Influential Variables and Causal Relations Impact on Innovative Performance and Sustainable Growth of SMEs in Aspect of Industry 4.0 and Digital Transformation
Abstract
:1. Introduction
2. Theoretical Background and Hypothesis
2.1. I4.0 and DT
2.2. DT and SG
2.3. DT and TC
2.4. TC, IP, and SUSG
2.5. Conceptual Definition of Constructs
2.6. Research Questions and Differences
2.6.1. Research Questions
- Research proposition: DT affects Sustainable Growth
- Research question (RQ 1): Does DT affect innovative performance?
- Research question (RQ 2): Does innovative performance affect sustainable growth?
- Research question (RQ 3): Does DT affect technology competency?
- Research question (RQ 4): Does technology competency affect innovative performance?
- Research question (RQ 5): Does technology competency affect sustainable growth?
- Research Question (RQ 6): Will the variables that affect innovative performance and sustainable growth differ by industry?
2.6.2. Differences in Research
2.7. Research Hypotheses and Research Model
2.7.1. DT and IP
2.7.2. Innovative Performance and Sustainable Growth
2.7.3. DT and TC
2.7.4. TC, IP, SUSG
3. Materials and Methods
3.1. Conceptual Definition of Constructs
3.2. Independent Variables (DT): BD, AI, IoT SFR, CPS, IOP
3.2.1. Measurement of (BD)
3.2.2. Measurement of IoT
3.2.3. Measurement of SFR
3.2.4. Measurement of CPS
3.2.5. Measurement of IOP
3.3. Mediation Variables
3.3.1. TC
3.3.2. IP
3.4. Dependent Variable: SUSG
3.5. Data Collection and Sample Characteristics
4. Results
4.1. Verification of Measurement Model
- Internal consistency reliability was verified with three measurement items: Cronbach α (more than 0.7), Dijkstra-Henseler’s rho_A (ρA; more than 0.7), and composite reliability (CR; more than 0.7).
- Convergent Validity was verified with two measurement items: Outer Loading Relevance (0.7 or higher) and AVE (Average Variance Extracted; 0.5 or higher).
- Discriminant Validity was verified with two measurement items: Fornell-Larcker Criterion and Cross-Loadings.
4.2. Verification of Structural Model
- Core Model: Validation of hypotheses, variables, ad pathways affecting SUSG
- H1 (Accept): DT affects Innovative Performance (IP)
- H2 (Accept): IP affects Sustainable Growth (SUSG)
- H3 (Accept): DT affects Technology Competency (TC)
- H4 (Accept): TC affects Innovative Achievements (IP)
- H5 (Accept): TC affects Sustainable Growth (SUSG)
- Mediation Model: Specific indirect effects
- DT affects IP affects SUSG (Accept)
- DT affects TC affects SUSG (Accept)
- DT affects TC affects IP affects SUSG (Accept)
- TC affects IP affects SUSG (Accept)
- DT affects TC affect IP (Accept)
- Comprehensive Model: Total effects
- DT affects SUSG (Accept)
5. Discussion
- Differences in SUSG impact on SMEs by industry
- There was no difference in Division 1, Division 2, Division 3, Division 4, Division 5, and Division 7 in the total effect of DT affects SUSG.
- Division 4 > Division 1 > Division 2 > Division 7 > Division 5was the order of most significant total effect of DT affects TC, but it was relatively low in Division 1 and Division 3.
- In the total effect of TC affects SUSG, Division 1 showed a significant value (14.830) compared to Overall (10.302), and Division 3, Division 4, Division 5, and Division 7 showed small values.
- In the total effect of IP affects SUSG, only Division 2was significant, and the other Divisions were not significant.
- The order of the total effect of TC affects IP was Division 1 > Division 7 > Division 5 > Division 3, but Division 1 and Division 2 were not significant.
- Division 6 (crafts/others) had no variable relationship affecting SUSG. It is an analysis of the characteristics of an industry that relies on handicrafts, and it is a unique point.
5.1. Hypothesis Testing
- 1. H1 (Accept): DT affects IP
- 2. H2 (Accept): IP affects SUSG
- 3. H3 (Accept): DT affects TC
- 4. H4 (Accept): TC affects IP
- 5. H5 (Accept): TC affects SUSG
- DT affects IP, and IP is verified as a variable that affects SUSG.
- DT affects TC, and TC is verified as a variable that affects IP.
- TC is verified as a variable affecting SUSG.
- Mediation Model: Specific indirect effects
- 1. DT affects IP affects SUSG (Accept)
- 2. DT affects TC affects SUSG (Accept)
- 3. DT affects TC affects IP affects SUSG (Accept)
- 4. TC affects IP affects SUSG (Accept)
- 5. DT affects TC affects IP (Accept)
- DT affects SUG through the mediating effect of IP and TC.
- TC affects SUSG through the mediating effect of IP.
- DT affects IP through the mediating effect of TC.
- Comprehensive Model: Total effects
- DT affects SUSG (Accept)
5.2. Differences in Variables according to Industry Divisions
6. Conclusions
- Theoretical implications: Influential variables for the SUSG of SMEs vary by industry. DT affected SUSG in all seven industries. TC and IP mediated the SUSG effect of DT. Through empirical verification of the DT application of SMEs, it was confirmed that SUSG could be achieved by comprehensively introducing DT and strengthening technological capabilities.
- In conclusion, it was suggested that DT is essential for the SUSG of SMEs and that influencing variables suitable for the industry should be applied. The results of this study will be a new field of interest for future researchers.
- Industry and business implications:
- By presenting variables that must be considered by industry when promoting strategy revision and innovation for SUSG, practical SUSG influence variables that can overcome the limitations of existing studies are presented.
- (1)
- A strategy to improve TC by introducing DT and achieving SUSG.
- (2)
- A strategy to improve TC and pursue SUSG through IP.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Measurement Variable | Operational Definition | Previous Research |
---|---|---|
Big Data (BD) | Any technology used to process large amounts of data or information, including structured and unstructured data, including capture, security, transmission, storage, analysis, curation, search, privacy, and visualization | [1,92,94,95,96] |
Measurement Variable | Operational Definition | Previous Research |
---|---|---|
IoT | Technology that connects to the Internet by embedding sensors and communication functions in various objects. In other words, a technology that connects various things through wireless communication. Communication between people, machines and products, and the Internet of Things | [1,97,98,99] |
Measurement Variable | Operational Definition | Previous Research |
---|---|---|
Smart Factory | An intelligent production plant that can improve quality, productivity, and customer satisfaction by applying information and communication technology (ICT) combined with digital automation to the entire production process, including design and development, manufacturing, and distribution. In addition, a future-oriented factory can control itself by collecting and analyzing process data in real-time by applying the Internet of Things (IoT) to facilities and machines in the factory. | [1,100,101,102,103,104,105,106,107,108] |
Measurement Variable | Operational Definition | Previous Research |
---|---|---|
Cyber-Physical System (CPS) | An intelligent system that performs reliable and safe distributed control by integrating virtual systems and physical systems such as people, processes, and facilities into virtual systems and networks. | [100,107,112,113] |
Measurement Variable | Operational Definition | Previous Research |
---|---|---|
Inter-operability (IOP) | The property that one system can be used interchangeably with another system (same or heterogeneous) without restrictions. | [53,91,98,99,113] |
Measurement Variable | Operational Definition | Previous Research |
---|---|---|
Technology innovation competency | Management system and ability to efficiently carry out technological innovation | [92,115,116,117] |
Technology marketing competency | Marketing ability of products that developed by new technology | [118] |
Technology commercialization competency | Ability to commercialize development technology through production, commercialization, and marketing | [119] |
Measurement Variable | Operational Definition | Previous Research |
---|---|---|
Marketing Performance | (a) Sales growth (b) Customer growth (c) Sales volume for existing and newly developed products. | [120] |
Innovation Performance | Introduction of new technologies, frequency of product replacement and change, applicable technologies | [120] |
Networking | Information sharing, resource sharing, market, and technology sharing | [120] |
Human Capital | Knowledge, Competence, and Behavior | [120] |
Customer | Increased market share growth, cost reduction, improved customer satisfaction, increased responsiveness, increased quality assurance | [121] |
Process | Increased operational efficiency, increased need to understand internal and external processes | [124] |
Incremental Innovation Performance | Incremental innovation for products/services | [122] |
Radical Performance | A breakthrough innovation for products/services | [122] |
Measurement Variable | Operational Definition | Previous Research |
---|---|---|
Technical performance (TP) | Technology spillover effect and technological competitiveness, technological product, and process innovation | [127] |
Financial performance (FP) | Increase in the operating profit rate, market share, assets, sales | [128] |
Non-financial performance (NFP) | Qualitative indicators such as employee satisfaction, awareness, and service benefits | [129] |
Economic performance (ECP) | Productivity, turnover, profit, business growth, and cost reduction | [82,87,125,130] |
Environmental performance (ENP) | energy use, resource optimization, and waste reduction | [82,87,125,130,131,132] |
Social performance (SCP) | CSR project investment, employee welfare initiative, accident reduction | [82,87,125,126,130,131,132,133] |
n = 303 | Frequency | Percent | |
---|---|---|---|
1. Business type | Private business | 30 | 9.9 |
Corporate business | 273 | 90.1 | |
2. Industry sector | IT/SW | 61 | 20.1 |
Craft/others | 21 | 6.9 | |
Machinery/parts | 38 | 12.5 | |
Bioindustry/foods | 35 | 11.6 | |
Pharmaceutical/Bio-health | 46 | 15.2 | |
Electrics/electronics | 53 | 17.5 | |
Chemicals/fibers/materials | 49 | 16.2 | |
3. Years in operation | Under 1 year | 2 | 0.7 |
1–2 years | 15 | 5.0 | |
2–3 year | 53 | 17.5 | |
3–5 years | 158 | 52.1 | |
More than 5 years | 75 | 24.8 | |
4. Sales Volume (USD) | Less than $0.1 million | 133 | 43.9 |
$0.1–0.3 million | 7 | 2.3 | |
$0.3–0.5 million | 15 | 5.0 | |
$0.5–1 million | 20 | 6.6 | |
More than $1 million | 45 | 14.9 | |
5. Manufacturing | Outsourcing | 278 | 91.7 |
Outsourcing and in-house | 15 | 5.0 | |
In-house | 10 | 3.3 | |
6. Employees | 10–202 people | 131 | 43.2 |
More than 20 people | 24 | 7.9 | |
3–55 people | 26 | 8.6 | |
Fewer than three people | 16 | 5.3 | |
5–10 people | 106 | 35.0 | |
7. Gender | Male | 249 | 82.2 |
Female | 54 | 17.8 | |
8. Age | 20 s | 3 | 1.0 |
30 s | 92 | 30.4 | |
40 s | 167 | 55.1 | |
50 or over | 41 | 13.5 |
Internal Consistency Reliability | Convergent Validity | Discriminant Validity | |||
---|---|---|---|---|---|
Variables | Cronbach’s α > 0.7 | Composite Reliability (rho_A) > 0.7 | Composite Reliability (C.R) > 0.7 | Average Variance Extracted (AVE) > 0.5 | Fornell–Larcker |
BD | 0.881 | 0.891 | 0.918 | 0.737 | Yes |
CPS | 0.768 | 0.807 | 0.863 | 0.678 | Yes |
IoT | 0.811 | 0.820 | 0.875 | 0.638 | Yes |
SFR | 0.868 | 0.877 | 0.910 | 0.717 | Yes |
DT | 0.891 | 0.891 | 0.913 | 0.567 | Yes |
TIC | 0.863 | 0.870 | 0.898 | 0.594 | Yes |
TMC | 0.846 | 0.847 | 0.897 | 0.686 | Yes |
TC | 0.881 | 0.884 | 0.908 | 0.587 | Yes |
IPC | 0.877 | 0.879 | 0.924 | 0.802 | Yes |
IPra | 0.800 | 0.800 | 0.882 | 0.714 | Yes |
IP | 0.839 | 0.841 | 0.879 | 0.511 | Yes |
ENP | 0.795 | 0.796 | 0.907 | 0.830 | Yes |
NFP | 0.813 | 0.813 | 0.915 | 0.843 | Yes |
TEP | 0.831 | 0.834 | 0.922 | 0.855 | Yes |
SUSG | 0.845 | 0.849 | 0.886 | 0.565 | Yes |
Variables | BD | CPS | DT | ENP | IP | IPC | IPH | IPra | IoT | NFP | SFR | SUSG | TC | TEP | TIC | TMC |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
BD | 0.858 | |||||||||||||||
CPS | 0.557 | 0.824 | ||||||||||||||
DT | 0.769 | 0.700 | 0.753 | |||||||||||||
ENP | 0.377 | 0.397 | 0.473 | 0.911 | ||||||||||||
IP | 0.454 | 0.422 | 0.586 | 0.462 | 0.715 | |||||||||||
IPC | 0.347 | 0.344 | 0.483 | 0.355 | 0.856 | 0.896 | ||||||||||
IPH | 0.216 | 0.223 | 0.326 | 0.281 | 0.637 | 0.447 | 1.000 | |||||||||
IPra | 0.443 | 0.381 | 0.515 | 0.426 | 0.812 | 0.434 | 0.393 | 0.845 | ||||||||
IoT | 0.613 | 0.600 | 0.884 | 0.396 | 0.520 | 0.427 | 0.287 | 0.459 | 0.799 | |||||||
NFP | 0.477 | 0.458 | 0.649 | 0.507 | 0.555 | 0.446 | 0.319 | 0.496 | 0.625 | 0.918 | ||||||
SFR | 0.486 | 0.531 | 0.875 | 0.420 | 0.531 | 0.438 | 0.308 | 0.462 | 0.758 | 0.599 | 0.847 | |||||
SUSG | 0.493 | 0.517 | 0.667 | 0.748 | 0.589 | 0.464 | 0.342 | 0.536 | 0.629 | 0.880 | 0.606 | 0.752 | ||||
TC | 0.515 | 0.514 | 0.686 | 0.507 | 0.544 | 0.432 | 0.329 | 0.486 | 0.656 | 0.576 | 0.630 | 0.659 | 0.766 | |||
TEP | 0.352 | 0.413 | 0.505 | 0.381 | 0.426 | 0.333 | 0.241 | 0.393 | 0.508 | 0.619 | 0.457 | 0.822 | 0.533 | 0.925 | ||
TIC | 0.312 | 0.425 | 0.516 | 0.366 | 0.429 | 0.341 | 0.267 | 0.380 | 0.483 | 0.541 | 0.510 | 0.588 | 0.700 | 0.524 | 0.771 | |
TMC | 0.522 | 0.486 | 0.656 | 0.485 | 0.537 | 0.418 | 0.321 | 0.491 | 0.635 | 0.557 | 0.603 | 0.630 | 0.958 | 0.503 | 0.580 | 0.828 |
BD | CPS | IP | IPC | IPH | IPra | IoT | SFR | SUSG | TC | TIC | TMC | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
DT | 1.000 | 1.000 | 1.887 | 1.000 | 1.000 | 1.000 | ||||||
IP | 1.000 | 1.000 | 1.000 | 1.420 | ||||||||
TC | 1.887 | 1.420 | 1.000 | 1.000 |
Hypothesis | Path | Path Coefficient | Verification | ||||||
---|---|---|---|---|---|---|---|---|---|
T-Statistics | p Values | ||||||||
Core Model | H1 | DT → IP | 4.955 | 0.000 | Accept | ||||
H2 | IP → SUSG | 5.626 | 0.000 | Accept | |||||
H3 | DT → TC | 16.506 | 0.000 | Accept | |||||
H4 | TC → IP | 3.010 | 0.003 | Accept | |||||
H5 | TC → SUSG | 9.421 | 0.000 | Accept | |||||
Mediation Model | Specific indirect effects | DT → IP → SUSG | 3.387 | 0.001 | Accept | ||||
DT → TC → SUSG | 7.459 | 0.000 | Accept | ||||||
DT → TC → IP → SUSG | 2.729 | 0.006 | Accept | ||||||
TC → IP → SUSG | 2.779 | 0.005 | Accept | ||||||
DT → TC → IP | 2.929 | 0.003 | Accept | ||||||
Comprehensive Model | Total effects | DT → SUSG | 12.313 | 0.000 | Accept | ||||
Model fit: SRMR (standard root mean square residual) for the entire model | 0.0081 (Saturated model) | ||||||||
Construct cross-validated redundancy | |||||||||
BD | DT | IP | SUSG | TC | |||||
SSO | 1212.000 | 2424.000 | 2121.000 | 909.000 | 4242.000 | ||||
SSE | 692.770 | 2021.000 | 1710.668 | 623.129 | 3310.044 | ||||
Q2 (=1-SSE/SSO) | 0.428 | 0.000 | 0.193 | 0.314 | 0.220 |
Total Effects (Overall) | Total Effects (Division 1) | Total Effects (Division 2) | Total Effects (Division 3) | Total Effects (Division 4) | Total Effects (Division 5) | Total Effects (Division 6) | Total Effects (Division 7) | |
---|---|---|---|---|---|---|---|---|
T Statistics/p-Value | ||||||||
(H1) DT → IP | 13.848/0.000 | 4.807/0.000 | 8.354/0.000 | 5.922/0.000 | 5.584/0.000 | 5.980/ 0.000 | 1.467/0.143 | 5.278/0.112 |
(H2) IP → SUSG | 3.498/0.000 | 0.944/0.345 | 2.707/0.007 | 1.497/0.134 | 0.397/0.691 | 1.599/ 0.000 | 0.876/0.381 | 1.589/0.000 |
(H3) DT → TC | 19.023/0.000 | 6.186/0.000 | 12.311/0.000 | 5.132/0.000 | 17.944/0.000 | 12.191/ 0.000 | 1.675/0.094 | 12.251/0.000 |
(H4) TC → IP | 3.438/0.000 | 5.756/0.000 | 0.520/0.012 | 2.504/0.012 | 1.091/0.275 | 3.309/0.110 | 0.843/0.400 | 4.270/0.000 |
(H5) TC → SUSG | 10.302/0.000 | 14.830/0.000 | 2.058/0.000 | 3.515/0.000 | 6.822/0.000 | 4.111/0.000 | 1.405/0.160 | 5.596/0.000 |
(Comprehensive) DT → SUSG | 12.878/0.000 | 5.512/0.000 | 6.326/0.000 | 4.027/0.000 | 6.607/0.000 | 7.537/ 0.000 | 1.701/0.089 | 5.514/0.000 |
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Kim, S.; Ha, T. Influential Variables and Causal Relations Impact on Innovative Performance and Sustainable Growth of SMEs in Aspect of Industry 4.0 and Digital Transformation. Sustainability 2023, 15, 7310. https://doi.org/10.3390/su15097310
Kim S, Ha T. Influential Variables and Causal Relations Impact on Innovative Performance and Sustainable Growth of SMEs in Aspect of Industry 4.0 and Digital Transformation. Sustainability. 2023; 15(9):7310. https://doi.org/10.3390/su15097310
Chicago/Turabian StyleKim, Seoksoo, and Taekwan Ha. 2023. "Influential Variables and Causal Relations Impact on Innovative Performance and Sustainable Growth of SMEs in Aspect of Industry 4.0 and Digital Transformation" Sustainability 15, no. 9: 7310. https://doi.org/10.3390/su15097310
APA StyleKim, S., & Ha, T. (2023). Influential Variables and Causal Relations Impact on Innovative Performance and Sustainable Growth of SMEs in Aspect of Industry 4.0 and Digital Transformation. Sustainability, 15(9), 7310. https://doi.org/10.3390/su15097310