Home International Journal of Crowd Science Article
PDF (1.7 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript
Show Outline
Figures (3)

Tables (8)
Table 1
Table 2
Table 3
Table 4
Table 5
Show 3 more tables Hide 3 tables
Open Access

Enhancing Organizational Performance: Synergy of Cyber-Physical Systems, Cloud Services, and Crowdsensing

Ahmad Al-Qerem1, Ali Mohd Ali2, Ahmad Nabot1, Issam Jebreen1, Mohammad Alauthman3, Someah Alangari4, Faisal Aburub5, Amjad Aldweesh6()
Computer Science Department, Faculty of Information Technology, Zarqa University, Zarqa 13110, Jordan
Communications and Computer Engineering Department, Faculty of Engineering, Al-Ahliyya Amman University, Amman 19328, Jordan
Department of Information Security, University of Petra, Amman 11196, Jordan
Department of Computer Science, College of Science and Humanities Dawadmi, Shaqra University, Shaqra 11911, Saudi Arabia
Department of Business Intelligence and Data Analytics, University of Petra, Amman 11196, Jordan
College of Computing and Information Technology, Shaqra University, Shaqra 11911, Saudi Arabia
Show Author Information

Abstract

In the contemporary business landscape, software has evolved into a strategic asset crucial for organizations seeking sustainable competitive advantage. The imperative of ensuring software quality becomes evident as low-quality software systems pose formidable challenges to organizational performance. This study delves into the profound impact of three key dimensions of information system quality on organizational performance—information quality (IQ), quality of service (QoS), and software quality (SQ). Anchored in the DeLone and McLean information system (IS) success model, a quantitative questionnaire was administered to 360 industry experts and academics. Rigorous data analysis, employing exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM), revealed significant positive effects of all three quality dimensions on organizational performance. Among these dimensions, software quality emerged as the most influential, showcasing substantial total effects, closely followed by information and service qualities. The study underscores the tangible value derived from strategic investments in enhancing software, information, and service quality. Elevating these facets manifests as a catalyst for improved organizational performance, empowering decision-makers with accurate and timely information while enhancing user satisfaction with the system. This research contributes significantly to the IS success literature by empirically validating the synergistic relationship between information quality, service quality, software quality, and organizational outcomes. The systematic analysis offered in this study goes beyond theoretical validation, providing actionable insights for managers. The findings guide the prioritization of quality initiatives and resource allocation, enabling organizations to maximize competitive advantage. As a future research direction, investigating moderator influences and exploring alternate quality constructs relevant to contemporary technologies, including cyber-physical systems, cloud services, and crowdsensing, holds promise for further enriching our understanding of the evolving digital landscape.

Keywords

software qualityinformation qualityquality of serviceorganizational performancecyber-physical systemscloud service crowdsensing

References

[1]
R. V. Ferreira and A. P. M. S. Cherobim, Impacto dos investimentos em TI no desempenho organizacional de empresas de panificação de minas gerais: Estudo multicaso, BASE Rev. De Adm. E Contabilidade Da Unisinos, vol. 9, no. 2, pp. 147–161, 2012.
Crossref
[2]

M. M. Moughari and T. U. Daim, Developing a model of technological innovation for export development in developing countries, Technol. Soc., vol. 75, p. 102338, 2023.

Crossref Google Scholar
[3]
J. A. O’brien and G. Marakas, Management Information Systems. New York, NY, USA: McGraw-Hill, 2011.
[4]
E. Turban, L. Volonino, G. R. Wood, and J. C. Sipior, Information Technology for Management: Advancing Sustainable, Profitable Business Growth. Hoboken, NJ, USA: Wiley, 2013.
[5]

J. Issam and A. Q. Ahmad, Empirical study of analysts’ practices in packaged software implementation at small software enterprises, Int. Arab J. Inf. Technol., vol. 14, no. 4, pp. 543–551, 2017.

Google Scholar
[6]

N. Gorla, T. M. Somers, and B. Wong, Organizational impact of system quality, information quality, and service quality, J. Strateg. Inf. Syst., vol. 19, no. 3, pp. 207–228, 2010.

Crossref Google Scholar
[7]

M. Al-Okaily, R. Alghazzawi, A. F. Alkhwaldi, and A. Al-Okaily, The effect of digital accounting systems on the decision-making quality in the banking industry sector: A mediated-moderated model, Glob. Knowl. Mem. Commun., vol. 72, nos. 8&9, pp. 882–901, 2023.

Crossref Google Scholar
[8]

Q. Fu, A. A. Abdul Rahman, H. Jiang, J. Abbas, and U. Comite, Sustainable supply chain and business performance: The impact of strategy, network design, information systems, and organizational structure, Sustainability, vol. 14, no. 3, p. 1080, 2022.

Crossref Google Scholar
[9]

M. Alauthman, A. Al-Qerem, S. Alangari, A. M. Ali, A. Nabo, A. Aldweesh, I. Jebreen, A. Almomani, and B. B. Gupta, Machine learning for accurate software development cost estimation in economically and technically limited environments, Int. J. Softw. Sci. Comput. Intell., vol. 15, no. 1, pp. 1–24, 2023.

Crossref Google Scholar
[10]
W. H. DeLone and E. R. McLean, Information systems success: The quest for the dependent variable, Inf. Syst. Res., vol. 3, no. 1, pp. 60–95, 1992.
Crossref
[11]

Y. H. Al-Mamary, A. Shamsuddin, and N. Aziati, The pilot test study of relationship between management information system success factors and organization performance at sabafon company in Yemen, Int. J. U E Serv. Sci. Technol., vol. 8, no. 2, pp. 337–346, 2015.

Crossref Google Scholar
[12]

S. I. Ali Al Randi, R. C. Jalagat, and A. Bashayreh, Influence of social media usage and work performance on organizational performance: The case of A broadband company in Oman, Turk Turizm Arastirmalari Dergisi, vol. 4, no. 4, pp. 419–435, 2022.

Crossref Google Scholar
[13]

J. Zhang, Y. Shu, and H. Yu, Fairness in design: A framework for facilitating ethical artificial intelligence designs, International Journal of Crowd Science, vol. 7, no. 1, pp. 32–39, 2023.

Crossref Google Scholar
[14]

L. Dagiliene and K. Šutiene, Corporate sustainability accounting information systems: A contingency-based approach, Sustain. Account. Manag. Policy J., vol. 10, no. 2, pp. 260–289, 2019.

Crossref Google Scholar
[15]

P. Q. Huy and V. K. Phuc, Does strategic corporate social responsibility drive better organizational performance through integration with a public sector scorecard? Empirical evidence in a developing country, Processes, vol. 8, no. 5, p. 596, 2020.

Crossref Google Scholar
[16]

L. Burkon, Quality of service attributes for software as a service, J. Syst. Integr., vol. 4, no. 3, pp. 38–47, 2013.

Google Scholar
[17]

J. Agarwal, M. Kumar, M. Sharma, D. Verma, and R. Sharma, Application of intelligent adaptive neuro fuzzy method for reusability of component based software system, Int. Arab J. Inf. Technol., vol. 20, no. 5, pp. 773–781, 2023.

Crossref Google Scholar
[18]

V. Yadav, R. Singh, and V. Yadav, Estimation model for enhanced predictive object point metric in OO software size estimation using deep learning, Int. Arab J. Inf. Technol., vol. 20, no. 3, pp. 293–302, 2023.

Crossref Google Scholar
[19]
A. C. Gillies, Software Quality: Theory and Management. London, UK: Chapman & Hall, Ltd., 2011.
[20]
K. Naik and P. Tripathy, Software Testing and Quality Assurance: Theory and Practice. New York, NY, USA: John Wiley & Sons, 2011.
[21]

T. Ravichandran, and C. Lertwongsatien, Effect of information systems resources and capabilities on firm performance: A resource-based perspective, J. Manag. Inf. Syst., vol. 21, no. 4, pp. 237–276, 2005.

Crossref Google Scholar
[22]

G. Ray, L. Xue, and J. B. Barney, Impact of information technology capital on firm scope and performance: The role of asset characteristics, Acad. Manag. J., vol. 56, no. 4, pp. 1125–1147, 2013.

Crossref Google Scholar
[23]

R. H. Ananda and E. S. Astuti, Analysis of the relationship of information technology capability to innovation and organizational performance (meta-analysis), Profit: Jurnal Administrasi Bisnis, vol. 17, no. 1, pp. 55–68, 2023.

Google Scholar
[24]

S. Chatterjee, R. Chaudhuri, D. Vrontis, and G. Giovando, Digital workplace and organization performance: Moderating role of digital leadership capability, J. Innov. Knowl., vol. 8, no. 1, p. 100334, 2023.

Crossref Google Scholar
[25]

S. N. Ramaswami, R. K. Srivastava, and M. Bhargava, Market-based capabilities and financial performance of firms: Insights into marketing’s contribution to firm value, J. Acad. Mark. Sci., vol. 37, no. 2, pp. 97–116, 2008.

Crossref Google Scholar
[26]

M. S. Kim and B. Thapa, Relationship of ethical leadership, corporate social responsibility and organizational performance, Sustainability, vol. 10, no. 2, p. 447, 2018.

Crossref Google Scholar
[27]

A. Nabot, V. Garaj, and W. Balachandran, Consumer attitudes toward online shopping, Int. J. Soc. Ecol. Sustain. Dev., vol. 5, no. 3, pp. 13–24, 2014.

Crossref Google Scholar
[28]
Z. Awang, SEM Made Simple: A Gentle Approach to Learning Structural Equation Modeling. Bangi, Malaysia: MPWS Rich Publication, 2015.
[29]
J. W. Creswell, Qualitative, Quantitative, and Mixed Methods Approaches. Thousand Oaks, CA, USA: Sage, 2003.
[30]

A. R. Hashmi, N. A. Amirah, and Y. Yusof, Organizational performance with disruptive factors and inventory control as a mediator in public healthcare of Punjab, Pakistan, Management Science Letters, vol. 11, no. 1, pp. 77–86, 2021.

Crossref Google Scholar
[31]

M. M. Kokar, K. Baclawski, and Y. A. Eracar, Control theory-based foundations of self-controlling software, IEEE Intell. Syst. Appl., vol. 14, no. 3, pp. 37–45, 1999.

Crossref Google Scholar
[32]

M. Nakhaeinejad, H. Khademi Zare, M. Habibi, and M. R. Khodoomi, Improvement of multi-item order systems and inventory management models using optimal control theory, Trans. Inst. Meas. Contr., vol. 45, no. 1, pp. 104–119, 2023.

Crossref Google Scholar
[33]

J. Barney, Firm resources and sustained competitive advantage, J. Manag., vol. 17, no. 1, pp. 99–120, 1991.

Crossref Google Scholar
[34]

B. Wernerfelt, A resource-based view of the firm, Strateg. Manag. J., vol. 5, no. 2, pp. 171–180, 1984.

Crossref Google Scholar
[35]

J. Liu, B. Liang, and W. Ji, An anomaly detection approach based on hybrid differential evolution and K-means clustering in crowd intelligence, International Journal of Crowd Science, vol. 5, no. 2, pp. 129–142, 2021.

Crossref Google Scholar
[36]

R. J. Arend, and M. Lévesque, Is the resource-based view a practical organizational theory? Organ. Sci., vol. 21, no. 4, pp. 913–930, 2010.

Crossref Google Scholar
[37]

T. Connor, The resource-based view of strategy and its value to practising managers, Strateg. Change, vol. 11, no. 6, pp. 307–316, 2002.

Crossref Google Scholar
[38]

S. W. O’Leary-Kelly and R. J. Vokurka, The empirical assessment of construct validity, J. Oper. Manag., vol. 16, no. 4, pp. 387–405, 1998.

Crossref Google Scholar
[39]

R. B. Kline, Response to Leslie Hayduk’s review of principles and practice of structural equation modeling, 4th edition, Can. Stud. Popul., vol. 45, nos. 3&4, pp. 188, 2018.

Crossref Google Scholar
[40]
W. Black and B. J. Babin, Multivariate data analysis: Its approach, evolution, and impact, in The Great Facilitator, B. J. Babin and M. Sarstedt, eds. Cham, Switzerland: Springer, 2019, pp. 121–130.
Crossref
[41]
J. Cohen, Statistical Power Analysis for the Behavioral Sciences. New York, NY, USA: Routledge, 2013.
Crossref
[42]

X. Li, T. Zhang, X. Sun, and Y. Ma, Product map analysis from a crowd of small- and medium-sized e-commerce sites: A bottom-up approach, International Journal of Crowd Science, vol. 7, no. 3, pp. 131–136, 2023.

Crossref Google Scholar
[43]

G. Gable, D. Sedera, and T. Chan, Re-conceptualizing information system success: The IS-impact measurement model, J. Assoc. Inf. Syst., vol. 9, no. 7, pp. 377–408, 2008.

Crossref Google Scholar
[44]

H. H. Weng, J. S. Chen, and P. C. Chen, Effects of green innovation on environmental and corporate performance: A stakeholder perspective, Sustainability, vol. 7, no. 5, pp. 4997–5026, 2015.

Crossref Google Scholar
[45]

Y. Choi and Y. Yu, The influence of perceived corporate sustainability practices on employees and organizational performance, Sustainability, vol. 6, no. 1, pp. 348–364, 2014.

Crossref Google Scholar
[46]
I. Jebreen, R. Wellington, and S. G. MacDonell, Packaged software implementation requirements engineering by small software enterprises, in Proc. 20th Asia-Pacific Software Engineering Conf. (APSEC), Bangkok, Thailand, 2013, pp. 50–57.
Crossref
International Journal of Crowd Science
Volume 9 Issue 1,
March 2025
Pages 44-55
DOI: 10.26599/IJCS.2023.9100033
Cite this article:
Al-Qerem A, Ali AM, Nabot A, et al. Enhancing Organizational Performance: Synergy of Cyber-Physical Systems, Cloud Services, and Crowdsensing. International Journal of Crowd Science, 2025, 9(1): 44-55. https://doi.org/10.26599/IJCS.2023.9100033
Metrics & Citations  
Article History
Copyright
Rights and Permissions
Return

Demographic data for respondents.

Demographic profileCategoryFrequency (n=360)Percentage (%)
GenderMale27676.6
Female8423.4
Age18−306518.1
31−4012735.3
41−507922.0
Over 508924.6
PositionEmployee3610.1
Manager18952.5
Consultant8724.1
Academic4813.3

Descriptive analysis results.

VariableItemM for itemsM for variableSD for variable
IQIQ1: The provided information is complete, valuable, and understandable.4.414.390.65
IQ2: The provided information is timely and meets your needs.4.36
IQ3: The provided information is up-to-date and accurate.4.39
IQ4: The provided information helps firms’ managers make the right decisions.4.40
QoSQoS1: The provided services are affected by the collected data.4.224.241.07
QoS2: The response time of the provided services is reduced.4.36
QoS3: The provided services to the customers and employees are of high quality.4.19
QoS4: The provided services are up-to-date.4.21
QoS5: The cost of the provided services is reduced.4.22
SQSQ1: The used information system meets firm needs, which helped in improving the quality of the provided services.4.434.280.92
SQ2: The information system is easy to use, flexible, responsive, and efficient to enable firms to achieve a competitive advantage.4.19
SQ3: The information system helps the firm achieve its goal by providing up-to-date information.4.21
SQ4: The used information system helps firm managers in enhancing organizational planning.4.29
OPOP1: Does inventory management improve customer satisfaction, which increases organizational performance?4.474.491.11
OP2: Respect employees’ qualities and contributions increased company success.4.49
OP3: Social responsibility of business satisfied customers’ and employee’s expectations which increased organizational performance.4.48
OP4: My firm uses a reward system for innovative employees, which increases organizational performance.4.51
OP5: My firm offers managerial and technical programs for employees.4.50

Rotated factor matrix results.

ItemResult of the rotated factor matrix
IQQoSSQOP
IQ30.860−−−
IQ20.852−−−
IQ10.774−−−
IQ40.756−−−
IQ50.451<0.60−−−
QoS1−0.948−−
QoS5−0.934−−
QoS4−0.932−−
QoS2−0.794−−
QoS3−0.784−−
SQ1−−0.911−
SQ2−−0.907−
SQ4−−0.828−
SQ3−−0.808−
OP1−−−0.880
OP4−−−0.859
OP3−−−0.825
OP2−−−0.788
OP5−−−0.786

Goodness of fit indices (GoF).

VariableNumber of itemsParsimonious fit(χ2/df)Incremental fitAbsolute fit (RMSEA)Cronbach’s alpha (α)
NFITLICFIAGFI
SQ41.3210.9200.9330.9610.8920.0310.823
QoS50.941
OP50.751
IQ40.733

Cronbach’s alpha (α), standardized loadings (Std β), CR, and average variance extracted (AVE).

VariableItemCronbach’s alpha (α > 0.7)Std β (> 0.7)CR (> 0.7)AVE (> 0.5)
Note: CR: critical ratios; AVE for the second-order model = averaging the squared multiple correlations for the first-order indicators.
IQIQ10.7330.8430.8760.781
IQ20.833
IQ30.783
IQ40.814
QoSQoS10.9410.7970.9210.852
QoS20.864
QoS30.874
QoS40.877
QoS50.834
SQSQ10.8230.8630.9180.841
SQ20.842
SQ30.833
SQ40.853
OPOP10.7510.8210.8240.710
OP20.791
OP30.865
OP40.876
OP50.812

Discriminant validity results.

VariableAVEIQQoSSQOP
IQ0.7810.609−−−
QoS0.8520.6610.725−−
SQ0.7100.7730.7540.501−
OP0.8410.7120.7190.8100.707

Direct hypotheses path.

HypothesisPathStd βSECRResult
Nots: SE: standard error; ***: p < 0.001.
H1QoS→OP0.351***0.1123.452Supported
H4IQ→OP0.456***0.1233.921Supported
H5SQ→OP0.532***0.1204.342Supported

Indirect hypotheses path.

HypothesisRelationshipStd βSECRResult
Note: ***: p < 0.001.
H2QoS→IQ0.310***0.1433.043Supported
H3SQ→ IQ0.342***0.1613.530Supported
H6SQ→QoS0.496***0.1343.325Supported