The Impacts of Environmental Context on Technology Adoption and

Their Invariance Analysis in Chinese Supply Chains

Zengwen Yan

and Kaining Ge

School of Intelligent Finance and Business, Xi’an Jiaotong Liverpool University, Suzhou, China

Management School, University of Liverpool, Liverpool, U.K.

Keywords: Technology Adoption, Regulatory Environment, Business Innovation Environment, Technology Performance,

Technology–Organisation–Environment Framework.

Abstract: Industry 4.0 technologies are increasingly used by corporations worldwide, but their successful adoption

remains problematic. In particular, the manufacturing and logistics industries in China have achieved more

promising outputs, supported by the adoption of emerging technologies in their supply chains. It is important

to research whether environmental context provides a conducive atmosphere for the corporate adoption of

these technologies. The study employs structural equation modelling (SEM) with data collected through 1,441

questionnaires from the manufacturing and related industries across mainland China. This paper focuses on

and discusses how environmental context affects technology adoption (TA) and post-performance based on

the technology–organisation–environment (TOE) framework. The study finds that in China, the regulatory

environment (RE) does not directly affect technology adoption and performance (TAP); rather, the business

innovation environment (BIE), greatly affected by the RE, influences TAP. This study enriches the content

on environmental context, examines the robustness and generalizability of the results.

1 INTRODUCTION

In the contemporary landscape of global industry and

technology, the concept of 'Industry 4.0' emerges as a

pivotal force reshaping the dynamics of production

and supply chain management. Xu et al. (2018)

posited that the ‘Industry 4.0 project is considered a

major endeavour for Germany to establish itself as a

leader of integrated industry’; achieving this goal

predominantly hinges on technology adoption (TA),

which is still in its nascent stages. This necessity is

widely recognized across industries, especially in

manufacturing and logistics, to maintain continuous

production and reliable supply chains amid economic

uncertainties. Digital transformation through TA

enhances supply chain resilience and visibility

(Narwane et al., 2023), highlighting TA's importance

in reducing risks and losses.

This study initiates its examination of TA in

supply chains through a technology–organisation–

environment (TOE) framework, identifying gaps

from existing literature. Notably, the research on TA's

environmental contexts is limited (Lin, 2014),

somewhat vague and broad, despite extensive studies

on its organizational and technological aspects (Yeh

and Chen, 2018). This research, therefore, focuses on

the environmental dimension of TOE. Second, it

highlights the scant attention to environmental

factors, especially the regulatory environment (RE), a

crucial and original TOE element impacting TA and

performance (TAP) that has been overlooked for

years (Schwarz and Schwarz, 2014; Yeh and Chen,

2018). This paper aims to bridge this gap by focusing

on RE, comparing it with the business innovation

environment (BIE) regarding TAP (Zhu et al., 2003).

Last, it addresses the call for testing findings from

developed economies in developing ones, examining

Chinese enterprises to glean insights into TA within

the Chinese context, responding to calls for broader

geographic research applicability (Adomako and

Danso, 2014).

2 LITERATURE REVIEW

2.1 Theoretical Background

The TOE framework developed by Tornatzky et al.

(1990) is commonly used, with the aim of facilitating

104

Yan, Z. and Ge, K.

The Impacts of Environmental Context on Technology Adoption and Their Invariance Analysis in Chinese Supply Chains.

DOI: 10.5220/0012622200003717

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 6th International Conference on Finance, Economics, Management and IT Business (FEMIB 2024), pages 104-111

ISBN: 978-989-758-695-8; ISSN: 2184-5891

the adoption of technological innovations; its

technological, organisational and environmental

contexts were identified to ascertain whether a firm

can successfully implement a technological

innovation. Besides, the TOE framework, with a

validated theoretical foundation, provides valuable

insights for TA across companies, consistent

empirical studies support the usefulness of the TOE

framework and it is argued that environmental

context contributes more to the adoption of

information technology than technological and

organisational contexts (Pan and Jang, 2008). The

environmental context refers to the external situations

a corporation may encounter, for example, the

government’s regulatory policies, the BIE created by

the local community and the corporation’s industry

competitors (Tornatzky et al., 1990).

2.2 Environmental Context of the

Technology‒Organisation‒

Environment Framework

Prior research on environmental context lacks clarity

(Schwarz and Schwarz, 2014). RE significantly

impacts TAP, with mixed findings on its effect. Zhu

and Kraemer (2005) found that government

regulations play a key role in encouraging firms to

adopt new technologies, with supportive regulations

or business laws providing incentives and fostering

trust in e-business. In China, governmental

regulations and support notably shape business

operations, and in emerging markets, the blend of

Information Technology adoption and political ties

greatly affects firms' performance (Luo et al., 2023).

Thus, enhancing understanding of the environmental

context's role in TA is a primary goal of this research,

as depicted in Figure 1.

Figure 1: Focus and potential contribution of this study.

3 HYPOTHESIS DEVELOPMENT

3.1 Regulatory Environment and

Business Innovation Environment

From a broad perspective, the notion of RE should

encompass the political climate and governance

authority, as well as policy matters of a region.

Enhancing the RE in the marketplace establishes

institutional protections for BIE, safeguarding their

accomplishments and economic gains. This, in turn,

boosts their innovative drive, with the enhancement

of RE seen as pivotal for the BIE's survival and

growth (Li et al. 2023). Opara et al. (2017) found that

the RE significantly influences the BIE in Alberta,

Canada, highlighting that political leadership and a

supportive policy milieu are essential for a thriving

BIE. The RE is deemed crucial for BIE to secure a

competitive edge, especially benefiting from robust

public safety and security, intellectual property rights

protection, and an efficient judicial and legal

framework. These factors help minimize the BIE's

regulatory compliance costs and waste.

Generally, regulations contain strong controlling

purposes of facilitating new targets—innovation

being core among these—but the link between the RE

and the BIE is indirect because it depends on the types

of regulations and targets, which is reflected in the

study of the livestock industry by Lin et al. (2023). A

business-friendly RE allows an innovation

environment to incubate and hatch because

government activities pave the way for innovation by

preparing a suitable external context, for instance,

firms in countries with flexible employment laws

raise a competitive edge over those in stricter

regulatory nations, affecting the easiness of access to

credit (Moro et al., 2022); as the BIE is rooted in the

RE.

H1: The more friendly and welcoming the RE, the

more dynamic the BIE.

3.2 Regulatory Environment and

Technology Adoption and

Performance

Prominent studies have concluded that TA is shaped

by three sets of factors, one of which is regulatory

policies. The influence of the RE on TA is significant

and the level of TA is consistent with specific

regulatory policies (Javier and Frank, 2006). In fact,

TAP is encouraged by regulatory incentives,

indicating that advanced technology is important for

corporations in many ways, but is still not widespread

The Impacts of Environmental Context on Technology Adoption and Their Invariance Analysis in Chinese Supply Chains

105

for several reasons.

Kobos et al. (2018) claimed that a connection

exists between regulatory constraints and TA,

although the effects created by regulatory factors vary

with the nature of each technology. Wang and Feeney

(2016) adopted a stakeholder perspective to explore

the regulatory behaviours of government, arguing that

a positive connection exists between the RE and TAP,

and that government and corporations share common

interests as external and internal stakeholders,

respectively. Opara et al. (2017) argued that political

support contributes to TAP in Alberta, with

government policies in that area playing the key role

in TAP. Ouyang et al. (2019) empirically tested the

supportive role of TA regulations in the hotel industry

and found that the effect of such regulations may vary

in size and scale. Peng et al. (2023) highlight the vital

role of policies supporting IT capabilities,

differentiated green innovation, and environmental

regulations in boosting green tech innovation and

corporate performance. Thus, the regulative

institutions are the primary stimulus for corporations

concerning their technology-related activities.

H2a: There is a positive relationship between a

well-regulated environment and TAP.

3.3 Business Innovation Environment

and Technology Adoption and

Performance

Innovation plays a pivotal role in giving corporations

a competitive advantage, both in the external business

environment and in their internal innovation

capability (Damanpour and Schneider, 2006). The

BIE reflects changes in customer needs and future

trends for business in relation to improving

technological capability—the BIE is closely related

to the adoption and performance of technology

because there is a high degree of uncertainty

associated with corporations’ potential success (Tidd,

2001). Indeed, complexity and uncertainty affect

organisational structure and intentions for TA.

Prajogo and Ahmed (2006) argued that to enable TA,

an active business context is required to incorporate

practices for implementation and this context

represents the enabling stimulus factor for TA.

Corporations operate within a certain environmental

context, the impulses of which lead them to further

innovation, including TA. TAP responds to the BIE,

which offers opportunities and resources—such as

information and technology—and constraints, such as

regulations and restrictions. Khanagha et al. (2013)

argued that an ideal BIE should provide appropriate

learning patterns and sufficient competences and

resources, which can contribute to TA. Similarly,

Persico et al. (2014) contended that providing a

platform is essential to introducing technology, and

changes should be gradual to achieve long-lasting

effects. Corporations in a high-level BIE are more

likely to introduce technology and obtain the

expected performance (Pan and Jang, 2008).

H2b: The BIE is positively related to TAP for

corporations, and the openness and dynamics of

innovation in the business environment contribute to

corporations being more likely to adopt technology

and achieve better technological performance.

3.4 Conceptual Model

Apart from verifying the impacts of RE on TAP, and

examining the influence of BIE on TAP, this study

also investigates the generalisability and applicability

of the improvements it has made to the TOE

framework. Given that all the hypotheses are

supported, whether the indicators of each construct

and the model are applicable in different contexts

remains doubtful. Thus, this study also conducts an

invariance analysis from the perspectives of firm size

and location. Figure 2 displays these relationships

within the conceptual model.

Figure 2: Conceptual model.

4 RESEARCH DESIGN

4.1 Measurements Development and

Data Collection

This study applied the existing scales of Zhao (2018)

as the framework and then adapted the items to

measure the RE (Adomako and Danso, 2014), the

BIE (Zhu and Kraemer, 2005) and TAP (Zhu and

Kraemer, 2005; Xu et al., 2018). New measurements

were developed as well based on the authors’

understanding of the constructs concerning the

Chinese cultural context and on observations made

during interviews and firm visits. Data were collected

from municipalities and provincial cities across

mainland China for comprehensiveness. 1,441

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106

corporations were studied, and a key informant from

each corporation was identified to complete the

questionnaire, who ensured the information about the

internal and external processes was reliable and

insightful.

4.2 Reliability and Validity

The research model was tested using structural

equation modelling (SEM) with Amos 24.0, a

covariance-based SEM. Considering the nature of this

study, the covariance-based SEM was the preferred

technique for theory testing and development, as

indicated by Fornell and Bookstein (1982). For the

TAP construct, the study adopted a second-order

mode in the measurement model. In the following two

subsections, five representative technology

constructs in the first-order measurement model—

automation technology (AT), the information

management system (IMS), the Internet of Things

(IoT), big data (Data) and the logistics platform

(Plat)—are analysed along with the RE and BIE

constructs; the third subsection discusses the second-

order TAP construct.

4.2.1 Reliability Analysis

Construct reliability refers to the degree to which

items are free from random errors and, as a result,

yield consistent results. According to the criteria

suggested by Hair et al. (2010), squared multiple

correlations (SMCs) should be greater than 0.36 to

indicate the reliability of each item for the latent

Table 1: Presents the reliability results for each construct.

SMC CR SMC CR

BIE

BIE1 .643 .836

RE1 .480 .847

BIE2 .494 RE2 .527

BIE3 .533 RE3 .646

BIE4 .575 RE4 .674

AT1 .830 .938

IMS

IMS1 .803 .891

AT2 .806 IMS2 .671

AT3 .865 IMS3 .721

IoT

IoT1 .835 .919

Data

Data1 .891 .949

IoT2 .762 Data2 .929

IoT3 .774 Data3 .764

Plat

Plat1 .856 .928

Plat2 .806

Plat3 .769

variable. The values of SMC in the measurement

model were all greater than this suggested limit.

Further, composite reliability (CR) was analysed

following Hair et al.’s (2010) suggestion that the CR

value be greater than 0.7 to indicate reliable and

consistent data within the same construct (Straub,

1989).

4.2.2 Construct Validity

Straub (1989) argued that successive stages of

refinement are necessary for developing an

appropriate measurement model. Confirmatory factor

analysis was employed to examine construct validity,

with two types of validity assessed. Convergent

validity examines consistency across multiple

operationalisations (Bagozzi et al., 1991). Here, all

standardised factor loadings (Std) ranged between 0.6

and 0.95 and were significant (p < 0.001), strongly

supporting good convergent validity for each

construct. The average variance extracted (AVE)

(Fornell and Larcker, 1981) was applied to further

confirm convergent validity. The AVE value of each

construct should exceed the threshold value of 0.5

(Hair et al., 2010).

Table 2: Convergent validity results for the BIE, RE, and

technology constructs.

Std AVE Std AVE

BIE

BIE1 .802 .561

RE1 .693 .582

BIE2 .703 RE2 .726

BIE3 .730 RE3 .804

BIE4 .758 RE4 .821

AT1 .911 .834

IMS

IMS1 .896 .732

AT2 .898 IMS2 .819

AT3 .930 IMS3 .849

IoT

IoT1 .914 .790

Data

Data1 .944 .861

IoT2 .873 Data2 .964

IoT3 .880 Data3 .874

Plat

Plat1 .925 .810

Plat2 .898

Plat3 .877

Discriminant validity compares the square root of the

AVE of a particular construct with the correlation

between that construct and other constructs. The

value of the square root of the AVE should be higher

than the correlation (Henseler et al., 2015).

The Impacts of Environmental Context on Technology Adoption and Their Invariance Analysis in Chinese Supply Chains

107

Table 3: Discriminant validity of each construct.

AVE BIE RE Data Plat AT IoT IMS

BIE .561 .749

RE .582 .744 .763

Data .861 .103 .100 .928

Plat .810 .129 .139 .538 .900

AT .834 .123 .074 .362 .329 .913

IoT .790 .110 .094 .453 .531 .420 .889

IMS .732 .058 .083 .436 .617 .335 .557 .856

4.2.3 Rationality of Second-Order Construct

The structure of TAP as a second-order construct was

described above. The paths from the TAP construct to

four of the five first-order constructs were of high

magnitude and significance, according to the

suggested limit of 0.7 (Chin, 1998). For the AT

construct, the value was quite close to 0.5: the value

suggested by Hair et al. (2010) as acceptable. Marsh

and Hocevar (1988) proposed that the efficacy of the

second-order model be evaluated through the target

coefficient (t-ratio) with an upper bound of 1, which

is the outcome of the chi-square division between the

first- and the second-order constructs. The t-ratio of

the proposed model was 0.964, which isreasonably

close to 1. This result indicates that the second-order

construct captured the key connections among the

first-order constructs (Stewart and Segars, 2002). As

a result, on both theoretical and empirical grounds,

the conceptualisation of TAP as a higher-order and

multidimensional construct was justified. In addition,

reliability and validity issues for the second-order

construct were examined. Table 4 summarises all

relevant results.

Table 4: Reliability and validity: TAP construct.

P Std SMC CR AVE

TAP

IMS 0.700 0.490 0.789 0.433

IoT *** 0.695 0.483

AT *** 0.478 0.228

Plat *** 0.744 0.554

Data *** 0.640 0.410

Note: ***

< 0.001

4.3 Model Fitness

The Goodness-of-Fit Index (GFI) and its adjusted

version (AGFI), which corrects for the number of

indicators per latent variable, assess model fit by

comparing the proposed model to observed data, with

values above 0.9 indicating acceptability (Hooper et

al., 2008). Similarly, the Comparative Fit Index (CFI)

evaluates model discrepancy, considering sample

size, with values closer to 1 suggesting a better fit;

this study's model showed a CFI of 0.98, denoting a

good fit (Teo & Khine, 2009). The Root Mean Square

Error of Approximation (RMSEA) addresses sample

size issues, aiming for values under 0.08 for

acceptable fit; the model achieved 0.04 (Hooper et al.,

2008; Hair et al., 2010). Lastly, a Chi-square to

degrees of freedom ratio between 1 and 5, as seen

with 3.325 in this study, signifies a good fit without

overfitting (James, 1987). All indices confirmed the

model's adequacy.

Table 5: Summary statistics of model fitness.

Chi-square 206.165 GFI 0.978

Degree of freedom 62.000 AGFI 0.968

Chi-square/DF 3.325 CFI 0.98

P value < 0.000 RMSEA 0.04

Standardised RMR 0.0302

5 DISCUSSION

5.1 Results

5.1.1 Conceptual Model Results Analysis

A correlation analysis of the data for possible

relationships among variables has been conducted,

the results in Table 6 reveal that RE is positively

related to BIE (p < 0.001) and significantly influences

BIE, supporting H1: a friendly and welcoming RE

leads to a dynamic BIE, and the more friendly and

welcoming the RE, the more dynamic the BIE. In

examining the effects of the RE and the BIE on TAP,

the quantified data show different results. For the

relationship between RE and TAP, H2a is rejected (p

= 0.202), indicating no significant effect of RE on

TAP in China, challenging common beliefs about

RE's importance in business across Mainland China.

That is, the present study found no direct effects on

this relationship. For the relationship between the BIE

and TAP, the significance value was p = 0.044, thus

supporting H2b at the 95% confidence level. This

implies that the BIE is positively related to TAP for

corporations, and the openness and dynamics of

innovation in the business environment contribute to

corporations being more likely to adopt technology

and achieve better technological performance.

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Table 6: Results of hypotheses.

RE BIE *** H1 Supported

RE TAP 0.202 H2a Rejected

BIE TAP 0.044* H2b Supported

Note: * p < 0.05, *** p < 0.001

5.1.2 Robustness Analysis

There might be other factors affecting the results if

the dataset were changed. Thus, the authors re-

categorised the data into sub-groups, as per features

of the data used for analysis, and selected one sub-

group to run the SEM again to test the robustness of

the above results. No significant difference was found

(Table 7) between the re-categorised data and the

above results proving the reliability of the results.

Table 7: Results of re-categorised data (coastal area).

RE BIE *** H1 Supported

RE TAP 0.841 H2a Rejected

BIE TAP 0.011* H2b Supported

Note: * p < 0.05, *** p < 0.001

To explore the generalisability of the conceptual

model in this study to other contexts, the authors

further conducted multi-group invariance analysis in

the aspects of firm size and location. Given that the

conceptual model can be replicable in each context, a

comparison of multi-group SEMs was carried out.

Byrne (2016) noted the importance of factor loadings,

covariances, and structural regression paths in

evaluating the model’s relevance for multi-group

equivalence, with results detailed in Table 8. The

model's fit was confirmed using CMIN/DF, AGFI,

CFI, and RMSEA metrics.

To test the invariance of multi-group SEMs, the p

value, ΔCFI, and ΔTLI are key, the latter two are

frequently employed to assist with the judgment of

invariance results. Despite the p value's limitations,

Little (1997) and Cheung and Rensvold (2002)

highlighted ΔTLI ≤ 0.05 and ΔCFI ≤ 0.01 as

indicators of invariance to be supported, respectively.

Table 8 shows the values of p, ΔCFI and ΔTLI

confirm the invariance of multi-group SEMs in the

context of the firm location. As for the context of the

firm size, though the p-value is significant and rejects

the invariance from a statistical perspective, the

values of ΔCFI and ΔTLI support the invariance of

multi-group SEMs in the context of firm size.

Therefore, the study supports the conceptual model's

invariance and general applicability.

Table 8: Fit goodness and comparison of multi-group

invariance results.

Model CMIN DF CMIN/DF AGFI CFI RMSEA P ΔCFI ΔTLI

Large

vs.

SMEs

Baseline 279.902 127 2.204 0.958 0.979 0.029

Measurement

weights

329.217 137 2.403 0.955 0.973 0.031 *** −0.006 0.004

Structural

covariances

330.552 138 2.395 0.955 0.973 0.031 0.248 0 0

Structural

residuals

348.930 140 2.492 0.952 0.971 0.032 *** −0.002 0.002

Coastal

vs.

Inland

Baseline 296.148 127 2.332 0.956 0.976 0.030

Measurement

weights

301.629 137 2.202 0.958 0.977 0.029 0.857 0.001 −0.003

Structural

covariances

304.861 138 2.209 0.958 0.977 0.029 0.072 0 0

Structural

residuals

305.282 140 2.181 0.959 0.977 0.029 0.81 0 −0.001

Note: *** p < 0.001

5.2 Managerial Implications

Municipal governments should use emerging

technologies to boost city competitiveness and

innovation, fostering business growth and supply

chain development. Government should act as a

service provider, supporting a BIE and using online

platforms for enterprise services. Industry

associations mediate between government and

businesses, influencing policy for a dynamic business

climate. For technology startups, easy access to

venture capital, local education, and industry

associations support technology implementation and

innovation. Information platforms also play a crucial

role in TAP. This study offers insights for

policymakers, industry associations, investors,

corporate management, and professionals on using

emerging technologies to enhance operational

efficiency and innovation, benefiting both

government and industry by understanding TAP's

impact.

5.3 Theoretical Implications

This study reveals three key theoretical implications

of testing a conceptual model. Firstly, it shows that

TA in supply chains, especially in China, is driven by

external corporate environments and faces challenges

in practical implementation due to the gap between

research and industry practices. It highlights the

reluctance in adopting new technologies due to

uncertain outcomes. This study provides successful

TAP evidence and increases corporations’ confidence

in implementing emerging technologies in their

supply chains. Secondly, it contributes to the TOE

framework by focusing on the environmental

context's role in TA, a previously underexplored area,

and distinguishes between regulatory and business

environmental impacts on TA in Chinese supply

chains. Further, it suggests a new direction for

analysing the BIE as the root cause of TAP. Lastly,

The Impacts of Environmental Context on Technology Adoption and Their Invariance Analysis in Chinese Supply Chains

109

it applies findings from developed contexts to

developing ones like China, showing that Chinese

corporations are affected by government policies

similarly to those in mature economies, suggesting a

shift towards a more mature market economy in

China. This challenges traditional views and

emphasizes the evolving role of government

regulations in supporting corporate needs in China.

6 CONCLUSIONS

Based on the TOE framework, this study adds to the

literature by examining how environmental factors

impact TAP from an environmental viewpoint. It

finds a connection between the RE and BIE, offering

a fuller view of TAP adoption in Chinese supply

chains before and after. To prevent TA failure,

companies need to fully assess their environments

since RE doesn't directly affect TAP success. Instead,

BIE, stemming from RE, plays a key role in whether

firms can successfully adopt new technologies to

boost performance. As emerging technologies are

complex, their application in production needs

ongoing focus to better TA effectiveness, lower

failure rates, enhance performance, and increase

competitiveness.

Due to length constraints, more details on

measurement development, sample and data

collection, and numerical analysis results can be

provided by contacting the authors for those

interested.

REFERENCES

Adomako, S. and Danso, A. (2014), “Regulatory

environment, environmental dynamism, political ties,

and performance: study of entrepreneurial firms in a

developing economy”, Journal of Small Business and

Enterprise Development, Vol. 21 No. 2, pp. 212–30.

Bagozzi, R. P., Youjae, Y. and Phillips, L. W. (1991),

“Assessing construct validity in organizational

research”, Administrative Science Quarterly, Vol. 36

No. 3, pp. 421–58.

Byrne, B. M. (2016), Structural equation modeling with

Amos: basic concepts, applications, and programming,

3rd edn, Routledge, New York, NY.

Cheung, G. W. and Rensvold, R. B. (2002), “Evaluating

goodness-of-fit indexes for testing measurement

invariance”, Structural Equation Modeling, Vol. 9, pp.

233–55.

Chin, W. W. (1998), “Issues and opinion on structural

equation modeling”, MIS Quarterly, Vol. 22 No. 1, pp.

vii–xvi.

Damanpour, F. and Schneider, M. (2006), “Phases of the

adoption of innovation in organizations: effects of

environment, organization and top managers”, British

Journal of Management, Vol. 17 No. 3, pp. 215–36.

Fornell, C. and Bookstein, F. L. (1982), “Two structural

equation models: LISREL and PLS applied to

consumer exit-voice theory”, Journal of Marketing

Research, Vol. 19 No. 4, pp. 440–52.

Fornell, C. and Larcker, D. F. (1981), “Evaluating structural

equation models with unobservable variables and

measurement error”, Journal of Marketing Research,

Vol. 18 No. 1, pp. 39–50.

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E. and

Tatham, R. L. (2010), Multivariate data analysis: a

global perspective, Pearson-Hall International, Upper

Saddle River, NJ.

Henseler, J., Ringle, C. M. and Sarstedt, M. (2015), “A new

criterion for assessing discriminant validity in variance-

based structural equation modeling”, Journal of the

Academy of Marketing Science, Vol. 43 No. 1, pp. 115–

35.

Hooper, D., Coughlan, J. and Mullen, M. (2008),

“Structural equation modelling: guidelines for

determining model fit”, Electronic Journal of Business

Research Methods, Vol. 6 No. 1, pp. 53–60.

James, C. A. (1987), “An approach for confirmatory

measurement and structural equation modeling of

organizational properties”, Management Science, Vol.

33 No. 4, pp. 525–41.

Javier, C. and Frank, W. (2006), “Information technology

adoption and political regimes”, International Studies

Quarterly, Vol. 50 No. 4, pp. 911–33.

Khanagha, S., Volberda, H., Sidhu, J. and Oshri, I. (2013),

“Management innovation and adoption of emerging

technologies: the case of cloud computing”, European

Management Review, Vol. 10 No. 1, pp. 51–67.

Kobos, P. H., Malczynski, L. A., Walker, L. T. N., Borns,

D. J. and Klise, G. T. (2018), “Timing is everything: a

technology transition framework for regulatory and

market readiness levels”, Technological Forecasting

and Social Change, Vol. 137, pp. 211–25.

Li, X., Tang, J. and Huang, J. (2023) ‘Place-based policy

upgrading, business environment, and urban

innovation: Evidence from high-tech zones in China’

, International Review of Financial Analysis

, 86.

Little, T. D. (1997), “Mean and covariance structures

(MACS) analyses of cross-cultural data: practical and

theoretical issues”, Multivariate Behavioral Research,

Vol. 32, pp. 53–76.

Luo, G., Serrão, C., Liang, D., & Zhou, Y. (2023) ‘A

Relevance-Based Technology – Organisation –

Environment Model of Critical Success Factors for

Digital Procurement Adoption in Chinese Construction

Companies’, Sustainability, 15(16), p. 12260.

Marsh, H. W. and Hocevar, D. (1988), “A new, more

powerful approach to multitrait-multimethod analyses:

application of second-order confirmatory factor

analysis”, Journal of Applied Psychology, Vol. 73 No.

1, pp. 107–17.

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110

Moro, A., Maresch, D., Ferrando, A. & Udell., (2022) ‘

Funding innovation and the regulatory environment –

The role of employment protection legislation ’ ,

Journal of Business Research, 145, pp. 745-756–756.

Narwane, VS, Raut, RD, Mangla, SK, Dora, M &

Narkhede, BE. (2023) ‘Risks to Big Data Analytics

and Blockchain Technology Adoption in Supply

Chains’, Annals of Operations Research, 327(1), pp.

339–374.

Opara, M., Elloumi, F., Okafor, O. and Warsame, H.

(2017), “Effects of the institutional environment on

public–private partnership (P3) projects: evidence from

Canada”, Accounting Forum, Vol. 41 No. 2, pp. 77–95.

Ouyang, Z., Wei, W. and Chi, C. G. (2019), “Environment

management in the hotel industry: does institutional

environment matter?”, International Journal of

Hospitality Management, Vol. 77, pp. 353–64.

Pan, M. and Jang, W. (2008), “Determinants of the adoption

of enterprise resource planning within the technology–

organization–environment framework: Taiwan’s

communications industry”, Journal of Computer

Information Systems, Vol. 48 No. 3, pp. 94–102.

Peng, Y., Zhou, J., Lin, M., & Feng, D. (2023) ‘The

Impact of IT Capability on Corporate Green

Technological Innovation: Evidence from

Manufacturing Companies in China ’ , Journal of

Information & Knowledge Management, 22(1), pp. 1–

24.

Persico, D., Manca, S. and Pozzi, F. (2014), “Adapting the

technology acceptance model to evaluate the innovative

potential of e-learning systems”, Computers in Human

Behavior, Vol. 30, pp. 614–22.

Prajogo, D. I. and Ahmed, P. K. (2006), “Relationships

between innovation stimulus, innovation capacity, and

innovation performance”, R&D Management, Vol. 36

No. 5, pp. 499–515.

Schwarz, C and Schwarz A. (2014), “To Adopt or not to

adopt: a perception-based model of the EMR

technology adoption decision utilizing the technology

organization environment framework”, Journal of

Organizational and End User Computing, Vol. 26, pp.

57–79.

Stewart, K. A. and Segars, A. H. (2002), “An empirical

examination of the concern for information privacy

instrument”, Information Systems Research, Vol. 13

No. 1, pp. 36–49.

Straub, D. W. (1989), “Validating instruments in MIS

research”, MIS Quarterly, Vol. 13 No. 2, pp. 147–169.

Teo, T. and Khine, M. S. (2009), Structural equation

modeling in educational research, Sense Publishers,

Rotterdam, Holland.

Tidd, J. (2001), “Innovation management in context:

environment, organization and performance”,

International Journal of Management Reviews, Vol. 3

No. 3, pp. 169–83.

Tornatzky, L. G., Fleischer, M. and Chakrabarti, A. K.

(1990), Processes of technological innovation,

Lexington Books, Lexington, MA.

Wang, S. and Feeney, M. K. (2016), “Determinants of

information and communication technology adoption in

municipalities”, American Review of Public

Administration, Vol. 46 No. 3, pp. 292–313.

Xu, L. D., Xu, E. L. and Li, L. (2018), “Industry 4.0: state

of the art and future trends”, International Journal of

Production Research, Vol. 56 No. 8, pp. 2941–62.

Yeh, C and Chen, Y. (2018), “Critical success factors for

adoption of 3D printing”, Technological Forecasting

and Social Change, Vol. 132, pp. 209–16.

Zhao, X. (2018), Logistics technology development report

of China 2018—technology assisting logistics, CEIBS-

GLP Supply Chain and Services Innovative Center,

Shanghai, China.

Zhu, K. and Kraemer, K. L. (2005), “Post-adoption

variations in usage and value of e-business by

organizations: cross-country evidence from the retail

industry”, Information Systems Research, Vol. 16 No.

1, pp. 61–84.

Zhu, K., Kraemer, K. and Xu, S. (2003), “Electronic

business adoption by European firms: a cross-country

assessment of the facilitators and inhibitors”, European

Journal of Information Systems, Vol. 12 No. 4, pp. 251–

68.

The Impacts of Environmental Context on Technology Adoption and Their Invariance Analysis in Chinese Supply Chains

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