One of the most salient problems currently confronting Software as a Service (SaaS) realization and scientific research is understanding how to efficiently connect software platforms using SaaS distribution models integrating the business functions. The extant empirical SaaS literature holds research gaps regarding the commercial and technical aspects of SaaS business models. To address these gaps, this article systematically reviews current empirical studies to understand how SaaS integration projects could be more efficiently managed in organizations by integrating technical and business aspects. Among other findings, we discover significant knowledge gaps in the field, mainly regarding the empirical validation of a coherent business strategy regarding the SaaS model and an integrated view of customer-perceived performance concerning SaaS architecture. Based on the analysis and synthesis of the findings, this study provides a framework for SaaS realization, including the principal dimensions to be considered in SaaS provision. Futhermore, a novel conceptual model proposed complements the framework by organizing 14 principal variables affecting SaaS integration on three decision-making levels, i.e., strategic, tactical, and operational. This study theoretically contributes to solidifying the SaaS integration literature by covering the three levels of decision-making and offering actionable insights into business practice and SaaS implementation. Regarding the practical contributions, this research guides managers in managing SaaS projects by emphasizing a holistic approach that incorporates all decision-making levels and perspectives, as well as interconnected technological and business aspects. Lastly, promising future research opportunities have been identified to move SaaS research forward.
AleemSAhmedFBatoolR, et al. (2019) Empirical investigation of key factors for SAAS architecture. IEEE Transactions on Cloud Computing9(3): 1037–1049. doi:https://doi.org/10.1109/TCC.2019.2906299.
2.
AlotaibiR (2016) Antecedents of software-as-a-service (SaaS) adoption: A structural equation model. International Journal of Advanced Computer Research6(25): 114–129.
3.
BajigharMShahzadM (2017) Autonomous software development: Sustain market share in constantly changing software product industries. In: Proceedings of the 21st world multi-conference on systemics, cybernetics and informatics (WMSCI 2017), Vol. 1, 243–248.
4.
BasiranNHYusofMM (2023) Measuring factors influencing quality of software-as-a-service. Information Development39(4): 835–852.
5.
BiolchiniJDAMianPGNataliACC, et al. (2007) Scientific research ontology to support systematic review in software engineering. Advanced Engineering Informatics21(2): 133–151.
6.
BlueSADevineJARuizMP, et al. (2021) Im/mobility at the US-Mexico border during the COVID-19 pandemic. Social Sciences10(2): 47. doi:https://doi.org/10.3390/socsci10020047.
7.
BrownA (2011) A case study in Agile-at-scale delivery. Lecture Notes in Business Information Processing77: 266–281.
8.
CavillierCWieserJ (2018) Connecting academia and small enterprises: A new field for knowledge management experiments. In: 15th International conference on intellectual capital, knowledge management and organizational learning (ICICKM 2018), pp.30–39.
9.
DasRDemirolDTunaG (2016) A novel tool for mining access patterns efficiently from web user access logs. In: 2nd ICENS international conference on engineering and natural science, 24–28 May 2016, Sarajevo, Bosnia and Herzegovina. ISBN: 978-605-83575-0-1.
10.
DemirelSTDasR (2018) Software requirement analysis: Research challenges and technical approaches. In: 6th International symposium on digital forensic and security (ISDFS), Antalya, Turkey, 2018, pp.1–6. doi:https://doi.org/10.1109/isdfs.2018.8355322.
11.
EthelbertOMoghaddamFFWiederP, et al. (2017) A JSON token-based authentication and access management schema for cloud SaaS applications. In: 2017 IEEE 5th international conference on future internet of things and cloud (FiCloud), Prague, Czech Republic, 2017, pp.47–53. doi:https://doi.org/10.1109/ficloud.2017.29.
12.
HamidNBAMohamedIDaudM, et al. (2019) OTPAF: A security requirement conceptual model of SaaS for Malaysian government based on common criteria. In: 2019 International conference on electrical engineering and informatics (ICEEI), Bandung, Indonesia, 2019, pp.69–74. doi:https://doi.org/10.1109/iceei47359.2019.8988832.
13.
HintschJKramerFMullerH, et al. (2016) Software as a service production: An operationalizable domain model. In: CBI 2016: 18th IEEE conference on business informatics, pp.14–19. doi:https://doi.org/10.1109/cbi.2016.44.
14.
JalaliSWohlinC (2012) Systematic literature studies. In: Empirical software engineering and measurement (ESEM ‘12): Proceedings of the ACM-IEEE international symposium on empirical software engineering and measurement, pp.29–38. doi:https://doi.org/10.1145/2372251.2372257.
15.
KähkönenTSmolanderK (2013) ERP Integration - A systematic mapping study. In: Proceedings of the 15th international conference on enterprise information systems, Vol. 3, 23–35. doi:https://doi.org/10.5220/0004419900230035.
KhalafRSubramanianRMikalsenT, et al. (2010) Enabling community participation for workflows through extensibility and sharing. In: Rinderle-MaSSadiqSLeymannF (eds) Business Process Management Workshops. BPM 2009. Lecture Notes in Business Information Processing. Berlin, Heidelberg: Springer, Vol 43. https://doi.org/10.1007/978-3-642-12186-9_20.
18.
KitchenhamBBreretonP (2013) A systematic review of systematic review process research in software engineering. Information and Software Technology55(12): 2049–2075.
19.
KitchenhamBAChartersS (2007) Guidelines for performing systematic literature reviews in software engineering. EBSE Technical Report (EBSE-2007-01), 1–65.
20.
KourtesisDKuttruffVParaskakisI (2011) Optimising development and deployment of enterprise software applications on PaaS: The CAST project. In: CezonMWolfsthalY (eds) Towards a Service-Based Internet. ServiceWave 2010 Workshops. ServiceWave 2010. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, Vol 6569. https://doi.org/10.1007/978-3-642-22760-8_2.
21.
LimTMLeeASHYapMK (2016) User acceptance of SaaS ERP considering perceived risk, system performance and cost. In: KimKJoukovN (eds) Information Science and Applications (ICISA) 2016. Lecture Notes in Electrical Engineering. Singapore: Springer, Vol. 376. doi:https://doi.org/10.1007/978-981-10-0557-2_6.
22.
McWilliamHLiWUludagM, et al. (2013) Analysis tool web services from the EMBL-EBI. Nucleic Acids Research41(W1): W597–W600.
23.
MoherDLiberatiATetzlaffJ, et al. (2009) Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLOS Medicine6(7): e1000097.
24.
MooreBMahmoudQH (2009) A service broker and business model for SaaS applications. In: 2009 IEEE/ACS international conference on computer systems and applications, Rabat, Morocco, 2009, 322–329. doi:https://doi.org/10.1109/aiccsa.2009.5069343.
25.
MunteanMBrandasCCristescuM, et al. (2021) Improving cloud integration using design science research. Economic Computation and Economic Cybernetics Studies and Research55(1): 201–218.
26.
NamKKimJKimW, et al. (2006) Design and implementation of SoD engine for Linux. In: 8th International conference on advanced communication technology (ICACT), Vol. 2, 1323–1326.
27.
NgSWDamHKChoetkiertikulM, et al. (2018) Predicting issues for resolving in the next release. Lecture Notes in Business Information Processing234: 164–177.
28.
ObeidJSAlexanderRWGentilinSM, et al. (2016) IRB Reliance: An informatics approach. Journal of Biomedical Informatics60: 58–65.
29.
ParkSLeeSParkYB (2015) Best practices in software engineering for SaaS-cloud era. In: ParkJStojmenovicIJeongHYiG (eds) Computer Science and its Applications. Lecture Notes in Electrical Engineering. Berlin, Heidelberg: Springer, Vol. 330. doi:https://doi.org/10.1007/978-3-662-45402-2_31.
RussellJ (2003) Delivery excellence: A practical program for continuous improvement software development. IT Professional5(3): 37–41.
32.
SääksjärviM (2006) Success of IS outsourcing as a predictor of IS effectiveness: Does IT governance matter? In: HirschheimRHeinzlADibbernJ (eds) Information Systems Outsourcing. Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-540-34877-1_11.
33.
SaltanASmolanderK (2021) Bridging the state-of-the-art and the state-of-the-practice of SaaS pricing: A multivocal literature review. Information and Software Technology133: 106510.
34.
SethSJohariRMahajanS, et al. (2019) Design and development of optimized scheduling algorithm for software as a service based applications in secure cloud environment. In: 2019 6th International conference on signal processing and integrated networks (SPIN), Noida, India, 2019, pp.329–334. doi:https://doi.org/10.1109/spin.2019.8711611.
35.
ShatsOGoldnerWFengJ, et al. (2016) Thyroid cancer and tumor collaborative registry (TCCR). Cancer Informatics15: CIN.S32470. doi:https://doi.org/10.4137/cin.s32470.
36.
Van EsK (2023) Netflix & big data: The strategic ambivalence of an entertainment company. Television & New Media24(6): 656–672.
37.
WakrimeALabbaciHRekikM, et al. (2020) Toward reconfiguration of BPaaS based on SaaS services using clustering and boolean satisfiability. SN Computer Science1(3): 156.
38.
WohlinC (2014) Guidelines for snowballing in systematic literature studies and a replication in software engineering. In: Proceedings of the 18th international conference on evaluation and assessment in software engineering, pp.1–10. Association for Computing Machinery. doi:10.1145/2601248.2601268.
39.
WuJChenQGaoS (2015) How SaaS application led to cloud enabled business innovation: A case study from China. In: PACIS 2015 proceedings, 9. https://aisel.aisnet.org/pacis2015/9
