pandemic many companies moved activities into the virtual world and as a result offline Social relationships became rarer, but as it turns out, even more valuable, likewise, the inter-organizational cooperation enabling many companies to survive.

This chapter aims to develop knowledge about entrepreneurs’ SR and their links with inter-organizational cooperation. The results of an integrative systematic literature review show that the concept of Social relationships, although often investigated, lacks a clear definition, conceptualization, and operationalization. This chapter revealed a great diversity of definitions for Social relationships, including different scopes of meaning and levels of analysis. The authors identify 10 building blocks and nine sources of entrepreneurs’ Social relationships. The authors offer an original typology of Social relationships using 12 criteria. Interestingly, with regard to building blocks, besides those frequently considered such as trust, reciprocity and commitment, the authors also point to others more rarely and narrowly discussed, such as gratitude, satisfaction and affection. Similarly, the authors discuss the varied scope of sources, including workplace, family/friendship, past relationships, and ethnic or religious bonds. The findings of this study point to a variety of links between Social relationships and inter-organizational cooperation, including their positive and negative influences on one another. These links appear to be extremely dynamic, bi-directional and highly complex.

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

The railway signal equipment failure diagnosis is a vital element to keep the railway system operating safely. One of the most difficulties in signal equipment failure diagnosis is the uncertainty of causality between the consequence and cause for the accident. The traditional method to solve this problem is based on Bayesian Network, which needs a rigid and independent assumption basis and prior probability knowledge but ignoring the semantic relationship in causality analysis. This paper aims to perform the uncertainty of causality in signal equipment failure diagnosis through a new way that emphasis on mining semantic relationships.

This study proposes a deterministic failure diagnosis (DFD) model based on the question answering system to implement railway signal equipment failure diagnosis. It includes the failure diagnosis module and deterministic diagnosis module. In the failure diagnosis module, this paper exploits the question answering system to recognise the cause of failure consequences. The question answering is composed of multi-layer neural networks, which extracts the position and part of speech features of text data from lower layers and acquires contextual features and interactive features of text data by Bi-LSTM and Match-LSTM, respectively, from high layers, subsequently generates the candidate failure cause set by proposed the enhanced boundary unit. In the second module, this study ranks the candidate failure cause set in the semantic matching mechanism (SMM), choosing the top 1st semantic matching degree as the deterministic failure causative factor.

Experiments on real data set railway maintenance signal equipment show that the proposed DFD model can implement the deterministic diagnosis of railway signal equipment failure. Comparing massive existing methods, the model achieves the state of art in the natural understanding semantic of railway signal equipment diagnosis domain.

It is the first time to use a question answering system executing signal equipment failure diagnoses, which makes failure diagnosis more intelligent than before. The EMU enables the DFD model to understand the natural semantic in long sequence contexture. Then, the SMM makes the DFD model acquire the certainty failure cause in the failure diagnosis of railway signal equipment.

The objective of this work is to demonstrate the relationships between the two main processes of research and development (R&D) activities: the knowledge generation phase (KPP) and the knowledge commercialization, or transfer, phase (KCP), in a sector that is intensive in this type of activity, such as the pharmaceutical sector. In addition, within the framework of the general objective of this work, the authors propose two other objectives: (1) make advances in network efficiency measurement models, and (2) determine the factors associated with efficiency in the KPP and in the KCP in companies of the pharmaceutical sector in Spain.

A Network Data Envelopment Analysis (NDEA) model (Färe and Grosskopf, 2000) with categorical variables (Lee et al., 2020; Yeh and Chang, 2020) has been applied, and a sensitivity analysis of the obtained results has been performed through a DEA model of categorical variables, in accordance with the work of Banker and Morey (1986), to corroborate the results of the proposed model. The sample is made up of 77 companies in the pharmaceutical sector in Spain.

The results obtained point to a greater efficiency of pharmaceutical companies in the KPP, rather than in the KCP. Furthermore, the study finds that 1) alliances between companies have been the accelerating factors of efficiency in the KCP (but patents have slowed this down the most); 2) the quality of R&D and the number of R&D personnel are the factors that most affect efficiency in the KPP; and 3) the quality of R&D again, the benefits obtained and the position in the market are the factors that most affect efficiency in the KCP.

The authors have not found studies that show whether the efficiency obtained by R&D-intensive companies in the KPP phase is related to better results in terms of efficiency in the KCP phase. No papers have been found that analyse the role of alliances between R&D-intensive companies and patents, as agents that facilitate efficiency in the KCP phase, covering the gap in the research on both problems. Notwithstanding, this work opens up a research path which is related to the improvement of network efficiency models (since it includes categorical variables) and the assessment of the opinions of those who are responsible for R&D departments; it can be applied to decision-making on the aspects to improve efficiency in R&D-intensive companies.

In this paper the interconnectedness among financial institutions and the level of systemic risks of four types of Chinese financial institutions are investigated.

By the means of RAS algorithm, the interconnection among financial institutions are illustrated. Different methods, including Linear Granger, Systemic impact index (SII), vulnerability index (VI), CoVaR, and MES are used to measure the systemic risk exposures across different institutions.

The results illustrate that big banks are more interconnected and hold the biggest scales of inter-bank transactions in the financial network. The institutions which have larger size tend to have more connection with others. Insurance and security companies contribute more to the systemic risk where as other institutions, such as trusts, financial companies, etc. may bring about severe loss and endanger the financial system as a whole.

Since other institutions with low levels of regulation may bring about higher extreme loss and suffer the whole system, it deserves more attention by regulators considering the contagion of potential risks in the financial system.

This study builds a valuable contribution by examine the systemic risks from the perspectives of both interconnection and tail risk measures. Furthermore; Four types financial institutions are investigated in this paper.