Assessing the readiness of technology and information technology adoption according to tam theory to serve research on the intentions to buy and sell steel of companies in ho chi minh city

Assessing the Readiness of Technology and Information Technology Adoption According to TAM Theory to Serve Research on the Intentions to Buy and Sell Steel of Companies in Ho Chi Minh Ci

TRUONG DAI HOC VAN LANG

KHOA THUONG MAI

Assessing the Readiness of Technology and Information Technology Adoption According to TAM Theory to Serve Research on the Intentions to Buy and Sell Steel

of Companies in Ho Chi Minh City

Abstract: Businesses are increasingly focused on the steel market in today's globalized context Seeking sustainable production and business methods that align with the United Nations’ Sustainable Development Goals is critically important To adapt to

environmental and economic challenges, companies are gradually transitioning to more environmentally friendly and efficient steel production methods Steel 1s a pivotal industry with consistently high market demand Therefore, this study integrates TAM theory to examine and assess the demand factors and purchasing intentions of steel among businesses in Ho Chi Minh City, based on the form-making method

Keywords: TAM theory, Steel, UN goals, environmentally friendly

1 Introduction

The steel industry is pivotal to the global economy, underpinning major sectors such as construction, automotive manufacturing, and infrastructure development As one of the most commonly used materials worldwide, the demand for steel continues to grow annually, reflecting its critical role in various industrial applications

However, the steel industry faces significant challenges, including price volatility, environmental regulations, and global competition Regarding the former, fluctuations in the prices of raw materials such as iron ore and coking coal make cost management and price forecasting exceedingly challenging According to the environmental regulations, increasingly stringent environmental standards require steel producers to invest in cleaner technologies and processes to minimize their ecological footprint Moreover, intense global competition, especially from countries with lower production costs like China and

India, compels companies to innovate and improve operational efficiencies to maintain competitiveness Given these dynamics, it is crucial for companies to develop and implement effective business strategies to navigate the complexities of the steel market successfully

The study focuses on the strategic analysis of the steel purchasing process within the context of fluctuating market conditions and increasing global competition

Understanding how companies make purchasing decisions and what factors influence these decisions are essential for optimizing the procurement strategy in the steel industry

2 Literature Review

In exploring factors influencing the purchasing decisions in the steel industry, the study draws upon various theoretical frameworks and previous research findings that are analogous to those in other industries, such as the automotive sector For instance,

"Consumers' Purchase Intentions of Green Electric Vehicles: The Influence of

Consumers’ Technological and Environmental Considerations" by Bireswar Dutta and Hsin-Ginn Hwang provides insights into how environmental and technological factors influence purchasing decisions

The current study will employ a similar methodological approach, utilizing models such as the Theory of Planned Behavior (TPB) to understand the behavioral intentions behind steel purchasing Structural Equation Modeling (SEM) and Confirmatory Factor Analysis (CFA) will be used to analyze the data collected from companies in Ho Chi Minh City that frequently purchase steel This approach will help identify not only the direct factors such as price, quality, and supply reliability that influence purchasing decisions but also broader organizational and environmental factors that may play a role Consumer Purchase Intention in the Steel Industry

Purchase intention, within the context of the steel industry, represents the probability that a business will finalize the purchase of steel products from suppliers This intention is influenced by several factors that indicate the readiness of businesses to procure steel based on their specific needs and circumstances

Theoretical Models Applied to Steel Purchasing

Theory of Planned Behavior (TPB) According to Icek Ajzen (2002), behavioral intentions in purchasing can be predicted by three key factors: attitude toward the behavior, subjective norms, and perceived behavioral control In the context of steel purchasing:

Attitude The business’s positive or negative evaluation of purchasing steel Subjective Norms The influence of societal and industry norms on the decision- making process

Perceived Behavioral Control The perceived ease or difficulty of purchasing steel, influenced by factors such as supplier relationships and market conditions Technology Acceptance Model (TAM) Originally introduced by Fred Davis in

1986, TAM can be adapted to understand the acceptance of technological advancements

in steel manufacturing and logistics It examines:

Perceived Usefulness How businesses perceive the benefits of advanced steel products or enhanced supply chain technologies in meeting their operational needs Perceived Ease of Use The degree to which businesses believe that enareging with innovative technologies for steel procurement is free from effort

Applying Structural Equation Modeling (SEM) in Steel Purchasing Research Partial Least Squares Structural Equation Modeling (PLS-SEM) This statistical technique will be utilized to analyze the relationships between the theoretical constructs

of TPB and TAM within the steel purchasing context PLS-SEM 1s ideal for this study as

it allows the incorporation of both measurement models (which reflect how observable

variables represent constructs) and structural models (which depict how constructs interrelate)

Measurement Model Focuses on the validity and reliability of the constructs used

to measure attitudes, norms, and control perceptions

Structural Model Assesses the strength and direction of the relationships between constructs, predicting how factors like perceived usefulness and ease of use influence purchase intentions

Research Methodology

Qualitative Research, In-depth interviews and focus groups with key stakeholders

in the steel industry to gather detailed insights into the factors influencing their

purchasing decisions

Quantitative Research Surveys distributed to a broad sample of businesses within the steel industry to quantify the influence of numerous factors on purchase intentions and technology acceptance

The adaptation of these theoretical frameworks and methodologies will provide a robust basis for understanding and analyzing the factors that influence steel purchasing decisions in Vietnam, helping to identify key areas for improvement and strategic development in the industry

3 Theoretical Framework and Research Hypotheses

3.1 Theoretical Framework

Consumer Purchase Intention in the Steel Industry Purchase intention, also known as Purchasing Intent, refers to the likelihood of consumers being ready to finalize the purchase of specific steel products or services from businesses In the steel mdustry, this intention may be influenced by factors such as price, product quality, and supplier reliability

According to Ajzen (2002), behavioral intention 1s shaped by three factors: attitude toward the behavior, subjective norm, and perceived behavioral control In the context of steel trading:

Attitude The positive or negative feelings of businesses towards purchasing steel Subjective Norm The influence of societal and industry norms on the decision- making process

Perceived Behavioral Control The perceived ease or difficulty of procuring steel According to Elbeck (2008), purchase intention is manifested through potential customers’ willingness to buy steel products A company's business strategies can be informed by market surveys of consumers’ purchasing intentions in the steel industry Technology Acceptance Model (TAM) Applied to Steel Trading The Technology Acceptance Model (TAM), introduced by Fred Davis in 1986, is a theoretical framework that explains how users evaluate and adopt technology In the context of steel trading, TAM can be adapted to understand the acceptance of technological solutions or digital platforms for procurement processes

Perceived Usefulness The extent to which businesses believe that adopting new technological solutions for steel procurement will benefit their operations

Perceived Ease of Use The degree to which businesses perceive that using these technological solutions will be straightforward and uncomplicated

The TAM model has been widely utilized in technology adoption research and is considered effective in explaining users’ behavior towards adopting new technologies Many studies have extended the TAM model to various fields, including steel trading, to explore additional influencing factors

Perceived

Attitude Behavioral Actual External toward intention to system

Variables using > use use

x

Perceived ease of

use

FIGURE 1 Technology Acceptance Model (TAM)

Steel Trading Company Steel trading companies, often referred to in the industry

as ST companies, specialize in the buying and selling as well as distribution of steel products Key products include steel coils, steel plates, structural steel, and construction steel These companies are often referred to by various names depending on the scale and type of products, including steel suppliers, steel distributors, and steel manufacturers For simplicity, researchers often use the term "ST Company" to refer to businesses 1n this

sector

SEM (Structural Equation Modeling) This 1s a statistical technique used to assess relationships between structural and measurement variables in research SEM allows the study of how independent variables affect dependent variables through intermediary variables This model includes a measurement model, where measurement variables are linked to theoretical concepts, and a structural model, where these concepts interact with each other SEM is commonly used to analyze how factors such as product quality, supply chain efficiency, and customer relationships impact the success of a steel trading company

——

FIGURE 2 Model assessment using PLS-SEM

Qualitative research is a method of collecting information and data m 'non- numerical’ form to obtain detailed information about the object of research, survey, or investigation (hereinafter referred to as ‘research object’) for in-depth analysis or evaluation This information is often collected through interviews, direct observations, or focus group discussions using open questions, and is often applied in cases of small, focused research samples

Quantitative research is a method of collecting information and data in the form of arithmetic and statistical data to obtain basic, general information about the research object to serve statistical and analytical purposes; In other words, quantifying data collection and analysis Information and data are often collected through surveys using large-scale questionnaires and are often applied in cases of large research samples 3.2 Hypotheses of Research

The scope of this study 1s focused on the steel industry in Vietnam, particularly in

Ho Chi Minh City, from 2023 to the present The study aims to assess the factors that influence the purchasing decisions of steel by companies in this rapidly developing economic region Ho Chi Minh City, as a significant industrial and economic hub,

provides a critical context for understanding these dynamics, given its robust growth in construction and manufacturing sectors that are major consumers of steel

3.2.1 TAM Theory and External Variables

According to Fred Davis (1986), the basic TAM Model tests the two most significant personal beliefs regarding the acceptance of information technology:

"perceived usefulness" (PU) and "perceived ease of use" (PEU) PU 1s defined as "the degree to which a person believes that using a particular system will improve his or her job performance." PEU is defined as "the degree to which a person believes that using a system will be effortless." These two behavioral beliefs are perceived, which then leads

to individual behavioral intentions and actual behavior Additionally, the research aims to examine external variables that directly affect PU and PEU and provide crucial factors to evaluate following the variables Therefore, the following hypotheses are proposed: H11: Factors for optimal performance of steel products (Tech1)

H2: Factors related to the improvement of steel products (Tech2)

H3: Factors about the disadvantages of steel products (Tech3)

H4: Factors about safety of steel products (Tech4)

H5; TAM theory (TAM1, TAM2)

3.2.2 Influence of Private-Label Brands on Purchasing Decisions

When customers have a better understanding of private-label products, they tend

to perceive them more positively Research by (Mieres et al., 2006) shows that the higher consumers’ ability to recognize private-label products, the higher their intention to purchase them Therefore, the authors believe that consumers will choose steel product brands that are more well-known and utilized than new steel product brands Thus, the following hypothesis is proposed:

H6: Brand (Br) positively influences the intention to purchase steel

3.2.3 Impact of Information Technology Quality and Services on Purchasing

Decisions

H7: Quality of information (Inf1) positively influences the intention to purchase steel H8: Quality of service (Inf2) positively influences the intention to purchase steel H9: Quality of system (Inf3) positively influences the intention to purchase steel

Strategic Orientation

ˆ

H2

H4

Ownership Hl Management H6 Financial Structure Style A Perfo e

Vv

Organizational

FIGURE 3 Research hypothesis diagram

4 Material and Methods

This study employs both qualitative and quantitative research methodologies Initially, a comprehensive review of studies on the same topic was conducted by the author Following this, consultations were held with specialists to refine the content of the scale used in the study Subsequently, an online and offline survey was conducted by the authors to gather data from participants

The collected data was then imported into Smart PLS 3.0 software for analysis Smart PLS 3.0 was chosen for its capabilities in assessing the measurement model, providing robust statistical analysis, and facilitating structural equation modeling The software aids