Technology Decisions Research Articles

Business Sustainability Through Technology Adoption: Readiness and Acceptance of E-commerce Technology in MSMEs

The digital economy requires all countries to participate in preparing for digital transformation. As a country in Southeast Asia, Indonesia must prepare for the era of global competition. In 2020, the Indonesian government launched a digital transformation program with the vision of the ”Largest Digital Economy.” The digitalization of micro and small businesses in Indonesia is experiencing a slowdown due to the difficulty in adopting technology by MSME actors, and partly because of the COVID-19 pandemic. The focus of this research emphasizes the impact of the readiness of micro, small, and medium enterprises as individual decision-makers on the adoption of marketing technology. The Readiness Acceptance Model (TRAM) technology developed by Lin et al. (2007) utilized in this study explains that the impact of individual characteristics and experiences on their use and usefulness dominates the marketing technology decision-making process. The population of this study includes the micro, small, and medium enterprises in the Sukabumi area with 220 respondents as business actors. The method used is path analysis. The results show that the effect of technological readiness of MSME actors in Sukabumi on the acceptance of e-commerce carriers and technological know-how are influenced by the mindset of optimism, innovation, and discomfort, which have a big impact on the perception of ease of use (PEU) and the perception of usefulness (PU). The PEU has a massive impact on perceived advantages (PU) and the PU and PEU both have a significant positive impact on the intention to use.
 Keywords: technology readiness, technology acceptance model, e-commerce, UMKM

Information support of CAD of technological processes of structurally complex product prototypes

Objectives. The purpose of the study of information support for computer-aided process planning system of prototypes of structurally complex products is to formalize the adoption of technological decisions using interconnected databases.Methods. The method of decomposition of technology design processes was used in the work.Results. In the study of information support for computer-aided process planning system of prototypes of structurally complex products, the problem of determining the structure of information support was solved, the sequence of using information support at various stages of system implementation was determined and universal scheme for the formation of operational description of the technological process was developed.Conclusion. The use of the results of the study in the creation of computer-aided process planning system for prototypes of structurally complex products could increase the productivity of technological design in 7.8 times.

Impact of the government’s financial support policy on technology innovation decisions of small and medium enterprise in Ho Chi Minh city

The paper examines the impact of the government's financial support policy on technological innovation decisions in 150 small and medium enterprises (SMEs) in Ho Chi Minh City. The study uses the Binary logistic model to quantify the influence of the government’s financial support policies on the decision to innovate technology of enterprises. The survey was collected directly from 150 small and medium sized enterprises in 6 districts of Ho Chi Minh City. Research results show that the government's financial support policy has a positive impact on the enterprises’ technological innovation decisions. However, this policy is not effective at high-grossing enterprises. Additionally, the technological innovation decisions are affected by other factors, including the operating time and the volumn of the enterprise, and the market competitiveness. The research findings propose suggestions to improve the effectiveness of this policy on technological innovation decisions in SMEs.

Data-owning democracy or digital socialism?

ABSTRACT This article contrasts two reform proposals articulated in recent debates about how to democratize the digital economy: data-owning democracy and digital socialism. A data-owning democracy is a political-economic regime characterized by the widespread distribution of data as capital among citizens, whereas digital socialism entails the social ownership of productive assets in the digital economy and popular control over digital services. The article argues that while a degree of complementarity exists between the two, there are important limitations to theories of data-owning democracy that have not yet received significant attention within the literature. The bulk of the article highlights three ways in which digital socialists would consider a data-owning democracy to fall short of achieving a more just digital economy: a lack of workplace democracy, limitations in terms of scope, and a lack of democratic control over long-term investment decisions in new technology. The article thus contributes to determining what is at stake in recent debates about how to democratize the digital economy.

Technology Opportunity Recognition Algorithm and Decision Assistance for Non-Drug Antidepressant Field in China

The aim of this study is to propose a framework for non-pharmaceutical antidepressant technology opportunity identification in China, based on patent analysis and assisted decision making. Semantic mining technology was used to divide technology life cycle, classify technology, identify key technology associated opportunities, and identify core technology functions of 532 non-drug antidepressant patent data in China from 1995 to 2021. Combined with co-occurrence and multi-dimensional scale analysis, strategic coordinate maps and technology decision assistance maps were drawn. The development of non-drug antidepressant technology in China was divided into embryonic stage, formative stage, and development stage. Six technical categories, 30 high-frequency technical associated opportunities, and low-frequency and high-frequency core technical opportunities for non-drug antidepressants were summarized. Based on four technical fields and four functional clusters of strategic coordinate graphs, the technical opportunity decision auxiliary atlas was constructed. By comparison, this map is more intuitive than the technology-efficacy matrix, when presenting technology efficiency mapping. This study provides a set of systematic algorithms for opportunity identification and decision assistance based on patent data, in addition to reference for strategic decision making of non-drug antidepressant technology subjects in China.

False data injection attack detection in dynamic power grid: A recurrent neural network-based method

The smart grid greatly facilitates the transmission of power and information by integrating precise measurement technology and efficient decision support systems. However, deep integration of cyber and physical information entails multiple challenges to grid operation. False data injection attacks can directly interfere with the results of state estimation, which can cause the grid regulator to make wrong decisions and thus poses a huge threat to the stability and security of grid operation. To address this issue, we propose a detection approach against false data injection attacks for dynamic state estimation. The Kalman filter is used to dynamically estimate the state values from IEEE standard bus systems. A long short-term memory (LSTM) network is utilized to extract the sequential observations from states at multiple time steps. In addition, we transform the attack detection problem into supervised learning problem and propose a deep neural network-based detection approach to identify attacks. We evaluate the effectiveness of the proposed detection approach in multiple IEEE standard bus systems. The simulation results demonstrate that the proposed detection approach outperforms benchmarks in improving the detection accuracy of malicious attacks.

Прототип системы поддержки принятия технологических решений для процесса ЭЛС

Прототип системы поддержки принятия технологических решений для процесса ЭЛС

SUBSTANTIATION OF THE CHOICE OF A RATIONAL ORGANIZATIONAL AND TECHNOLOGICAL DECISION FOR THE CONSTRUCTION OF THE UNDERGROUND PART OF HIGH-RISE BUILDINGS WHEN USING THE “UP−DOWN” METHOD

Problem statement. There is a noticeable tendency to increase the number of storeys of buildings and constructions with the development of urbanisation and growing demand for urban development. Analysis of the number of residential and public buildings damaged due to hostilities in Ukraine shows the prospects of restoring the large settlements by rebuilding the high-rise buildings, increasing the total number of storeys, and forming the infrastructure complexes of a new generation. Limited time and planned investments increase involving international finance institutions caused the problem with the effective evaluation of methods for constructing high-rise buildings, considering extremely short deadlines on the major construction and installation work. Purpose of the article – is to develop a method of choice of a rational organisational and technological decision for constructing the underground parts of high-rise buildings, which is based on an analysis of the factor of construction work complexity, considering the “up−down” method and the experts' evaluation method. Conclusion. The choice of the reasonable organisational and technological decision regarding the construction of high-rise buildings' underground parts considering the “up−down” method was justified. The factors that impact the complexity of construction work in the construction of underground parts of high-rise buildings were revealed. It was determined that for the construction of the pit in the construction of the underground part of high-rise buildings, which have deep foundations and a multi-storey system of deep floors, with the “up−down” method application, the use of the semi-closed method is reasonable. To make a justified decision on the choice of rational option for constructing the pit in the construction of the underground part of high-rise buildings by the “up−down” method for each case of changing the initial project data, performing the analysis according to the proposed method was recommended.

Responsible innovation in the age of science conspiracism

ABSTRACT Responsible innovation is centered around the ideal that societal stakeholders are entitled to participate in scientific and technological decision-making by voicing their needs and worries. Individuals who believe in science conspiracies (referred to here as ‘science conspiracists’) pose a challenge to implementing this ideal because it is not clear under what conditions their inclusion in responsible innovation exercises is possible and advisable. Yet precisely because of this uncertain status, science conspiracists constitute an instructive case in point to travel towards the edges of inclusion and understand how we draw the line between ‘includables' and ‘unincludables’. In this paper, we seek to explore this relationship between responsible innovation and science conspiracism by using the method of thought experimentaiton. We test four possible exclusion criteria for science conspiracists. We conclude by revisiting the relationship between conspiracism and responsible innovation and sketching a novel perspective on the ideal of stakeholder inclusion.

Supply Chain Efficiency and Effectiveness Management Using Decision Support Systems

In supply chain management, decision support systems and time series forecasting play an essential role. The accuracy of time-series predictions is critical for the performance optimization of every supply chain. This article suggests a method based on state-space modelling (SSM) for structured time series forecasting. Technology and advance implementations of decision support systems (DSS) have improved considerably. DSS has been used as a more restricted functionality of the database, modelling, and user interface, although technical advances made DSS even more effective. Web development has facilitated inter-organizational decision-making support systems and has resulted in many innovative implementations of current technology and many new decision-making technologies. The study of multiple configurations shows that the SSM and DSS are ideal for solving the problem being studied; in particular, the DSS guarantees appropriate prediction errors and a correct computational effort to provide adequate customer order plans.

Autonomous decision-making for dogfights based on a tactical pursuit point approach

Although unmanned aerial vehicles (UAVs) have become increasingly indispensable in modern warfare, technology for UAV autonomous decision-making in within-visual-range aerial combat is still a tricky problem because of the complexity of air combat scenarios and rapid situation changes. This paper proposes a novel tactical pursuit point (TPP)-based autonomous decision-making framework to improve UAV decision-making abilities in dogfight scenarios, in which the decision-making problem is decomposed into two procedures: parameterized decision space generation and autonomous decision-making based on the decision space. First, a tactical pursuit space approach is presented to define the parameterized decision space as a convex hull of six tactical pursuit bases with distinct tactical purposes. In this way, a broad set of alternative TPPs can be formed by one decision-making vector. Then, an adaptable approach for both reinforcement learning methods and expert-knowledge-based methods is proposed to easily generate this decision-making vector. Finally, the effectiveness of the proposed method is validated by numerical simulations. It is verified that the TPP-based framework enriches a fighter's decision space while enabling better turning performance and that appropriate maneuvers can be performed on the basis of this framework. The overall win rate is also demonstrated to be dominant.

Design Technology and AI-Based Decision Making Model for Digital Twin Engineering

This research considers the problem of digital twin engineering in organizational and technical systems. The theoretical and methodological basis is a fundamental scientific work in the field of digital twins engineering and applied models. We use methods of a system approach, statistical analysis, operational research and artificial intelligence. The study proposes a comprehensive technology (methodological approach) for digital twin design in order to accelerate its engineering. This technology consists of design steps, methods and models, and provides systems synthesis of digital twins for a complex system (object or process) operating under uncertainty and that is able to reconfigure in response to internal faults or environment changes and perform preventive maintenance. In the technology structure, we develop a simulation model using situational “what-if” analysis and based on fuzzy logic methods. We apply this technology to develop the digital twin prototype for a device at the creation life cycle stage in order to reduce the consequences of unpredicted and undesirable states. We study possible unforeseen problems and device faults during its further operation. The model identifies a situation as a combination of failure factors of the internal and external environment and provides an appropriate decision about actions with the device. The practical significance of the research is the developed decision support model, which is the basis for control systems to solve problems related to monitoring the current state of technical devices (instruments, equipment) and to support adequate decisions to eliminate their dysfunctions.

Valuing Health Gain from Composite Response Endpoints for Multisystem Diseases

ObjectivesThis study aimed to demonstrate how to estimate the value of health gain after patients with a multisystem disease achieve a condition-specific composite response endpoint. MethodsData from patients treated in routine practice with an exemplar multisystem disease (systemic lupus erythematosus) were extracted from a national register (British Isles Lupus Assessment Group Biologics Register). Two bespoke composite response endpoints (Major Clinical Response and Improvement) were developed in advance of this study. Difference-in-differences regression compared health utility values (3-level version of EQ-5D; UK tariff) over 6 months for responders and nonresponders. Bootstrapped regression estimated the incremental quality-adjusted life-years (QALYs), probability of QALY gain after achieving the response criteria, and population monetary benefit of response. ResultsWithin the sample (n = 171), 18.2% achieved Major Clinical Response and 49.1% achieved Improvement at 6 months. Incremental health utility values were 0.0923 for Major Clinical Response and 0.0454 for Improvement. Expected incremental QALY gain at 6 months was 0.020 for Major Clinical Response and 0.012 for Improvement. Probability of QALY gain after achieving the response criteria was 77.6% for Major Clinical Response and 72.7% for Improvement. Population monetary benefit of response was £1 106 458 for Major Clinical Response and £649 134 for Improvement. ConclusionsBespoke composite response endpoints are becoming more common to measure treatment response for multisystem diseases in trials and observational studies. Health technology assessment agencies face a growing challenge to establish whether these endpoints correspond with improved health gain. Health utility values can generate this evidence to enhance the usefulness of composite response endpoints for health technology assessment, decision making, and economic evaluation.

Valuation of Early-stage Technology via Machine Learning: An Application to UK Pharmaceutical Firms

We present a conceptual and analytical framework for valuing early-stage technology using machine learning and deep learning techniques, with a focus on UK pharmaceutical startups. We demonstrate that the value of technology can be quantified using technological novelty, market potential, risks, incentive regulation, and product lifecycle. Non-linear machine learning algorithms, such as tree and boosting methods, outperform traditional linear regression and can achieve over 90% predictive power. We also extend our framework to integrate hand-collected company descriptions in order to capture more details in technical content using deep learning. This paper gives implications for technology financing, transfer and decision making.

Analysis of Financial Knowledge Factors, Financial Attitude and Financial Behavior Toward User Financial Technology (Fintech) Decisions At Mercu Buana University

One of the latest technological developments in Indonesia is financial technology or Financial Technology (Fintech). In the current era of modern technology, the use of money in physical form has begun to be eliminated with more modern technology, namely digital wallets. Fintech payment offers an easy method of financial technology and the use of money that becomes more practical and efficient. Fintech is also seen as a new market that integrates finance and technology and replaces traditional financial structures with new technology-based processes. Fintech can provide services with ease and minimize costs compared to traditional methods that already exist. The purpose of this research is to find out how the influence of financial literacy on the decisions of users of financial technology (Fintech) in the city of West Jakarta. The sampling method in this study is a quantitative method with an associative approach. The sampling technique used in this study was non-probability sampling using accidental sampling.

FEATURES OF DESIGN OF ADAPTIVE MULTIFUNCTIONAL HEADWEAR – BALAKLAVA

The paper presents the results of information research on modern adaptive multifunctional clothing. In addition to the main functions, these clothes have additional functions that expand their purpose. The design of such products aims to improve the efficiency of human adaptation to specific conditions through a multifunctional system, clothing. This process requires integrating research into comfort, functionality, and protective properties of materials and clothing, including environmental protection and fashion sustainability. The classification of adaptive multifunctional clothes by functionality, usefulness, type of transformation, type of means of influence, and duration of impact is developed. It is proposed to expand the functionality of the balaclava headdress by giving it therapeutic and prophylactic properties. The product will positively affect the human body by affecting the biologically active areas on the head and neck. Based on the analysis of existing balaclavas, their classifier was developed. The most widespread constructive and technological decisions of such products are defined. It is established that modern balaclavas are designed mainly in the unisex style. Their design often consists of three parts, which gives the product high ergonomic comfort and the possibility of transformation.

Drilling Waste Disposal Technology Using Soil Cement Screens

Known technological solutions for well drilling waste utilization are analyzed. The technological decision of the device of storage of drilling waste from soil-cement elements with the device of a covering from soil-cement is offered. It is proposed to fill the repository with waste in layers of drilling mud, site soil, using a layer of composition for neutralization with the use of ash burning wood residues. The composition is proposed to be prepared near storage facilities. It is proposed to make the neutralizing composition periodically in the sludge storage as they are filled. In the case of a high level of sludge contamination with petroleum products, sorbents are added in addition to filling the storage. The thickness of the layers is chosen based on optimal humidity. It is offered to add ash from the Mykolayiv thermal power plant in the amount of 5 wt.% By weight of cement when installing the walls and covering the sludge storage from soil cement. It was found that with increasing the shelf life of samples in water to 270 days increases the average compressive strength of soil cement samples without additives and with the addition of the appropriate percentage of fly ash by more than 7%.

Transcriptional programming in a Bacteroides consortium

Bacteroides species are prominent members of the human gut microbiota. The prevalence and stability of Bacteroides in humans make them ideal candidates to engineer as programmable living therapeutics. Here we report a biotic decision-making technology in a community of Bacteroides (consortium transcriptional programming) with genetic circuit compression. Circuit compression requires systematic pairing of engineered transcription factors with cognate regulatable promoters. In turn, we demonstrate the compression workflow by designing, building, and testing all fundamental two-input logic gates dependent on the inputs isopropyl-β-D-1-thiogalactopyranoside and D-ribose. We then deploy complete sets of logical operations in five human donor Bacteroides, with which we demonstrate sequential gain-of-function control in co-culture. Finally, we couple transcriptional programs with CRISPR interference to achieve loss-of-function regulation of endogenous genes—demonstrating complex control over community composition in co-culture. This work provides a powerful toolkit to program gene expression in Bacteroides for the development of bespoke therapeutic bacteria.

Поддержка принятия решения при выборе метода глобального освещения на основе кроссплатформенного приложения

Decision-making technology is considered on the example of developing a specialized DSS for developers of information visualization systems. An urgent task is to support the choice of a global illumination method from more than 60 existing ones. The formulation of this problem is based on the analysis of the subject area of global illumination methods and the accepted indicators for their evaluation. The simplest options for organizing a rational solution to the problem are given. The developed DSS is a desktop cross-platform software application based on the AvaloniaUI platform, which can be used as an example of typical solutions to such problems.

Multiple-UAV Reinforcement Learning Algorithm Based on Improved PPO in Ray Framework

Distributed multi-agent collaborative decision-making technology is the key to general artificial intelligence. This paper takes the self-developed Unity3D collaborative combat environment as the test scenario, setting a task that requires heterogeneous unmanned aerial vehicles (UAVs) to perform a distributed decision-making and complete cooperation task. Aiming at the problem of the traditional proximal policy optimization (PPO) algorithm’s poor performance in the field of complex multi-agent collaboration scenarios based on the distributed training framework Ray, the Critic network in the PPO algorithm is improved to learn a centralized value function, and the muti-agent proximal policy optimization (MAPPO) algorithm is proposed. At the same time, the inheritance training method based on course learning is adopted to improve the generalization performance of the algorithm. In the experiment, MAPPO can obtain the highest average accumulate reward compared with other algorithms and can complete the task goal with the fewest steps after convergence, which fully demonstrates that the MAPPO algorithm outperforms the state-of-the-art.