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Key Factors of Government Knowledge Base Adoption in First-, Second- and Third-Tier Cities in China

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Wisdom, Well-Being, Win-Win (iConference 2024)

Part of the book series: Lecture Notes in Computer Science ((volume 14598))

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Abstract

The government knowledge base (GKB) has increasing significance nowadays as a means of achieving enhanced connectivity, productivity, availability, and efficiency. The facilitators and barriers to adopting a government knowledge base are unknown from the perspective of civil servants. This research seeks to identify and compare critical factors that impact government knowledge base adoption in first-, second- and third-tier cities in China. According to the literature review, factors from the UTAUT and TOE framework were integrated, and trust to knowledge base and intention to knowledge reuse were introduced to the research model. A questionnaire was designed based on the model and distributed to civil servants. The proposed model was validated, and the collected data was analyzed by PLS-SEM. The results show the factors (effort expectancy, social influence, competitive pressure, trust to knowledge base, intention to knowledge reuse) have a positive impact on the adoption of knowledge base in local governments in all cities while performance expectancy and compatibility have a positive impact on the adoption only in first- and second-tier cities. Facilitating conditions do not positively impact the adoption of a knowledge base in any city. The development of a new GKB adoption model has contributed to a new theoretical finding in the area of digital government. The outcomes will provide insights to local governments seeking to make investment decisions on knowledge base adoption.

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Author information

Authors and Affiliations

  1. Wuhan University, Wuhan, Hubei, People’s Republic of China

    Jing Zhou & Li Si

  2. Center for Studies of Information Resources, Wuhan, Hubei, People’s Republic of China

    Li Si

Authors
  1. Jing Zhou
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  2. Li Si
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Corresponding author

Correspondence to Jing Zhou .

Editor information

Editors and Affiliations

  1. iSchool organization, Berlin, Germany

    Isaac Sserwanga

  2. University of Tsukuba, Tsukuba, Japan

    Hideo Joho

  3. Jilin University, Changchun, China

    Jie Ma

  4. Stockholm University, Kista, Sweden

    Preben Hansen

  5. Wuhan University, Wuhan, China

    Dan Wu

  6. University of Tsukuba, Tsukuba, Japan

    Masanori Koizumi

  7. University of California, Los Angeles, CA, USA

    Anne J. Gilliland

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Appendices

APPENDIX A. Descriptive Analysis of the Respondents

Demographics

Category

Respondents in second-tier cities

Respondents in third-tier cities

Frequency

Percentage (%)

Frequency

Percentage (%)

Age

18–25

53

25.6%

56

17.6%

26–35

63

30.4%

166

52.0%

36–45

56

27.1%

63

19.7%

46–55

26

12.6%

19

6.0%

 >55

9

4.3%

15

4.7%

Gender

Male

92

44.4%

148

46.4%

Female

115

55.6%

171

53.6%

Designation group

Top management

19

9.2%

24

7.5%

Executive

112

54.1%

205

64.3%

Support group

76

36.7%

90

28.2%

GKB adoption

Yes

156

75.7%

203

63.6%

No, but know it

36

17.4%

49

15.4%

No, never know it

15

7.2%

67

21.0%

Work experience in local government

 <1 year

59

28.5%

83

26.0%

1–5 years

114

55.1%

132

41.4%

6–10 years

25

12.1%

28

8.8%

 >10 years

9

4.3%

76

23.8%

APPENDIX B. Results of Construct Reliability and Validity

Construct

Data of first- and second-tier cities

Data of third-tier cities

Cronbach’s alpha

Composite reliability (rho_a)

Average variance extracted (AVE)

Cronbach’s alpha

Composite reliability (rho_a)

Average variance extracted (AVE)

BI

0.859

0.859

0.780

0.883

0.883

0.81

CB

0.880

0.900

0.805

0.887

0.89

0.816

CP

0.905

0.915

0.842

0.911

0.919

0.85

EE

0.864

0.865

0.787

0.872

0.872

0.796

FC

0.711

0.687

0.606

0.713

0.717

0.617

IR

0.907

0.909

0.843

0.901

0.903

0.834

PE

0.903

0.906

0.838

0.906

0.908

0.842

SI

0.887

0.898

0.817

0.887

0.914

0.817

TB

0.798

0.858

0.718

0.826

0.842

0.746

APPENDIX C. Results of Fornell-Larcker Criterion

(First- and Second-tier Cities)

 

BI

CB

CP

EE

FC

IR

PE

SI

TB

BI

0.883

        

CB

0.442

0.897

       

CP

0.51

0.532

0.918

      

EE

0.66

0.452

0.382

0.887

     

FC

0.237

0.277

0.317

0.187

0.778

    

IR

0.555

0.544

0.49

0.581

0.225

0.918

   

PE

0.463

0.397

0.255

0.479

0.129

0.207

0.915

  

SI

0.653

0.488

0.547

0.524

0.327

0.361

0.468

0.904

 

TB

0.655

0.528

0.472

0.555

0.332

0.58

0.416

0.58

0.847

(Third-tier Cities)

 

BI

CB

CP

EE

FC

IR

PE

SI

TB

BI

0.9

        

CB

0.467

0.903

       

CP

0.495

0.575

0.922

      

EE

0.638

0.418

0.358

0.892

     

FC

0.136

0.198

0.197

0.09

0.785

    

IR

0.592

0.542

0.446

0.56

0.159

0.914

   

PE

0.428

0.395

0.27

0.459

0.084

0.228

0.918

  

SI

0.642

0.508

0.552

0.524

0.172

0.37

0.486

0.904

 

TB

0.645

0.548

0.481

0.546

0.253

0.575

0.431

0.592

0.863

APPENDIX D. Results of Heterotrait-Monotrait Ratio (HTMT)

(First- and Second-tier Cities)

 

BI

CB

CP

EE

FC

IR

PE

SI

TB

BI

         

CB

0.499

        

CP

0.577

0.592

       

EE

0.765

0.511

0.432

      

FC

0.251

0.344

0.375

0.21

     

IR

0.624

0.603

0.54

0.654

0.268

    

PE

0.525

0.438

0.284

0.542

0.157

0.227

   

SI

0.746

0.541

0.612

0.596

0.381

0.397

0.524

  

TB

0.768

0.634

0.553

0.656

0.424

0.677

0.472

0.683

 

(Third-tier Cities)

 

BI

CB

CP

EE

FC

IR

PE

SI

TB

BI

         

CB

0.523

        

CP

0.552

0.637

       

EE

0.726

0.474

0.403

      

FC

0.151

0.251

0.244

0.111

     

IR

0.659

0.604

0.492

0.629

0.2

    

PE

0.478

0.437

0.299

0.517

0.104

0.251

   

SI

0.716

0.567

0.618

0.595

0.21

0.401

0.544

  

TB

0.747

0.644

0.558

0.638

0.318

0.664

0.492

0.689

 

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Zhou, J., Si, L. (2024). Key Factors of Government Knowledge Base Adoption in First-, Second- and Third-Tier Cities in China. In: Sserwanga, I., et al. Wisdom, Well-Being, Win-Win. iConference 2024. Lecture Notes in Computer Science, vol 14598. Springer, Cham. https://doi.org/10.1007/978-3-031-57867-0_3

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