Modelling the Relationship Between Information Technology Infrastructure and Organizational Agility: A Study in the Context of India

Abstract

This article mainly investigates the impact of information technology (IT) infrastructure on organizational agility. Primary data collected from 300 business and IT executives working in various publicly owned banking groups functioning across India have been used for this study and a structural equation modelling (SEM) is employed to assess the IT-agility link. This article reports two-folded research findings. First, IT infrastructure enables both the sensing and responding components of organizational agility. Second, firms should not overlook the IT-agility contradiction, that is, the impeding role of IT towards achieving augmented agility. This study greatly contributes to the information systems (IS) literature as it has meticulously explored the much discussed but understudied human IT-agility linkage. The present research has successfully established the significant positive relationship between the critical dimensions of agile human IT infrastructure, namely, business functions, interpersonal management, technology management expertise and organizational sensing and responding agilities.

Keywords

Human IT infrastructure organizational agility SEM IS knowledge and skills

Introduction

In recent times, organizations are experiencing an intensive competitive stress generated due to technological progress, changing customers’ demands, economic cycles, globalization, etc. Due to the prevalence of these hypercompetitive aspects in the contemporary business environment, the firms have started acknowledging ‘agility’ as a critical strategic organizational competence which enables the firm to sense and seize the market opportunities. Therefore, it is indispensable that firms continually adjust their resources, infrastructure and strategies in order to remain adaptable to internal and external changes. To sense the environmental changes and respond readily is essential for every organization so as to become agile and it is an imperative determinant of firm success (Overby, Bharadwaj, & Sambamurthy, 2006).

Presently, firms are investing in information technology (IT) infrastructure to create superior IT capability which triggers augmented agility. Following Lu and Ramamurthy (2011), high-quality IT infrastructure generates IT-enabled innovative and rapid responses to deal with unexpected market-related changes. Therefore, the business and information system (IS) executives have recognized the role of IT as an enabler of firm’s agility and accepted this as a key contributor in a firm’s capability to realize and respond to market changes (Chen et al., 2015; Roldán, Leal-Rodríguez, & Felipe, 2015). Henceforth, organizations can become more responsive by operating in an agile IT platform, where IT infrastructure has the ability to expand or contract to meet the market demands.

Following Fink and Neumann (2009), the IT infrastructure is broadly categorized as the human IT components and the technical IT components. They have suggested that the human IT components highlight the business knowledge/skills, technical knowledge/skills and behavioural knowledge/skills of IT professionals, where the technical IT components outline hardware, operating systems/software, shared network connectivity, information sharing networks, communication protocols, data, efficient data management systems, etc. This study focuses on the human IT elements and thoroughly investigates their effect on agility. Based on a meticulous empirical analysis, this work examines the different measures under human IT infrastructure and organizational agility by highlighting a more holistic and comprehensive understanding of their relationship.

Organizational agility highlights togetherness among people, process and systems to accomplish organization’s objectives, visions and strategies. The research work conducted by Overby et al. (2006) recommends that effective IT deployment fosters in a high-velocity information transfer, which facilitates the swiftness of the firms to quickly realize and respond to market uncertainties. Therefore, superior human IT infrastructure assists the firms to develop their sensing ability through quick detection and interpretation of a diverse range of market opportunities. The responding agility of the firm delineates its ability in making the quick transformation of knowledge into action relating to the unanticipated business environmental changes. Extending the existing concept of IT-agility linkage, the present work accentuates these two constructs from a human IT infrastructure and organizational sensing and responding agility perspective. Therefore, this article addresses the most important research question, that is, ‘Does agile human IT infrastructure enhance organizational agility?’

This research primarily investigates both the independent variable (human IT infrastructure) and dependent variable (organizational agility) in terms of second-order reflective measures and tries to establish a significant structural linkage between the human IT infrastructure dimensions, namely, business functions, interpersonal management and technology management expertise (Lee, Koh, Yen, & Tang, 2002) and organizational agility components such as sensing and responding agility. To be precise, this article meticulously examines three categories of relationships, that is, first the business functional expertise and sensing and responding organizational agilities linkage, second the interpersonal management expertise and sensing and responding organizational agilities association and lastly the technology management expertise and sensing and responding organizational agilities connection.

The subsequent sections of this study are illustrated as follows: the second section represents the review of literature describing the theoretical backgrounds of the IT-agility association. The methodology and analysis are described in the third and fourth sections, respectively. Lastly, the concluding remarks, research implications and limitations and future research avenues are exhibited in the fifth, sixth and seventh sections, respectively.

Review of Literature

Organizational Agility

Recently, the concept of agility has drawn significant attention of a wide-ranging IS researchers (Bi, Davison, Kam, & Smyrnios, 2012; Chakravarty, Grewal, & Sambamurthy, 2013; Galliers, 2007; Overby et al., 2006; Nazir & Pinsonneault, 2012; Tallon & Pinsonneault, 2011). The phrase ‘agile’ mostly defines organizations that are able to adjust and execute well in fast changing environments (Cai et al., 2013; Chen et al., 2015; Dove, 2001; Lu & Ramamurthy, 2011; Mao, Liu, & Zhang, 2015a, 2015b). Dynamic capabilities (Teece, Pisano, & Shuen, 1997), market orientation (Narver & Slater, 1990), strategic flexibility (Hitt, Keats, & Demarie, 1998) and absorptive capacity (Zahra & George, 2003), etc., are few critical concepts in the field of management on which the theory of agility may be built upon. Organizations have to be quick in accumulating their technical expertise, workforces and management with effective communication infrastructure so as to respond to continually changing, unanticipated customer demands in a fluctuating market environment (Chen & Siau, 2012; Mao et al., 2015a, 2015b).

The principles for agile systems from a business or a technical perspective have been introduced by Dove (1999, 2005). He has used the term system to depict a collection of interconnected modules shared by a common context to serve a common objective. From a business viewpoint, modules deal with groups of people, while from a technical angel it represents a collection of software components or machines. The responding component of agility is regarded as the ‘response ability’, which is the physical capability to act and the sensing component is concerned with the ‘knowledge management’, that represents the intellectual aptitude to identify suitable things to act on (Cai et al., 2013; Joshi, Farooquie, & Chawla, 2016; Liu, Song, & Cai, 2014; Mao et al., 2015a). The role of IT to support both the sensing and responding constituents of agility by spreading out the reach and richness of firm knowledge and processes is explained by the work conducted by Overby et al. (2006). According to them, firms require a diverse range of capabilities to realize the imminent environmental changes such as customers’ demand changes, competitors’ strategy changes, government regulatory policy changes, etc. This is referred as the sensing agility of the firm and in this research it is studied in terms of three indicators such as the ability to recognize the changing customers’ need and competitor’s actions, environmental changes, finally effective deployment of innovative and advanced technology.

Following Dove (2001), firms may obtain a wide range of responses starting from a complex response, simple response to no response. Launching a new product line, new ventures, etc., entail complex moves and change in product features, price, etc., indicate simple moves. Moreover, sometimes making ‘no move’ gets beneficial for the firms as long as this action does not hamper the firm’s ability to sense business opportunities. Therefore, in this article, the responding component of agility is studied by three composite measures such as quick response to changing competitor’s strategy and customers’ needs, IT reengineering to better serve clients and lastly responding to market-related changes as opportunities.

Human IT Infrastructure

Agility in human IT infrastructure is related to the IS professionals, business managers and decision makers of an organization who have embraced the perspective of working in an unstable environment to face any unanticipated change with the necessary knowledge, skill and expertise to efficiently deal with these changes. The research conducted by Lee et al. (2002) explores two categories of knowledge, such as non-technology related and technology related. According to them, business functional knowledge/skill and interpersonal management expertise are two critical components of the non-technology-related knowledge and efficient technology management refers to the technology-related knowledge. The business functional expertise is delineated as the ability of the business and IS personnel to understand the internal and external business operations, acquiring suitable knowledge about market competitors and business environments, and appropriately interpreting the business-related issues. Effective interpersonal skill is also imperative to the IS professionals and decision makers since it facilitates the proper planning, organizing, leading, cooperative working and problem-solving qualities to accomplish assignments. Last, the technology management skill is essential to deal with rapidly changing technologies which foster effective management of technological fundamentals so as to create long-run competitive advantage. Therefore, the business and IS personnel possessing varied and in-depth business functional knowledge/skill, interpersonal and technology management expertise are certainly better positioned to build up innovative physical services that facilitate effective IT-business strategic alignment and IT-enabled competitive advantages (Fink & Neumann, 2009).

The major research gap obtained from the prior literature studies is that limited empirical research works have been conducted on the sensing and responding constituents of agility which are ideally regarded as integral agility elements. Moreover, very few studies were conducted on the human IT infrastructure taking into account only the IT professionals of various primary IT community provider companies to assess its influence of on augmented business value (Fink & Neumann, 2007, 2009). However, previous literature do not support any such human IT-agility-related studies done on Indian firms’ perspective taking into account of both the IT and business personnel. Henceforth, this study builds and develops the premise that the human IT infrastructure of an organization needs to inculcate an approach of acknowledging the critical IS expertise, namely, business functions, interpersonal management and technology management as the crucial weapons to attain superior sensing and responding firm agility.

Research Objectives

Bridging the above-mentioned research gaps, this study focuses on exploring the major dimensions under human IT infrastructure and organizational agility so as to examine the enabling role of business functions, interpersonal management and technology management expertise towards enhanced sensing and responding organizational agilities.

Rationale of the Study

Since, the literature supports various IT and agility-related studies conducted mostly in the advanced countries such as USA (Lu & Ramamurthy, 2011), China (Chen et al., 2014), etc., India, being a developing economy this type of study has not been previously conducted. It is evident that the financial institutions like banks play a pivotal role in driving substantial economic growth in India. Notwithstanding, the Indian banking groups have been utilizing core banking system (CBS) and other banking solutions for quite a long time so far, the research focusing on the IT-agility connection on these groups is very thin on the ground. Furthermore, there is little research on the benefits of utilizing human IT infrastructure elements to achieve augmented sensing and responding firm agility particularly in context to the Indian banking units. Therefore, it is important to study how these organizations exploit IT as their crucial competence for superior agility.

Methodology

Research Design and Sampling Procedure

This study thrives for a quantitative approach in order to answer the research question and accomplish the objectives utilizing an exploratory research design where a simple random probability sampling procedure has been employed to select the samples.

Scope of the Study

In order to avoid the perplexing effects on industrial variation, this study targets on small size public sector banks (PSBs), having higher gross non-performing assets (NPA) to gross advances ratio functioning in the state of Odisha and West Bengal, in eastern India. Out of 27 PSBs, three PSBs such as UCO bank, United bank of India (UBI), Vijaya bank were selected on the basis of high gross NPA to gross advances ratio, which is 5.42, 4.25 and 2.17, respectively (as of 31 March 2013, source: Reserve Bank of India, 2013). This research highlights on the aspects of how the bank managers and IT professionals working in these selected PSBs can help to attain more profitability by realizing and quickly responding to the unprecedented market-related changes. Banks operating in the urban regions are selected as a target sample frame and the scope of the study is limited to the business and IT executives working in the middle and senior level of management.

Data Collection

According to Lu and Ramamurthy (2011) and Chen et al. (2014), a matched-pair field survey is ideal for investigating the IT-agility linkage. In addition, Lu and Ramamurthy (2011), Cai et al. (2013) and Mao et al. (2015a) have studied various financial units containing banks, insurances and other financial services to gather responses from the informants. Based on these prior researches, this type of study was administered to collect primary responses from business and IT executives via offline and online modes, respectively. First, a total of 620 numbers of structured questionnaires were distributed by hand delivery method among the business executives out of which 390 filled questionnaires were returned. The contact information and e-mail addresses of IT executives were collected from them and a total of 477 numbers of e-mail addresses were collected, but only 386 of them were found to be having valid addresses. Excluding the unmatched items and missing data, the final sample size was determined to be 300, representing 48 per cent valid response rate. As presented in Table 5, the Hoelter index denotes the suitability of the sample size to conduct the analysis (see Table 1 for complete sample profile).

Empirical Model

The study concentrates on developing an empirical model by precisely examining the significant relationship between the human IT infrastructure components as the independent variable and organizational agility elements as the dependent variable. The conceptual research model is illustrated in Figure 1. After a thorough analysis, the final empirical model demonstrating the nature of structural associations among the individual items is represented in Figure 2.

Table 1.

Respondents’ Demographic Profile (N = 300)

Participants’ Age (in Years)	Work Experience (in Years)	Percentage
18–25 26–35 36–45 46–55 56–60	5–10 11–15 16–20 21–25 26 and more	12 26 23 28 11
Total		100
Participants (Matched Survey)	Frequency	Percentage
Business ExecutivesGeneral managersRegional managersBranch managersInsurance managersOther managers	23491338807	07.6616.3444.3329.3302.34
Total	300	100
IT ExecutivesChief information officersIT directorsIT managersOther IT executives	348714435	11.3329.0048.0011.67
Total	300	100

Source: Authors’ own findings.

Development of Instruments

The proposed research model is operationalized by using an ordinal five-point Likert-type rating scale to collect the responses for the studied multi-item measures where both the extreme points exhibit ranges from strongly disagree (1) to strongly agree (5).

Research Measures

The present study examines human IT infrastructure as a formative second-order construct comprising of technology management, business functions and interpersonal management expertise as the three crucial indicators and both the business and IT executives have been selected as target participants for these measures. The organizational agility is also studied in-terms of second-order reflective measures, namely sensing agility and responding agility focusing on business executives only (see Table 2 for the list of latent constructs along with indicators).

Validation of Instruments

First, an exploratory factor analysis (EFA) was conducted to examine the accumulated data utilizing SPSS (20.0) and in total 22 numbers of variables have been analysed out of which 5 numbers of factors were extracted through ‘principal axis factoring’ method based on the Eigen value greater than 1 involving ‘promax’ rotation. All these extracted factors explain nearly 63 per cent of variance. The factor loadings above the value of 0.5 have been taken into consideration and presented in Table 3. Similar range of factor loading values have also been highlighted by Lu and Ramamurthy (2011). Then, a confirmatory factor analysis (CFA) was performed using AMOS (20.0). To check the reliability, construct validity and good data fit, a series of tests were conducted which are discussed in the subsequent sections.

Analysis

Test for Construct Reliability

First, the composite reliability highlighting the internal consistency of the individual constructs was tested. The composite reliability values calculated for each construct had exceeded the standard value of 0.7. The unique and distinct measures studied under each construct were validated based on the value of Cronbach’s alpha and all the constructs exhibited the Cronbach’s alpha value within the range of 0.78–0.95 (see Table 4) which was higher than the threshold value of 0.7 (Hair, Black, Babin, & Anderson, 2010). This validated the higher reliability of the items selected under each construct.

Table 2.

List of Latent Constructs along with Indicators

Constructs	Indicators	Supporting Literatures
Business functional expertise	1) Proper understanding of the internal and external business functions (BFun1)2) Knowledge about market competitors and business environments (BFun2)3) Accurate interpretation of business issues (BFun3)	Lee et al. (2002)Fink and Neumann (2009)
Interpersonal management expertise	1) Aware about organizational culture and adapt to any organizational change (IMg1)2) Planning, organizing, leading, cooperative working and problem-solving qualities (IMg2)3) Propensity to project a positive attitude and be a proactive team player with an ability to accomplish assignments (IMg3)	Fink and Neumann (2007)Fink and Neumann (2009)
Technology management expertise	1) Effective management of technological fundamentals to create competitive advantage (TMg1)2) Ability to follow modern IT trends and use IT as a medium to attain organizational objective (TMg2)3) Integrated planning, design, optimization and operation of technological products, services and processes with a focus on developing effective IT skills (TMg3).	Lee et al. (2002)Fink and Neumann (2007)Fink and Neumann (2009)
Organizational sensing agility	1) Competitor’s actions and customer preferences (SAg1)2) Innovative and advanced technology (SAg2)3) Environmental changes (SAg3)	Overby et al. (2006)Chen et al. (2014, 2015)Chen et al. (2015)
Organizational responding agility	1) Quick response to changing competitor’s strategy and customer needs (RAg1)2) Restructuring of IT operations to better serve clients (RAg2)3) Responding to market related changes as opportunities (RAg3)	Overby et al. (2006)Chen et al. (2015)Mao et al. (2015a)

Source: Prepared by the authors.

Table 3.

Results Showing Exploratory Factor Analysis and Descriptive Statistics

Factors	Loaded Items	Factor 1	Factor 2	Factor 3	Factor 4	Factor 5	Minimum	Maximum	Mean	Standard Deviation
1. Business Functions	BFun1BFun2BFun3	0.840.780.74					1.002.001.00	5.005.005.00	3.753.553.62	1.030.950.92
2. Interpersonal Management	IMg1IMg2IMg3		0.720.730.62				1.001.001.00	5.005.005.00	3.493.493.63	1.081.061.02
3. Technology Management	TMg1TMg2TMg3			0.830.800.67			1.001.001.00	5.005.005.00	3.553.453.50	1.000.961.07
4. Sensing Agility	SAg1SAg2SAg3				0.550.720.75		1.001.002.00	5.005.005.00	3.603.533.60	0.960.910.91
5. Responding Agility	RAg1RAg2RAg3					0.760.790.79	2.001.001.00	5.005.005.00	3.523.543.52	0.950.990.96

Source: Authors’ calculations.

Test for Construct Validity

The construct validity was determined by calculating the convergent and discriminant validity of the items.

Test for Convergent Validity

First, the average variance extracted (AVE) values for each construct were tested and the calculated values of AVE were above the recommended value of 0.5 (Hair et al., 2010), which indicated that each latent construct was well explained by its observed variables. Then, a test of ‘significance’ estimated the t-statistics values for all the factor loadings as significant (since all p < 0.001), which demonstrated that the studied measures certainly established the convergent validity criterion. Similar analysis has also been performed by Bi et al. (2012) to test the convergent validity of the measures. Additionally, the significant factor loadings of distinct items on their designated constructs determined the convergent validity and the calculated standardized estimates derived from CFA for each construct confirmed that there was no convergent validity issue for the constructs (see Table 4).

Table 4.

Results Showing Confirmatory Factor Analysis and Correlation and Reliability of Latent Constructs

Model Constructs	Items	Standardised Loadings	Cronbach’s Alpha	Composite Reliability	AVE	MSV	ASV	1	2	3	4	5
1. Business functions	BFun1BFun2BFun3	0.95*0.920.76**	0.78	0.90	0.77	0.41	0.21	0.87
2. Interpersonal management	IMg1IMg2IMg3	0.93*0.690.79**	0.83	0.85	0.65	0.38	0.15	0.32	0.81
3. Technology management	TMg1TMg2TMg3	0.89*0.640.70**	0.90	0.78	0.55	0.03	0.01	–0.09	–0.06	0.74
4. Sensing agility	SAg1SAg2SAg3	0.87*0.950.96**	0.80	0.95	0.86	0.41	0.17	0.64	0.36	0.02	0.93
5. Responding agility	RAg1RAg2RAg3	0.73*0.710.82**	0.95	0.79	0.57	0.38	0.21	0.56	0.61	–0.18	0.36	0.75

Source: Authors’ calculations.

Notes: Diagonal elements are the square roots of average variance extracted; ***p < 0.001.

Test for Discriminant Validity

Discriminant validity is calculated when the unique and distinctive values of all the indicators converge at their specific true scores. It was determined by calculating the AVE scores, which generally explains the extent of variance extracted for each latent construct. The square root of the AVE for individual construct should be greater than the inter-construct correlation and this is the most appropriate determinant for discriminant validity. Table 4 represents that the square root of the AVE for each construct was showing superior value compared to its correlation with other constructs. The maximum shared square variance (MSV) and average shared square variance (ASV) are also other determinants of discriminant validity, and these estimated values were calculated to be less than the AVE values (see Table 4). Therefore, the issue of discriminant validity was not found to be a potential threat to the constructs.

Multiple data fit indices such as ratio of χ² to degrees of freedom (χ²/df), goodness of fit index (GFI), incremental fit index (IFI), normed fit index (NFI), comparative fit index (CFI), Tucker–Lewis index (TLI), relative fit index (RFI) and the root mean-square error of approximation (RMSEA), etc., were examined to validate the measurement and full structural model. Theoretically, the ratio of χ²/df value of below 3, the standard values of GFI, CFI, NFI, RFI, TLI and IFI higher or equal to 0.90 and an RMSEA value below 0.08 explain a good fit (Hair et al., 2010). Both the measurement and structural models were found to match the standard values of all of these fit indices as shown in Table 5. The structural linkage between the components of agile human IT infrastructure and organizational agility is illustrated in Figure 2.

Following Rai, Patnayakuni and Seth (2006), the path coefficients of the sub-constructs can be considered as the beta coefficients in a regression model. Based on this research, from Figure 2, it is apparent that the regression lines drawn from business functions and interpersonal management skill towards sensing and responding components are showing positive path coefficients and therefore, depict a significant positive relationship. The technology management expertise also shows a positive link with sensing agility, however, with responding agility it exhibits a negative relationship. Since, two out of three categories of human IT infrastructure dimensions (i.e., business functions and interpersonal management) represent a significant positive influence on the outcome variable and the last one (i.e., technology management) illustrate a partial positive effect, therefore, enough evidence is gathered to prove that human IT infrastructure possesses a significant positive relationship with organizational agility.

Table 5.

Fit Indices of Measurement and Structural Model

	Measurement Model	Structural Model
(χ²/df)	2.638	2.884
RMSEA	0.074	0.079
GFI	0.914	0.904
CFI	0.956	0.947
IFI	0.956	0.947
NFI	0.931	0.922
RFI	0.911	0.902
TLI	0.943	0.934
Hoelter	155 (p < 0.01)	104 (p < 0.01)

Source: Authors’ calculations.

Conclusions

This study has raised one important research question: whether human IT infrastructure acts as an enabler of organizational agility. It has been successfully inferred that agile human IT infrastructure components such as business functional skill, interpersonal management and technology management expertise, enhance both the sensing and responding constituents of organizational agility (Chen et al., 2014; Fink & Neumann, 2009; Overby et al., 2006). IT facilitates agility within an organization and by its effective utilization, human resources play a significant role in detecting any environmental changes and making proper assessments to deal with these changes. The major findings of this work propose that business and IT personnel need to concentrate on inculcating effective communication, cooperative working, efficient planning and organizing qualities for developing the knowledge about organizational culture so as to remain adaptable to any organizational change. Moreover, it is highly desired that they understand the internal and external business functions and accurately interpret any business-related issues. In addition, this study underscores the premise that effective technology management enables the firms attaining greater agility. Putting all together, it is concluded that firms that have concentrated on building an agile human IT infrastructure are found to be more attentive, responsive and adaptive to market changes and by this they may attain market leadership, profitability and sustainable competitive advantage in the long run.

Managerial Implications

The present research exhibits its uniqueness by examining agile human IT and organizational agility in terms of second-order reflective measures and successfully analyses their interconnection. This work is consistent with numerous existing bodies of research that underpin human IT infrastructure as a critical constituent for creating enhanced business value (Fink & Neumann, 2007, 2009).

One of the important findings of this study emphasizes on expertise of business and IT professionals regarding proper understanding of the internal and external business functions with knowledge about market competitors and business environments to accurately interpret the business-related issues. Another critical inference accentuates the proactive and positive attitude, cooperative working and other behavioural characteristics of the individuals to inculcate adaptability towards organizational changes. The study also underscores effective management of technological fundamentals with developed IT skill to follow contemporary IT trends and use IT as a medium to attain organizational objectives so as to create a long-run competitive advantage. Hence, it is suggested that the human IT infrastructure of an organization achieves agility by consolidating critical IS expertise such as business functional skill, interpersonal and technology management expertise so that suitable initiatives can be taken to comprehend the continually changing competitor’s actions and customers’ preferences (Fink & Neumann, 2009; Lee et al., 2002).

Throughout the survey, it was noticed that the banks generally used an integrated banking solution that helped in gathering change-related information and also executed its effective transformation. This banking solution along with a bank’s core banking system are likely to assist in improving the overall banking agility. However, the studied Indian banks are less profitable, having higher NPAs, which cause higher credit risk, reduced interest and increased carrying costs on non-income yielding assets. These aspects lead the banks to be less competitive in the market and diminish the sustainability and growth. Therefore, the inferences derived from this research certainly encourage the business and IT executives to enhance their capabilities in terms of envisaging the unprecedented competitors’ strategy, changing customers’ demands, environmental changes, etc. It is also imperative that they indeed focus on creating a sound IT platform to comprehend greater agility and proficiently manage organizational IT resources to attain superior business value.

Moreover, this study underlines the contradicting effect of IT on agility, which depicts that IT may sometimes impede agility depending on how it is deployed and managed in the organization (Overby et al., 2006). Tallon (2008) has explored the influence of ineffective IT management as an obstructing factor towards realization of superior agility. The present work supports these existing IT-agility-related studies and infers that ineffective IT management hinders a firm’s ability in availing a broad range of responses to deal with persistent market changes. This research work supports the studies that argue about the inflexible or unresponsive legacy IT system as a restrictive factor in a firm’s capability to respond to threats or opportunities (Tallon & Pinsonneault, 2011). From this it may be suggested that effective IT resource management is essential for organizations to realize augmented agility.

Limitations and Future Research

First, the small-sized PSBs having higher gross NPA to gross advances ratio functioning in the states of Odisha and West Bengal, in eastern India have been selected for this study, hence the inferred findings may not be generalized for a wider population. Second, according to past literature (Fink & Neumann, 2009; Lee et al., 2002), developing essential IS expertise to perceive and recognize agility is a long-term process, however, this study utilizes a cross-sectional research design that assesses the responses of the informants at one point of time. Therefore, the causal relationship among the studied variables is not established.

Further research should involve more longitudinal or experimental research designs to successfully delineate the causal relationship between agile human IT infrastructure and organizational agility. All these studied variables are considered as second-order latent constructs, while more research may investigate alternative items to better conceptualize these multifaceted latent constructs.

Footnotes

Acknowledgements

The authors are grateful to the anonymous referees of the journal for their extremely useful suggestions to improve the quality of the article. Usual disclaimers apply.

References

Davison

R.M.

Kam

& Smyrnios

K.X.

(2012). Developing organizational agility through IT and supply chain capability (Paper 64). Vietnam: Pacific Asia Conference on Information Systems (PACIS) Proceedings.

Cai

Huang

Liu

Davison

R.M.

& Liang

(2013). Developing organizational agility through IT capability and KM capability: The moderating effects of organizational climate (Paper 245). South Korea: Pacific Asia Conference on Information Systems (PACIS) Proceedings.

Chakravarty

Grewal

& Sambamurthy

(2013). Information technology competencies, organizational agility, and firm performance: Enabling and facilitating roles. Information Systems Research, 24(4), 976–997.

Chen

& Siau

K.L.

(2012). Effect of business intelligence and IT infrastructure flexibility on organizational agility. Thirty Third International Conference on Information Systems, 16–19, December, Orlando, FL.

Chen

Jin

Wang

Nevo

Wang

& Chow

W.S.

(2014). IT capability and organizational performance: The roles of business process agility and environmental factors. European Journal of Information Systems, 23(3), 326–342.

Chen

Wang

Nevo

Benitez-Amado

& Kou

(2015). IT capabilities and product innovation performance: The roles of corporate entrepreneurship and competitive intensity. Information & Management, 52(6), 643–657.

Dove

(1999). Design principles for highly adaptable business systems with tangible manufacturing examples. In Maynard

H.B.

& Zandin

K.B.

(Eds), Maynard’s Industrial Handbook. US: McGraw Hill.

Dove

(2001). Response ability: The language, structure, and culture of the agile enterprise. New York: John Wiley & Sons Inc.

Dove

(2005, March). Fundamental principles for agile systems engineering. Conference on Systems Engineering Research, Stevens Institute of Technology, Hoboken, NJ.

10.

Fink

& Neumann

(2007). Gaining agility through IT personnel capabilities: The mediating role of IT infrastructure capabilities. Journal of the Association for Information Systems, 8(8), 440–462.

11.

Fink

& Neumann

(2009). Exploring the perceived business value of the flexibility enabled by information technology infrastructure. Information & Management, 46(2), 90–99.

12.

Galliers

(2007). Strategizing for agility: Confronting information systems inflexibility in dynamic environments in agile information systems. (ed. Desouza

K.C.

, pp. 1–15). New York: Butterworth-Heinemann.

13.

Hair

Black

W.C.

Babin

B.J.

& Anderson

R.E.

(2010). Multivariate data analysis—A global perspective (7th Edition). New Jersey: Pearson.

14.

Hitt

M.A.

Keats

B.W.

& Demarie

S.M.

(1998). Navigating in the new competitive landscape: Building strategic flexibility and competitive advantage in the 21st century. Academy of Management Executive, 4(12), 22–42.

15.

Joshi

Farooquie

J.A.

& Chawla

(2016). Use of knowledge management for competitive advantage: The case study of Max Life Insurance. Global Business Review, 17(2), 450–469.

16.

Lee

Koh

Yen

& Tang

(2002). Perception gaps between IS academics and IS practitioners: An exploratory study. Information & Management, 40(1), 51–61.

17.

Liu

Song

& Cai

(2014). Knowledge management capability and firm performance: The mediating role of organizational agility (Paper 165). China: Pacific Asia Conference on Information Systems (PACIS) Proceedings.

18.

& Ramamurthy

(2011). Understanding the link between information technology capability and organizational agility: An empirical examination. MIS Quarterly, 35(4), 931–954.

19.

Mao

Liu

& Zhang

(2015a). How the effects of IT and knowledge capability on organizational agility are contingent on environmental uncertainty and information intensity. Information Development, 31(4), 358–382.

20.

Mao

Liu

& Zhang

(2015b). IT resource and competitive advantage: Role of knowledge management capability and resource commitment. 48th Hawaii International Conference on System Sciences, 5–8 January, Kauai, HI, pp. 3791–3800.

21.

Narver

J.C.

& Slater

S.F.

(1990). The effect of a market orientation on business profitability. Journal of Marketing, 4(54), 20–35.

22.

Nazir

& Pinsonneault

(2012). IT and firm agility: An electronic integration perspective. Journal of the Association for Information Systems (JAIS), 13(3), 150–171.

23.

Overby

Bharadwaj

& Sambamurthy

(2006). Enterprise agility and the enabling role of information technology. European Journal of Information Systems, 15(2), 120–131.

24.

Rai

Patnayakuni

& Seth

(2006). Firm performance impacts of digitally enabled supply chain integration capabilities. MIS Quarterly, 30(2), 225–246.

25.

Reserve Bank of India. (2013). Retrieved 26 December 2014 , from http://rbi.org.in/Scripts/PublicationsView.aspx?Id=15519.

26.

Roldán

L.J.

Leal-Rodríguez

A.L.

& Felipe

(2015). Information systems capabilities and organizational agility: Understanding the mediating role of absorptive capacity when influenced by a hierarchy culture. 2nd International Symposium on Partial Least Squares Path Modeling, 16–19 June, Seville, Spain.

27.

Tallon

P.P.

(2008). Inside the adaptive enterprise: An information technology capabilities perspective on business process agility. Journal of Information Technology Management, 9(1), 21–36.

28.

Tallon

P.P.

& Pinsonneault

(2011). Competing perspectives on the link between strategic information technology alignment and organizational agility: Insights from a mediation model. MIS Quarterly, 35(2), 463–486.

29.

Teece

Pisano

& Shuen

(1997). Dynamic capabilities and strategic management. Strategic Management Journal, 7(18), 509–533.

30.

Zahra

S.A.

& George

(2003). Absorptive capacity: A review, reconceptualization and extension. Academy of Management Review, 2(27), 185–203.