| Literature DB >> 31635373 |
Chun-Hsun Chen1, Yu-Li Lan2, Wei-Pang Yang3, Fang-Ming Hsu4, Chin-Lon Lin5, Hsing-Chu Chen6.
Abstract
This study explored the effects of information technology (IT) resources-in conjunction with IT infrastructure and organizational resources-on organizational capabilities and performance. The study further analyzed the mediating effect of organizational capabilities on the relationship between IT resources and organizational performance. A cross-sectional research design was adopted, and questionnaire copies were administered to senior care supervisors of Taiwanese day care centers, care institutions, and hospitals. In total, 328 valid questionnaire responses were obtained. The study results are summarized as follows: (1) A direct effect analysis revealed that IT infrastructure significantly affected service performance and financial performance; organizational resources significantly affected service performance but did not significantly affect financial performance. (2) A mediation model analysis indicated that organizational capabilities exerted a mediating effect on the relationship between IT resources and organizational performance. These results can serve as a reference for medical care organizations in developing strategies for reviewing internal IT resources, integrating internal and external capabilities, creating a competitive advantage, and boosting their performance.Entities:
Keywords: organizational capabilities; organizational performance; resource-based; telehealth care system
Mesh:
Year: 2019 PMID: 31635373 PMCID: PMC6844123 DOI: 10.3390/ijerph16203988
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 3.390
Figure 1Conceptual framework. THC: telehealth care.
Direct effect analysis results.
| Mode | Mode (Direct Mode) | ||||
|---|---|---|---|---|---|
| Standard path coefficient/ | Standard path coefficient (normalized coefficient and | Significance ( | Outcome of Practice | ||
| Path/adaptation statistic | |||||
| Hypothesis | Hypothesis | Hypothesis | |||
| H1a | IF → SP |
| 0.23(4.04) | 0.000 | support |
| H1b | IF → FP |
| 0.12(2.37) | 0.019 | support |
| H1c | OR → SP |
| 0.46(7.79) | 0.000 | support |
| H1c | OR → FP |
| −0.08(−1.38) | 0.166 | Not support |
| Mode adaptation statistic | |||||
| Chi-square | 49.200 | ||||
| df | 37 | ||||
| GFI | 0.975 | ||||
| AGFI | 0.956 | ||||
| RMR | 0.010 | ||||
| REMSEA | 0.032 | ||||
Abbreviations: IF: IT infrastructure; OR: organizational resources; SP: service performance; FP: financial performance; df: degree of freedom; GFI: goodness-of-fit index; AGFI: adjusted GFI; RMR: root mean square residual; REMSEA: root mean square error of approximation.
Figure 2Direct effect analysis; *** p < 0.001; * p < 0.01.
Figure 3Mediation effect analysis; *** p < 0.0001; ** p < 0.001
Mediation effect analysis results.
| Mode | Mode 2 (Intermediary Mode) | ||||
|---|---|---|---|---|---|
| Standard path coefficient/ | Standard path coefficient (normalized coefficient and | Significance ( | Outcome of Practice | ||
| Path/adaptation statistic | |||||
| Hypothesis | Hypothesis path | Hypothetical relationship | |||
| H2a | IF → IC |
| 0.23(4.55) | 0.000 | support |
| H2b | IF → EC |
| 0.62(11.19) | 0.000 | support |
| H2c | OR → IC |
| 0.16(3.46) | 0.000 | support |
| H2d | OR → EC |
| 0.32(5.23) | 0.000 | support |
| H3a | IC → SP |
| 0.45(4.78) | 0.000 | support |
| H3b | IC → FP |
| 0.27(2.59) | 0.009 | support |
| H3c | EC → SP |
| 0.32(3.40) | 0.000 | support |
| H3d | EC → FP |
| 0.30(3.02) | 0.002 | support |
| Mode adaptation statistic | |||||
| Chi-square | 93.940 | ||||
| d.f | 86 | ||||
| GFI | 0.966 | ||||
| AGFI | 0.946 | ||||
| RMR | 0.015 | ||||
| REMSEA | 0.017 | ||||
Abbreviations: IF: IT infrastructure; OR: organizational resources; IC: internal capabilities; EC: external capabilities; SP: service performance; FP: financial Performance.