Keywords

Shared Service, Global Capability Centre, Organization Performance.

Introduction

According to the Indian Government's FY2022–23 economic survey report, the Information Technology–Business Process Management (IT-BPM) industry in India has expanded tremendously over the last several decades to reach its present projected value of US$245 billion (NASSCOM, 2023). Key players in this growth story are the Global Capabilities Centers (GCCs), also known as Global In-house Centers (GICs), which operate on a Shared Service (SS) model. The GCCs are the offshore Shared Service Centers (SSCs) of major international corporations. The GCC market in India is estimated to be worth US$46 billion and is expected to grow at a compound annual growth rate (CAGR) of over 11.4%. These centers are established to take advantage of the distinct advantages that India has to offer, which go beyond cost and process efficiencies. The GCCs have advanced significantly from being centers of cost arbitrage to those of innovation and value arbitrage.

The purpose of the study is to create a coherent, organized, theory-based, and empirically proven model that includes all of the variables that affect how well Global Capability Centers in India function when it comes to Business Process (BP), Information Technology (IT), and Engineering Research and Development (ER&D). Lastly, the goal of this study is to produce previously undocumented research findings that can be applied to all GCC/ SS companies. The research implications are therefore of significance both to practitioners and academic researchers. From academic contribution perspective, the study contributes to the existing scientific knowledge of Shared Services business model as a strategic management concept. Furthermore, this study's deductions are applicable to other emerging economies and are not region-specific as the study has shown that Shared Services performance is a function of certain universal variables that are discussed in the following sections.

A Snapshot of Indian GCC Landscape

The Global Capability Centres in India now number over 1580, employing over 16 lakh individuals with a market size of around US$46 billion (NASSCOM, 2023). About 73% of all new centers have all three functional areas—IT, BPM, and engineering research and development—which says a lot about the variety of competent people that is currently accessible in India for a range of roles and functions. (Figure 1)

‘Shared services’ is the strategic business model of GCCs of most Fortune 500 companies to achieve organizational goals of leveraging growth or enhanced customer experience by a combination of measures such as cost reduction, higher quality business processes, standardization etc. The key differentiator of a GCC is its evolution from being just a cost centre to a value creator.A host of indigenous factors grant India ‘the most favoured GCC destination’ status resulting in the remarkable growth of the industry. India is leveraging its geographical advantage of ‘follow-the-sun’ location in providing general and specialized support functions to the enterprises worldwide (Figure 2). The unique strategic advantage of readily-available, trainable talent pool helped the country to rise to the position of leading business service provider globally (Mudambi & Venzin, 2010). (Figure 2)

Theoretical Background

The current section summarizes the key concepts pertaining to the subject of GCCs and Shared Services (SS), and key theories that provide strong foundation to the research.

Shared Services and Global Capability Centres

When business support functions such as F&A (Finance and Accounting), HR (Human Resources) or IT (Information Technology) are consolidated and provided from a separate unit to the rest of the organization, such arrangement can be termed as Shared Services. In this case, a semi-autonomous organizational unit provides internal services to numerous organizational units through the aggregation of business functions that are backed by a sharing arrangement (Miskon et al, 2010). The contemporary name given to the most evolved Shared Services centre is Global Capability Centre, which according to NASSCOM is a ‘strategic capability centre’ rather than a ‘delivery centre’ and describes a GCC as follows:

“Over the past few years, Global In-house Centres (GICs) have continued to evolve and mature at a rapid pace and earning enormous credibility equity along the way in recognition to their disruptive impact on transformation in a broad range of industries. GICs have matured to become trusted enterprise transformational agents and have morphed into becoming strategic partners to reshape the future of the business. Powered with a combination of strategic capacity and capabilities, GICs are now the ‘new’ business-as-usual for companies and it is only apt to (re) christen them as Global Capability Centers (GCCs)”.

The Shared Services Business Model

Research on the theoretical underpinnings of the shared services concept has revealed that, as of yet, no comprehensive explanation of the idea has been developed. Following this, diverse theories were used to draw varying insights or impart different dimensions to explain a specific aspect or attribute of the concept. New Institutional Economics theories that have been frequently employed to describe the SS phenomena include the principal-agent theory, transaction cost theory, resource-based view, and property-right theory (Wang & Wang, 2007; Esteve-Pérez & Mañez-Castillejo, 2008). Other theories such as the competence-based theory, capabilities-based game theory, dynamic capabilities-based theory, industrial economics, the network view, neoclassical economics, and resource-dependence that were earlier used to describe the outsourcing concept maybe used to explain the Shared Services concept.

One of the theories that underpin the study is the resource-based theory, where an organization’s success is ascribed to its ability to acquire VRIN (Valuable, Rare, Inimitable and Non-substitutable) resources (Ray et al, 2004; Penrose, 2009). The interconnected set of variables related to resources, processes, partners, customers, cost, revenue etc are coordinated and used for customer value creation and delivery, for a cost. Following earlier research in the area, the theoretical foundation for the Shared Services concept may be discussed by means of an eclectic mix of established economic and management theories that are specifically relevant to the research topic which are discussed below.

Business Model, a Key Component of Business Strategy

A definition of business model adopted for this study is the one put forth by (Zott et al, 2011). This is in contrast to the perspective of previous researchers, as the authors expand the scope of the firm-centric activity of business model development to include two important stakeholders outside the ambit of the firm, suppliers and users. Earlier researchers described business model as a “firm-centric system of interrelated activities aimed at value creation and appropriating a share of that value”. The authors reposition the business model to one that transcends the firm-focused or firm-limited orientation for value creation.

The Service Ecosystems Perspective of Business Models

The "service-strategy-based understanding of business models" that was derived from "the service ecosystems perspective" and put forth by (Vargo & Lusch, 2016) is used in this study. The theory provides a shared theoretical framework for understanding how markets, technologies, and business models interact performatively. The current study demonstrates the suitability of the ‘service ecosystems perspective on business models’ in the context of Shared Services. Therefore, the study develops on the aforementioned work thereby adding to the literature on GCC or SS performance and the role of technology disruption in it.

The Balanced Scorecard

One of the most popular methods for performance measurement in GCCs in specific and Shared Services industry in general, the Balanced Scorecard (BSC) framework gives a comprehensive picture of the operations and gives strategic direction to performance evaluation beyond cost or other financial considerations. The "Balanced Scorecard," which was first put forth, provides managers and other leaders with an extensive framework that converts corporate strategic goals into a sensible set of performance measurements. The current study recognizes the importance of evaluating GCC or SSC overall performance with respect to the above criteria and framed the questions for the online survey to understand what factors affect the following measures of GCC performance: Cost, Customer, Internal Business processes and People, Learning and Growth.

Conceptual Framework and the Formulation of Hypotheses

The research proposes that the GCC performance is affected by a set of factors, external and internal, positive and negative. Critical Success Factors are the factors essential for the successful performance of the GCC, while challenges when not mitigated become failure factors that pull down the performance. Therefore, a prior understanding of such factors by the GCC managers could support strategic, tactical, and operational decision making that could directly influence the GCC performance.

Constructs – Critical Success Factors and Challenges for Gccs’ Performance

These reasons or factors can be classified into – internal, such as organizational causes; external, such as political environment; others, such as social or cultural nature. Further, a deep cognizance of factors that could influence the extent of success or failure can form robust grounds for the design and deployment of Shared Services. They serve as practical guiding principles in the aspects of organizational structures and governance frameworks. Moreover, an understanding of the nature of organizations that have adopted the model helps in the success and sustainability of strategic implementations (Teece, 2019). In order to determine the critical success factors (CSF) for this study, the definition provided by (Gable et al., 2008) has been used: CSFs are factors that are essential to enhancing the degree of success experienced and whose existence suggests a benefit to the Shared Services initiative.

The authors highlight that it is important to know these factors in order to avoid or mitigate the consequences of failed Shared Services implementation demonstrated as unfinished projects or abandoned tasks related to implementation; other failures classified as correspondence failures, process failures, interaction failures and expectation failures resulting in - unmet objectives, missed deadlines, misinterpreted specifications and ineffective communication – to name a few. However, the studies that report success or failure of this model are largely contextual, limiting the generalizability or applicability across situations or organizations. The current study therefore attempts to arrive at the list of top factors common across services offered by these units without being restrictive on applicability.

Constructs – Emerging Technology and Talent Management

The GCC model is seen as an effective way to transform service delivery with technology intervention. An in-depth study of literature led to the identification of a set of technology related variables that influence GCC performance.

Construct – Shared Services or Global Capability Centre Performance

Of the most popular SSC or GCC performance management and measurement frameworks in vogue, Balanced Score Card framework has been chosen for the current study as the four performance indicators of the BSC framework capture broadly most Key Performance Indicators KPIs of GCCs.

The conceptual framework is then generated with the selection of variables that best support the study requirements and then framing the various causal interrelationships among them. Variables sourced from earlier studies have been adopted and renamed to suit the needs of the current study. Variables under each construct that were retained after the pre-test or pilot have been captured in the Figure 3 depicting the ‘Global Capability Centres Performance Model - A Conceptual model’ below. (Figure 3)

Influencing Factors

The Table 1 that follows lists the variables of the study construct-wise and the source of the same in literature. (Table 1)

Table 1 Measures and Source
Construct	Variables*	Source
Critical Success Factors (CSF)	Implementation strategy, Vision, Ownership, Strategic alignment and more	Success Factors for Software Outsourcing Partnership Management: An Exploratory Study Using Systematic Literature Review (Ali et al., 2017)
Challenges (CH)	Resistance to change, Lack of Change management Strategy, Standardization impediments etc	Understanding the Factors That Influence the Adoption of BPM in Two Brazilian Public Organizations (Syed et al., 2018) Organizational E-Government Readiness: An Investigation in Saudi Arabia (Alghamdi et al., 2014) A quality framework for services in shared service environments (Ramphal, 2011)
Emerging Technology (EMT)	Impact of Technology on service quality, Impact of Technology on scaling up, Technology adoption limited by talent shortage etc	Business value of information technology: Testing the interaction effect of IT and R&D on Tobin's Q. (Bardhan et al., 2013) Impacts of Robotic Process Automation on Global Accounting Services (Fernandez and Aman, 2018). Robotic Automation Process-The next major revolution in terms of back- office operations improvement. (Anagnoste, 2017)
Talent Management (TM)	Talent Strategy, Talent Upgradation, Skill gap consequences	Discussion on the reform of accounting talents cultivation in colleges and universities based on financial shared service A Review of Success Factors and Challenges of Public Sector BPR Implementations.(Jurisch et al., 2012) A study on talent management and its impact on organization performance-an empirical review. (Hongal and Kinange 2020).
GCC Performance	Cost, Customer, Internal Business Process, and People, Learning and Growth.	Balanced scorecard (Kaplan and David, 2007)

Hypotheses

The hypotheses that were derived are as follows. The null hypotheses are listed here.

I. The Critical Success Factors, Challenges, Emerging Technology and Talent Management do not influence the ‘Customer’ indicator of SSC or GCC performance

II. The Critical Success Factors, Challenges, Emerging Technology and Talent Management do not influence the ‘Cost’ indicator of SSC or GCC performance

III. The Critical Success Factors, Challenges, Emerging Technology and Talent Management do not influence the ‘Internal Business Processes’ indicator of SSC or GCC performance

IV. The Critical Success Factors, Challenges, Emerging Technology and Talent Management do not influence the ‘People, Learning and Growth’ indicator of SSC or GCC performance.

Research Methodology

The research strategy is descripto-explanatory, where both inductive and deductive approaches were employed. In order to achieve its goals, the study used both qualitative (expert interviews) and quantitative (questionnaire-based survey) research approaches were used to test the hypotheses. The study was set in the GCCs offering BP, IT and ER&D services in Bengaluru, Chennai and Hyderabad, the top three GCC destinations in South India, as featured in the NASSCOM’s 2023 report on GCCs.

At stage 1 of data collection, survey research, a prominent quantitative methodology tool was used to collect data and test the hypotheses objectively among sample that was representative of the entire GCC population in India. At stage II, qualitative research methodology has been adopted. Face to face semi structured interviews were conducted with 10 C-level executives who directly manage the GCCs in order to get their perspectives and insights.

Data Analysis

Descriptive Evaluation of the Data - Participants Profile (Table 2)

The percentage distribution of the respondents’ hierarchy-wise follows the industry average of organizational hierarchy-split (approximately) i.e., 20% - senior/top management, 30% - middle level and 50% - bottom level of organizational hierarchy. The percentage of respondents in Business Process Services was about 42%, while IT Services, and Engineering Research and Development Services ranked second and third respectively at 27% and 31%.

Descriptive statistics data pertaining to the current study are presented in Table 2. Standard deviation is used as a dispersion indicator to show how the responses vary from the mean, whereas mean is considered as a central tendency indicator to determine the respondents' opinions about various factors.

Table 2 Descriptive Statistics
Variable	Mean	Std. dev	Coefficient of variation (CV)
CSF	3.531	0.491	0.162
CH	2.811	1.508	0.527
EMT	3.314	0.353	0.105
TM	3.809	0.566	0.137
CO	2.502	0.651	0.244
CU	2.896	1.753	0.597
BP	3.836	0.553	0.145
PLG	3.824	0.559	0.134

Discriminant Validity and Convergent Validity Tests

The interrelationships between the variables were examined using correlation analysis. (Table 3)

Table 3 Correlation, Discriminant Validity Analysis, and Convergent Validity Analysis Reliability Analysis
Variables	CSF	CH	EMT	TM	CO	CU	IBP	PLG	AVE		CR
										MSV
CSF	-0.762								0.581	0.335	0.806
CH	0.321	-0.82							0.673	0.411	0.839
EMT	0.267	0.473	-0.751						0.565	0.265	0.938
TM	0.351	0.364	0.513	-0.818					0.67	0.385	0.831
CO	0.176	0.545	0.401	0.293	-0.77				0.594	0.424	0.863
CU	0.339	0.232	0.154	0.318	0.208	-0.779			0.608	0.212	0.829
IBP	0.301	0.556	0.495	0.317	0.713	0.218	-0.756		0.572	0.242	0.839
PLG	0.331	0.332	0.405	0.299	0.513	0.268	0.278	-0.775	0.602	0.342	0.844

The discriminant validity was evaluated using the square root of the average retrieved variance. Discriminant validity is demonstrated by the square root of AVE being larger than its association with other variables. As an alternative, the MSV value of AVE is compared with all variables to verify discriminant validity. Discriminant validity can be considered as achieved in the current study as AVE is greater than MSV (Fornell & Larcker, 1981). To investigate any possible associations between these items, AVE and item loadings were used in a convergent validity study. The variables have 50% more variance, according to the results, which show that all of the AVE values are more than 0.5 (Table 3). Composite reliability (CR) analysis has been used to ensure that all variables are consistent. The findings show CR values that are greater than the recommended threshold of 0.70.

Cronbach-α was estimated in order to conduct the reliability analysis test. According to Fornell & Larcker (1981), the least acceptable value of Cronbach-α is 0.70. The findings show that every variable exceeded the benchmark value, as presented in Table 4 below.

Table 4 Reliability Analysis Results
S.No	Constructs - Variables	Cronbach-a
1	Critical Success Factors (CSFs)	0.813
2	Limiting Factors/Challenges (CH)	0.832
3	Emerging Technology (EMT)	0.916
4	Talent Management (TM)	0.91
5	Cost (CO)	0.903
6	Customer (CU)	0.809
7	Internal Business Processes (IBP)	0.874
8	People Learning & Growth (PLG)	0.807

Factor Analysis

This study has employed factor analysis, which is among the most effective techniques for reducing variables. Eigen values were used to determine the number of variables, and factors having an Eigen value larger than one were extracted. Using Bartlett's test of sphericity (BTS) and the Kaiser–Meyer–Olkin (KMO) test, the data's suitability for factor analysis was evaluated. Factor analysis is deemed appropriate for the data set based on the BTS result of significance levels less than 0.05 and the output value of 0.955 for KMO, which indicates data fitness to proceed with factor analysis.

Factor 1 has all variables which talk about Critical Success Factors and its influence on the GCC performance. Hence, this was named as CSF. Factor 2 has predominately variables from the Challenges group such as lack of communication strategy, lack of suitable talent management strategy and apparent lack of predefined SLAs, to name a few. So, this was named as CH. Factor 3 has variables which refer to the role of Emerging Technology in GCC performance. Consequently, this was termed as EMT. Factor 4 has variables which are related to the function of Talent Management in GCCs. Therefore, this was termed as TM. The fifth factor was excluded from the analysis as the variables under these two factors are not unique and are already part of the other four factors.

Structural Equation Modeling

In order to arrive at a statistically verified theoretical model, AMOS - the extension module of SPSS was used. AMOS, an acronym for Analysis of Moment Structures is a visual program for Structural Equation Modeling (SEM). One of the purposes of SEM is to obtain the estimates of the model parameters, while the other is to assess the fitness of the model Estimation and Model Fit. The result for goodness of fit model statistics as shown in the ‘Model Fit Summary’ is that all the indices indicate the fitness of model (Table 5) (Figure 4).

Table 5 Model Fit Summary
Fit parameter	Result
RMSEA- Root Mean Squared Error of Approximation	0.029 (PCLOSE Insignificance)
RMR -Root Mean Square Residual	0.039
GFI - Goodness of Fit	0.912
CFI - Comparative Fit Index	0.925
AGFI - Adjusted Goodness of Fit	0.91
NFI - Normed Fit Index	0.933
RFI - Relative Fit Index	0.911
IFI -Incremental Fit Index	0.924
LI - Tucker–Lewis index	0.946

Items with substantial loadings—that is, those with values greater than 0.7—are sustained. The results show that every item loading exceeded the 0.7 threshold (see Figure 4). The RMSEA of <0.08 is interpreted as a good fit. Therefore, RMSEA of 0.029 indicates a good fit here. Here, the Root Mean Square Residual RMR is 0.039 which is less than 0.05 and closer to 0. Hence it indicates good fit. AGFI close to (and less than) 1 is considered a good fit. Similarly, since AGFI is reasonably close to 1 at 0.91, it is also considered a good fit. CFI should be equal to or greater than .90 for the acceptance of the model. Here CFI is 0.925. IFI, NFI, RFI are all above 0.9, showing good fitness. (Table 6)

Table 6 Standardized Regression Weights
Variables			Estimate
F1	<---	CHSNEW	.719
F1	<---	CFSSNEW	.701
F1	<---	EMT	.836
F1	<---	TMSNEW	.956
Customer	<---	F1	.964
Business_Process	<---	F1	.759
Cost	<---	F1	.944
People_learning	<---	F1	.917

The Standardized Regression Weights of most variables in Table 6 are above 0.7 with positive symbols. This shows that the factors identified in the study have a good influence on the dependent variable, GCC performance. The high factor loadings also indicate that the strong association between variables. As a result, we can say that the data supports the theoretically constructed conceptual model, also known as the "Global Capability Centers Performance Model" or "GCC Performance Model." That is, the model provides a good fit to the data.

Discussion

The purpose of this study is to investigate the elements that have a positive and negative impact on GCC's performance and to use structural equation modeling (SEM) to propose a conceptual model with factors influencing its performance. The identification of variables that positively impact GCC performance, such as talent management, emerging technologies, and critical success factors, as well as obstacles or failure factors that could have a negative impact if left unchecked or poorly addressed, can be seen as achieving the research goal. Each of the four GCC performance indicators—cost, customer, internal business processes and people, learning and growth variables—is impacted by the aforementioned elements. The robust connections found in the performance model's SEM output have supported this. The results of the SEM confirmed that the primary data supports the theoretically designed research model. Extended research on the use of SEM to identify factors impacting specific dimension of GCC performance may help in the prediction of GCCs performance.

The analysis of the results indicate that BP services tend to be labour intensive and relatively more process oriented than the other two services Therefore, they need a different set of strategies to attain and maintain results. This results in a completely unique set of factors which are critical to the success of the centre offering BP services. The same can be said about IT service vertical where the complexity of the work being done and timelines are different from that of BP vertical. This presents a different set of success factors that impact the IT verticals performance. The high-tech environment of ER&D centres typically needs state of art equipment, have long lead times and lower rate of successful completion. Consequently, the factors that are key to the success of the ER&D vertical are different from those of the other two service verticals. The data shows that the emerging technology has a particularly important role in driving the performance of the organization. The findings support the widely reported role of technology in Shared Services literature that technology adoption is slowly and surely becoming the primary influencing factor impacting GCC performance. With respect to talent management in GCCs, the most common challenges faced during the implementation are i) Shortage of skilled people to handle the newly consolidated, integrated functions ii) Retaining trained people through the transformation process, iii) Reskilling and upskilling in response to each process and technological development. Employees with low skill barriers perceived technology more as a hindrance than an enabler for operational convenience. This can be resolved by informing the staff members in advance of impending changes, how the changes will affect them, and how the business would assist in allaying their worries by providing them with retraining and upskilling.

Conclusion

The relative importance of the influencing factors on the GCC performance unravelled in the course of the study present significant decision-making support to practitioners. Therefore, the current research on GCCs contributes significantly to the literature on Business strategy in general and Shared Services or GCCs in particular. Further, the findings are in line with various studies, both academic and corporate about the potential of India as one of the best GCC destinations due to the unparalleled advantages unique to the country - availability of trained or trainable talent pool, industry-favourable government policy and a vibrant supporting ecosystem. The current study fulfils for it being an original investigation in the field of applied business research for the following reasons: (i) The study is first-of-its kind academic research of the contemporary business phenomenon of Shared Services(SS) from Indian perspective (ii) the study is of specific relevance to practising GCC managers, as it identifies the factors that influence the GCC performance and provides suggestions for solving real business challenges (iii)Finally, the study contributes to the advancement of the knowledge in the area of Shared Services strategic business model. The discovery of critical success elements and performance- limiting factors for GCC serves as a good starting point for further longitudinal or cross- sectional studies that recommend viable options to expand the realm of Shared Services industry in India. The need for a thorough scientific examination of the practice-focused Shared Services concept is key research implication of the study.

The practical or industry implication is that the findings are of significance to the entire spectrum of people in BP, IT or ER&D Shared Services i.e., consultants, analysts, industry practitioners, academicians, and scientists. Additional research focusing on each of the findings and their impact on services offered could move capability centres up the value chain especially in BP and ER&D areas. Managers can use the SEM model to identify the factors they need to focus such as specific critical success factors and challenges applicable to their service type and address them to achieve the goals. The study's conclusions about important success factors, such as talent management and technology, as well as the dynamics between them and the difficulties that must be handled or overcome, are helpful to practitioners in obtaining the targeted GCC performance. The factors that were identified as a part of this study that influence the Shared Services or GCC performance, either positively or negatively, can be further studied individually or/and collectively in greater detail potentially resulting in service or industry- specific, GCC lifecycle-specific or hierarchy-specific critical information on important levers for performance. The growing importance of technology as a positive disruptor in operations cannot be overemphasized or so, claim the findings of the study, lending even more significance to the need for future research in the area.

India's top source of skilled job creation is the GCC sector. By implementing policy changes to close the skill gap between supply and demand in the GCC/SS business, policymakers can fairly mitigate the widespread unemployment that is evident in our nation, even within the competent applicant pool.

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Received: 28-Jun-2024, Manuscript No. AMSJ-24-14969; Editor assigned: 29-Jun-2024, PreQC No. AMSJ-24-14969(PQ); Reviewed: 26-Jul-2024, QC No. AMSJ-24-14969; Revised: 16-Aug-2024, Manuscript No. AMSJ-24-14969(R); Published: 19-Sep-2024