IT-ENABLED DYNAMIC CAPABILITY ON PERFORMANCE: AN EMPIRICAL STUDY OF BSC MODEL

Capacidade dinâmica de TI e desempenho: Um estudo empírico do modelo BSC

Capacidad dinámica de TI y desempeño: Estudio empírico del modelo BSC

ABSTRACT

Few studies have investigated the influence of "information capital," through IT-enabled dynamic capability, on corporate performance, particularly in economic turbulence. Our study investigates the causal relationship between performance perspectives of the balanced scorecard using partial least squares path modeling. Using data on 845 Brazilian companies, we conduct a quantitative empirical study of firms during an economic crisis and observe the following interesting results. Operational and analytical IT-enabled dynamic capability had positive effects on business process improvement and corporate performance. Results pertaining to mediation (endogenous variables) and moderation (control variables) clarify IT's role in and benefits for corporate performance.

KEYWORDS:
Corporate performance; information technology; dynamic capability; business process improvement; economic turbulence

RESUMO

Poucos estudos já investigaram a influência do "capital de informação", através da capacidade dinâmica de TI, sobre o desempenho corporativo, particularmente em uma turbulência econômica. Nosso estudo investiga a relação causal entre perspectivas de desempenho do balanced scorecard, utilizando modelagem de caminho pelos mínimos quadrados parciais. Utilizando dados sobre 845 empresas brasileiras, desenvolvemos um estudo empírico quantitativo com empresas durante uma crise econômica, e observamos os seguintes resultados de interesse: a capacidade dinâmica de TI operacional e analítica teve efeitos positivos na melhoria dos processos de negócios e no desempenho corporativo; os resultados relacionados à mediação (variáveis endógenas) e à moderação (variáveis de controle) esclarecem o papel da TI e seus benefícios para o desempenho corporativo.

PALAVRAS-CHAVE:
Desempenho corporativo; tecnologia da informação; capacidade dinâmica; melhoria de processos de negócios; turbulência econômica

RESUMEN

Pocos estudios han investigado la influencia del "capital de la información", a través de la capacidad dinámica posibilitada por TI, sobre desempeño corporativo, especialmente en turbulencia económica. Nuestro estudio investiga la relación causal entre perspectivas de desempeño del balanced scorecard usando el modelado path de mínimos cuadrados. Utilizando datos sobre 845 empresas brasileñas, conducimos un estudio empírico cuantitativo de firmas durante una crisis económica y observamos los siguientes resultados interesantes. La capacidad operativa y analítica posibilitada por TI tuvo efectos positivos en la mejora de procesos empresariales y desempeño corporativo. Los resultados referentes a mediación (variables endógenas) y moderación (variables de control) aclaran el papel de TI y beneficios en el desempeño corporativo.

PALABRAS CLAVE:
Desempeño corporativo; tecnología de la información; capacidad dinámica; mejora de procesos empresariales; turbulencia económica

INTRODUCTION

The resource-based view (RBV) and information technology (IT) business alignment approaches dominate IT literature (Schwarz, Kalika, Kefi, & Schwarz, 2010). RBV theory (Barney, 1991) has a static aspect, characterized by inability to obtain resources to enable sustainable competitive advantage (Barney & Clark, 2007).

Dynamic capability (DC) arises from the RBV on environmental prospects that have a high change rate, enables complementary resources (Ambrosini & Bowman, 2009; Helfat et al., 2009; Wade & Hulland, 2004), and reconfigures resources to meet requirements and changes in external and internal environments (Teece, 2009, 2014; Teece, Pisano, & Shuen, 1997).

Since 2015, Brazilian companies have faced political and economic crises. Economic turbulence was negative (−3.8%) in 2015 (Instituto Brasileiro de Geografia e Estatística [IBGE], 2016), and the forecast for 2016 shows no change. In 2015, 580,000 companies ceased activity (IBGE, 2016) and listed companies' revenues fell by 5.4% from 2014 to 2015 according to the Economatica System.

In 2015, IT spending increased to 7.6% of Brazilian companies' revenues (GVCia, 2015). Per Albertin and Albertin (2016), during the economic crisis, IT investments were made to achieve efficiency and efficacy and promote productivity.

Per Augier and Teece (2009), managers must build dynamic capabilities to sense and seize opportunities, transforming and reconfiguring as opportunities and competitive forces dictate. Raschke (2010) suggests that IT-enabled dynamic capability (ITDC) allows organizations to design and reconfigure processes to improve efficiency, enabling new business forms (Anderson, Banker, & Ravindran, 2006), and to influence corporate performance (Brynjolfsson & Hitt, 1996; Stoel & Muhanna, 2009) and react to changing business conditions and corporate strategies under economic pressure (Kim, Shin, Kim, & Lee, 2011; Weill, Subramani, & Broadbent, 2002).

Since the 1990s, most corporate performance studies have increased used the balanced scorecard (BSC) of Kaplan and Norton (1992). As of May 2016, their paper, "The balanced scorecard: Measures that drive performance" (Kaplan & Norton, 1992), has been cited more than 1,945 times, per the Web of Science's Scholarly and Scientific Research. Some studies (Bento, Bento, & White, 2013) estimate that 60-70% of large organizations in the private, public, and nonprofit sectors have adopted the BSC. Kaplan (2010) highlights future opportunities to statistically investigate causal relationships among BSC perspectives and objectives, helping firms understand and use dynamic causal models effectively to guide strategies and operations.

This study investigates the relationship between ITDC, business process improvement (BPI), customer performance (CP), and financial performance (FP), using the BSC model to help managers understand ITDC's role in corporate performance during economic turbulence. We validate analytical and operational ITDC as a first-order construct, analyzing the mediation of all endogenous variables and checking the influences of control variables (sector and firm size) on the relationships between all latent variables. Figure 1 depicts our ITDC model of corporate performance measured using the BSC method.

Figure 1
Proposed ITDC model of corporate performance

THEORY AND HYPOTHESIS DEVELOPMENT

This section describes dynamic capability, IT-enabled dynamic capability, operational IT, analytical IT, and the BSC model.

Dynamic capability

DC originates in RBV theory (Wang & Ahmed, 2007), encouraging efficient resource management to improve business processes (Grant, 2010) by choosing an activity set with a unique value combination (Kaplan & Norton, 2000). Per the RBV, sustained competitive advantage comes from unique, distinguishing resources that may be valuable, rare, non-replicable, and non-substitutable (Birkinshaw & Goddard, 2009; Wade & Hulland, 2004).

The heterogeneity and immobility of certain resources make it difficult to obtain DC in volatile environments and conditions (Wade & Hulland, 2004).

Several studies have highlighted firm-level DC development relating to challenging external and internal environments (Augier & Teece, 2009; Eisenhardt & Martin, 2000; Helfat & Peteraf, 2009; Helfat & Winter, 2011; Teece et al., 1997). This capability includes the ability to react dynamically through internal activities (Eisenhardt & Martin, 2000; Teece, 2009; Winter, 2003; Zollo & Winter, 2002) and through other means (McKelvie & Davidsson, 2009; Pavlou & Sawy, 2011; Ravichandran & Lertwongsatien, 2005; Wang & Ahmed, 2007; Zahra, Sapienza, & Davidson, 2006), allowing an organization to reconfigure resources and organizational capability (Ambrosini & Bowman, 2009; Helfat & Peteraf, 2009; Meirelles & Camargo, 2014; Teece, 2014).

We define DC as a firm's ability to develop or maintain competitive advantage using its essential competence and collective ability to innovate, coordinate, and reconfigure internal resource skills. Thereby, the firm improves business processes, meets market challenges, and influences corporate performance under economic turbulence.

IT-enabled dynamic capability

Most IT research defines IT resources and their relationship with competitive advantage, business strategy, and corporate performance (Davenport & Harris, 2007; Davenport, Harris, & Morison, 2010; Helfat & Winter, 2011; Kim et al., 2011; Wade & Hulland, 2004). Bharadwaj (2000) claims that IT enables DC, promoting BPI. IT integrates, builds, and reconfigures internal competencies in value chain activities (Davern & Wilkin, 2010;Kim et al., 2011; Raschke, 2010). Its benefits lead to competitive advantage (Bhatt & Grover, 2005; Schwarz et al., 2010; Stoel & Muhanna, 2009).

IT use leads to operational (Bhatt & Grover, 2005; Melville, Kraemer, & Gurbaxani, 2004; Schwarz et al., 2010) and analytical benefits (Arnott & Pervan, 2014; Davenport & Harris, 2007; Davenport et al., 2010; Nudurupati, Bititci, Kumar, & Chan, 2011; Ramdani, 2012; Wade & Hulland, 2004), and influences BPI and corporate performance (Kohli, Devaraj, & Ow, 2012; Melville, Kraemer, & Gurbaxani, 2004; Nudurupati, Bititci, Kumar, & Chan, 2011).

We define ITDC as a firm's ability to change (improve, adapt, or reconfigure) a business process better than competitors by integrating activities, reducing cost, and capitalizing on business intelligence/learning (Schwarz et al., 2010).

Operational IT

Operational IT is categorized into technological infrastructure and transactional applications (Chuang, 2004; Maçada, Beltrame, Dolci, & Becker, 2012; Sobol & Klein, 2009) and automates activities (Otim, Dow, Grover, & Wong, 2012; Shang & Seddon, 2002).

Technological infrastructure enables connections between companies, information sharing, and data structuring, and deploys IT business value across an enterprise (Bhatt & Grover, 2005; Weill et al., 2002). Communication enabled by IT infrastructure among functions generates DC, which provides integration, flexibility, standardization, and business agility, and reduces IT cost.

IT transactional applications automate operational tasks and generate information, which ellucidates the activities through which a firm makes and delivers business value (O'Brien & Marakas, 2007; Ravichandran & Lertwongsatien, 2005; Shang & Seddon, 2002). DC enabled by IT business value uses technology to process and automate basic repetitive transactions and create information availability for business management (Raschke, 2010). IT in material requirement planning (MRP) designs, monitors, and controls floor shop routines, generating agility, flexibility, simulation power, and control, thereby recommending "what, when, and how" for production and "what, when, and where" for supplies and raw materials purchasing.

Analytical IT

Analytical IT enables tactical and strategic decision-making. Per Davenport and Harris (2007) and Davenport et al. (2010), management uses IT to provide analyses, data, and systemic knowledge for organizational processes and decision-making.

Tactical IT capability improves information quality, defined by accessibility, accuracy, and flexibility (Chuang, 2004; Maçada et al., 2012; Sobol & Klein, 2009). IT provides and uses information for the planning, execution, and control of activities (Arnott & Pervan, 2005, 2014; Singh, Watson, & Watson, 2002). IT provides information to run a firm and achieve objectives and targets (Arnott & Pervan, 2014), enabling analysis and measurement of time variations, and redirecting actions that contribute to operational productivity (Kaplan & Norton, 2008).

Strategic IT capability contributes to competitive advantage development (Melville et al., 2004) and helps increase market share. Strategic IT applications relate to a firm's core business, enabling a firm to competitively differentiate by aligning to customer needs (Ramdani, 2012).

Organizations react to external environments and create performance management models to understand and adapt (Kaplan & Norton, 1996). The relationship between external factor stimuli and strategy development is relevant to acquisition and creates IT capabilities (Kaplan & Norton, 2000). The performance management model of BSC proposed by Kaplan and Norton (1996, 2000, 2004, 2008) shows that IT enables organizations to respond to external and internal challenges through operational business processes, such as innovation, operation, and post-sale. Melville et al. (2004) find that IT business value is created when organizations understand how to develop IT to support business processes and achieve corporate performance that responds to environmental challenges. IT can be explained by how effectively a firm uses information systems (IS) to support and enhance core competencies (O'Brien & Marakas, 2007; Ravichandran & Lertwongsatien, 2005; Sen, Bingol, & Vayvay, 2017) to enable operational dynamic capability in the BSC model.

H1: Operational IT-enabled dynamic capability is positively associated with business process improvement.

External factors' impact on competitive advantage and firm performance depends on how firms develop dynamic capabilities for using management resources (Augier & Teece, 2009). Through information systems, IT enables managers to use performance management models (O'Brien & Marakas, 2007) such as the BSC systems to develop dynamic capabilities for responding to external changes (Sen et al., 2017). Per Kaplan and Norton (2000, 2008), strategic maps, dashboards, cockpits, and performance reports provide information to develop knowledge and provide intelligence to managers for decision-making. Organizational IT resources transform and create ways to run business processes (Henderson & Venkatraman, 1999) by developing new products and services or making efficiency gains (Maçada et al., 2012). IT is used in strategic processes to generate DC, which diagnoses, plans, formulates, and implements business strategies with flexibility, thus adapting, transforming, and achieving business process improvement in the BSC model (Arnott & Pervan, 2014; Ramakrishnan, Jones, & Sidorova, 2012).

H2: Analytical IT-enabled dynamic capability is positively associated with business process improvement.

We describe ITDC's principal contributions (Schwarz et al., 2010) and BSC (Kaplan & Norton, 1996), presenting IT as a resource that enables dynamic capabilities in business process improvement to achieve performance. We focus on the influence of "information capital provided by the BSC" because analytical and operational ITDC runs across all activities in the value chain. We concentrate on technology benefits offered and delivered by IT (Henderson & Venkatraman, 1999; Kohli & Grover, 2008; Otim et al., 2012; Poelmans, Reijers, & Recker, 2013; Tallon, 2008) to enable DC to achieve BPI (Helfat & Winter, 2011; Kim et al., 2011; Melville et al., 2004; Raschke, 2010; Schwarz et al., 2010); to respond to market challenges and develop internal competencies to innovate, coordinate, and reconfigure processes (Ambrosini & Bowman, 2009; Teece et al., 1997); and to influence multiple corporate performance perspectives (Devaraj & Kohli, 2000, 2003; Henderson, Kobelsky, Richardson, & Smith, 2010;Kohli, Devaraj, & Ow, 2012; Santos, Zheng, Mookerjee, & Chen, 2012; Teece, 2009).

Balanced scorecard model

The BSC is an integrated framework to track financial and nonfinancial indicators, helping an organization align its initiatives with strategy and achieve corporate performance (Bento et al., 2013; Brito & Brito, 2012; Kaplan, 2010; Sen et al., 2017; Yoshikuni & Albertin, 2014). Corporate performance is measured from the multidimensional prism perspective (Najmi, Etebari, & Emami, 2012), through financial and nonfinancial measures (Ouakouak & Ouedraogo, 2013; Santos-Vijande, López-Sánchez, & Trespalacios, 2012) of causal relations, within and between strategy objectives that culminate in FP.

Senior managers have focused on understanding how FP is created by identifying, measuring, and managing long-term shareholder value drivers (Brito & Brito, 2012; Bryant, Jones, & Widener, 2004; Jordão & Novas, 2013;Sen et al., 2017). Increased profitability results from revenue growth and spending reduction (cost, expenses, and asset use) (Atkinson, Kaplan, Matsumura, & Young, 2011). Revenue growth is necessary to satisfy clients; managerial actions and decisions can increase productivity. Profits could increase indirectly through production, increasing market share and generating higher revenues and lower operational spending (Kaplan & Norton, 2008; Papke-Shields, Malhotra, & Grover, 2006). To achieve BPI, an organization must develop intangible asset capability in information capital, human capital, and organization capital (Kaplan & Norton, 2004).

The BSC model describes causal relationships between performance measures by considering strategic business goals from four perspectives: financial, customer, business processes, and learning and growth. We draw on Kaplan and Norton (2008) to describe the BSC model's perspectives.

FP includes the primary objective of sustained shareholder value creation and sub-objectives of revenue growth, productivity, and risk management (Kaplan, 2010). FP relates to the ability to create long-term value for shareholders (Atkinson et al., 2011; Kaplan & Norton, 2008; Kohli et al., 2012; Ouakouak & Ouedraogo, 2013; Santos-Vijande et al., 2012; Yoshikuni & Albertin, 2014).

The conditions that create value for clients impact CP (Kaplan & Norton, 1996; Santos-Vijande et al., 2012). CP is measured by objectives relating to (a) attributes of goods and services, such as price, quality, time, availability, functionality, claims rate, and delivery reliability; (b) relationships with indicators, such as customer retention and partner satisfaction; and (c) image or brand (Kaplan & Norton, 2008; Sila & Ebrahimpour, 2005). CP is the organization's ability to fulfill attributes requested by clients, execute the business value chain's internal activities (Kaplan & Norton, 2008), and create customer satisfaction, loyalty, and retention (Kaplan, 2010).

Per Melville et al. (2004), a business process is the ordering of work activities across time and space, with a beginning, end, and identified inputs and outputs. A business process comprises the value chain's internal activities, leading to financial productivity, success, and satisfied customers (Jordão & Novas, 2013; Sen et al., 2017). The generic view of the internal business process perspective encompasses the entire internal value chain, decomposed into three processes: innovation, operations, and post-sales (Kaplan & Norton, 2008). Innovation activities focus on identified markets and customer needs; products and services meet those needs. Kaplan and Norton (2008) conclude with post-sales services for operational activities in which the value proposed (products and services) is crafted and delivered to meet customer needs; if needs are unmet, corrective actions are applied to satisfy customers.

Achieving BPI requires learning and growth to develop the organization; employee capabilities and skills, constituting intangible assets; information systems capabilities (IT); and an enabling corporate climate (Kaplan & Norton, 1996). An organization must provide employees with opportunities for growth and learning, creating a good work place (Jordão & Novas, 2013). We concentrate "information capital" on ITDC.

H3: Business process improvement is positively associated with customer performance.

H4: Business process improvement is positively associated with financial performance.

H5: Customer performance is positively associated with financial performance.

RESEARCH METHODOLOGY

In this section, sample, pilot test, data treatment, and statistical techniques are described.

Sample

The target population comprised organizations from several sectors, following Ouakouak and Ouedraogo (2013). We used research methods for business (Sekaran, 2000) and the recommendation of Tallon, Kraemer, and Gurbaxani (2000) to measure IT business value through executives' perceptions. This method has been used in IT studies (e.g., Kim et al., 2011; Papke-Shields et al., 2006; Powell & Dent-Micallef, 1997), and the key informants were professionals with decision-making ability, including chief executive officers, vice presidents, directors, managers, supervisors, coordinators, and business executives involved in management.

We designed a firm-level sample, and respondents were employed professionals taking classes to fulfill Master of Business Administration (MBA) degrees (D'Arcy & Devaraj, 2012) at a university in southwestern Brazil. The high-quality sample consisted of executives with eight to 30 years of experience in executive management.

To evaluate organizations' aspects accurately, we provided informants a 20-day period to validate survey information with other executives. During this period, respondents could call, email, or consult with us regarding the questionnaire, thereby increasing answer quality. Incomplete questionnaires were invalidated.

The questionnaire contained two sections: demographic and specific information. The demographic section sought data characterizing the firm and validating participation, including a direct search for secondary data. The specific information section assessed perception of the research constructs. Demographic information included control variables of sector and firm size (number of employees), incorporated as a moderating variable. The questionnaire's interval scale contained five choice categories for each manifest variable, evaluated along a Likert scale: (1) strongly disagree, (2) disagree, (3) neither agree nor disagree, (4) agree, and (5) strongly agree.

Pilot test

Manifest variables were obtained from the literature, as was guidance concerning variables' content validity, number of categories for items, and other issues (Hair, Black, Babin, Andersen, & Tatham, 2009; Sekaran, 2000).

Experts (researchers and professors with more than 12 years of experience) in business strategy and technology pretested the questionnaire. The results were positive and confirmed the questionnaire's quality. The questionnaire was applied to a sample of 40 organizations, fulfilling the minimum pretesting recommendation of 15 respondents (Malhotra, 2006). Respondents rated format, ease of understanding content, and other aspects, most of which were acceptable. We incorporated evaluators' suggestions.

Data treatment

The data treatment phase included a check for missing values. Microsoft Excel 2010 stored records of 845 valid cases; 247 questionnaires had missing data and were excluded from the research database.

We tested for nonresponse bias, comparing assessments of the pilot test respondents (early) and MBA respondents (late) for all variables. All t-test comparisons between the means of early and late respondents showed no significant differences (D'Arcy & Devaraj, 2012). We tested the model with a dummy variable indicating whether a respondent was a pilot test case (40 cases) or MBA. The path from the dummy variable to firm performance was not significant (CP β = 0.022, n.s. and financial performance β = 0.001, n.s.) (Nitzl & Hirsch, 2013).

The sample size accorded with partial least squares path modeling (PLS-PM) literature (Henseler, Ringle, & Sinkovics, 2009; Sosik, Kahai, & Piovoso, 2009; Urbach & Ahlemann, 2010), which considers the minimum sample size to be 10 times the number of structural paths arriving at a particular reflective construct. The minimum sample size was 20. For a more rigorous minimum sample size, we evaluated statistical power using the G*Power 3.1.9.2 software (Faul, Erdfelder, Lang, & Buchner, 2007). We observed two parameters: test power (power = 1 - β_{error prob. II}) and the effect size (ƒ²). Cohen (1988) and Hair, Hult, Ringle, and Sarstedt (2013) recommend a power of 0.80 and median ƒ² of 0.15. They note that CP and FP constructs should have two predictors.

The minimum sample size suggested by the software was 43, but to increase model consistency, doubling or tripling this value is beneficial (Ringle, Silva, & Bido, 2014). We used a sample of 845, complying with the empirical rule.

Statistical technique

PLS-PM calculates the maximum explained variance of all manifest variables, evaluates the reliability and validity of latent constructs, and estimates relationships between them (Hair et al., 2013). PLS-PM addresses situations observed in IT research (Ringle, Sarstedt, & Straub, 2012): the absence of symmetric variable distributions (or those with little "consolidation") measured by a still-nascent theory, large data amounts, or limited data amounts. PLS-PM can demonstrate the model's applicability and robustness (Ringle et al., 2014).

The PLS-PM technique does not adhere to the normal distribution of data, a set number of data observations and independence, or metric uniformity of the variable (Chin, 1998; Hair et al., 2013). Despite this, we proceeded with caution and followed the recommendations of Chin (1998) and Hair et al. (2013) while preparing missing values and checking data distribution and multicollinearity. We used the SmartPLS 2.0 M3 program for PLS-PM (Ringle, Wende, & Will, 2005).

The sample was defined by sector and firm size (number of employees), following Ray, Wu, and Konana (2009). Table 1 summarizes the sample profile and respondents.

Table 1
Demographic research

Sector		Firm size
Type	%	(Numbers of employees)	%
Agribusiness	4	Up to 250	32
Commerce	10	251 to 500	15
Finance	8	501 to 3000	12
Manufacturing	38	3001 and more	41
Services	40

Measurement model

We observe convergent validities by measuring the model's average variance extracted (AVE). Per the Fornell-Larcker criterion (Henseler et al., 2009), AVE should exceed 0.50 (Hair et al., 2013; Ringle et al., 2014). Table 2 presents results.

Table 2
Pearson correlation and descriptive statistics of latent variables

Latent variables	1	2	3	4	5
1 - Operational ITDC	0.79
2 - Analytical ITDC	0.78	0.80
3 - Business process improvement	0.50	0.51	0.73
4 - Customer performance	0.31	0.32	0.62	0.74
5 - Financial performance	0.34	0.37	0.48	0.49	0.85
AVE	0.62	0.65	0.53	0.55	0.72
Composite reliability	0.87	0.88	0.77	0.78	0.88
Means	3.12	3.17	3.57	3.86	3.43
Standard deviation	0.92	0.94	0.81	0.62	0.69
Variance coefficient	0.29	0.29	0.24	0.16	0.19
Skewness |Sk|	0.28	0.38	0.32	0.73	0.34
Kurtosis |Ku|	0.40	0.31	0.04	1.16	0.15

Composite reliability (CR) is the most appropriate measure for PLS-PM because it prioritizes variables by reliability (Ringle et al., 2014). We considered CR values between 0.70 and 0.90 satisfactory (Hair et al., 2013). Table 2 shows that CR values are adequate. We evaluated the variables' normality by coefficients of skewness (Sk) and kurtosis (Ku), and univariate and multivariate variables showed no severe violation of the normal distribution assumption (|Sk| < 3 and |Ku| < 10) (Marôco, 2010).

To check the model's discriminant validity, we analyzed cross loads. Table 3 shows that indicators have higher loads for some constructs and lower loads for others, indicating discriminant validity, per Ringle et al. (2014) and Urbach and Ahlemann (2010).

Table 3
Cross loads to assess discriminant validity

First latent variables	Items	1	2	3	4	5
1 - Analytical ITDC	AITDC_1	0.761	0.600	0.378	0.272	0.253
	AITDC_2	0.781	0.607	0.377	0.244	0.275
	AITDC_3	0.814	0.652	0.411	0.240	0.272
	AITDC_4	0.799	0.603	0.424	0.234	0.274
2 - Operational ITDC	OITDC_1	0.604	0.793	0.376	0.284	0.281
	OITDC_2	0.619	0.826	0.391	0.245	0.269
	OITDC_3	0.641	0.798	0.432	0.207	0.290
	OITDC_4	0.639	0.800	0.427	0.298	0.350
3 - Business process improvement	BPI_1	0.389	0.391	0.724	0.399	0.415
	BPI_2	0.336	0.318	0.780	0.603	0.360
	BPI_3	0.387	0.415	0.676	0.332	0.269
4 - Customer performance	CP_1	0.124	0.128	0.449	0.777	0.403
	CP_2	0.214	0.237	0.494	0.806	0.387
	CP_3	0.380	0.367	0.442	0.626	0.279
5 - Financial performance	FP_1	0.223	0.242	0.336	0.361	0.830
	FP_2	0.328	0.344	0.440	0.427	0.838
	FP_3	0.302	0.346	0.437	0.436	0.873

Table 3 shows results generated for cross loads. The indicators have significant loads (p-value < 0.001) in their constructs, confirming convergent validity. Discriminant validity is revealed when a load has the highest indicators in its latent variables. We estimated significance using the bootstrap method with 845 cases and 2,000 repetitions.

Structural model

To check for multicollinearity, we analyzed variance inflation factors (VIFs) for the FP and CP. The highest VIF is 1.897 for the "Operational ITDC" construct; there is no evidence of multicollinearity among the indicators per the limit recommended (< 5) by Marôco (2010).

We verified the effects of latent variables and control variables of sector and firm size (number of employees) on relationships between exogenous and endogenous variables (Table 4).

Table 4
Effects of latent and control variables in relationships between exogenous and endogenous variables

Case	Relationship between variables	Path coefficient	Standard error	t-	p-	R2 (%)
				value	value
1	Operational ITDC -> BPI	0.278	0.048	5.797	0.000	28.7
	Analytical ITDC -> BPI	0.290	0.047	6.197	0.000
	BPI -> CP	0.623	0.024	25.571	0.000	38.9
	BPI -> FP	0.294	0.039	7.541	0.000	28.8
	CP -> FP	0.302	0.042	7.239	0.000
2	Operational ITDC -> BPI	0.313	0.047	6.616	0.000	29.5
	Operational ITDC × Size -> BPI	0.083	0.049	1.702	0.089
	Analytical ITDC -> BPI	0.286	0.048	5.915	0.000
	Analytical ITDC × Size -> BPI	0.044	0.053	0.823	0.411
	Size -> BPI	−0.073	0.031	2.392	0.017
	CP × Size -> BPI	−0.001	0.046	0.026	0.979
	BPI -> CP	0.634	0.024	26.247	0.000	40.3
	BPI × Size -> CP	0.069	0.059	1.176	0.240
	Size -> CP	−0.038	0.028	1.380	0.168
	BPI × Size -> FP	0.007	0.045	0.163	0.870	29.3
	Size -> FP	0.057	0.029	1.980	0.048
	CP -> FP	0.300	0.042	7.095	0.000
	BPI -> FP	0.289	0.041	7.099	0.000
3	Operational ITDC -> BPI	0.275	0.046	6.032	0.000	28.2
	Operational ITDC × Sector -> BPI	0.061	0.077	0.795	0.426
	Analytical ITDC -> BPI	0.273	0.047	5.844	0.000
	Analytical ITDC × Sector -> BPI	−0.020	0.054	0.371	0.711
	Sector -> BPI	0.016	0.038	0.421	0.674
	Sector -> CP	−0.062	0.038	1.639	0.101	41.1
	BPI -> CP	0.629	0.025	25.030	0.000
	BPI × Sector -> CP	−0.080	0.031	2.590	0.010
	CP -> FP	0.285	0.043	6.578	0.000	29.5
	CP × Sector -> FP	−0.081	0.036	2.230	0.026
	BPI -> FP	0.293	0.042	7.058	0.000
	BPI × Sector -> FP	0.009	0.046	0.190	0.849
	Sector -> FP	0.008	0.050	0.158	0.875

Case 1 shows that relationships between all latent variables are statistically significant (p-value < 0.001). Case 2 demonstrates that all effects of firm size moderation on the relationship between exogenous and endogenous variables are not statistically significant (p-value < 0.05); firm size has a small direct influence on BPI (−0.073) and FP (0.057). Case 3 demonstrates that the effect of sector moderation on the relationship between ITDC (operational and analytical) and BPI is not statistically significant (p-value < 0.05). Sector moderation has a small effect on relationships between BPI and CP and between CP and FP, which were statistically significant (p-value < 0.05).

We checked ITDC's direct effect on all endogenous variables, finding that the analytical ITDC influences FP (p-value < 0.05; Table 5).

Table 5
Influence of IT-enabled dynamic capability on endogenous variables

Relationship between variables	Path coefficient	Standard error	t-value	p-Value	R2 (%)
Operational ITDC -> BPI	0.278	0.049	5.725	0.000	28.68
Analytical ITDC -> BPI	0.290	0.048	6.054	0.000
Operational ITDC -> CP	−0.016	0.045	0.355	0.723	38.87
Analytical ITDC -> CP	0.021	0.048	0.442	0.659
BPI -> CP	0.621	0.031	20.033	0.000
Analytical ITDC -> FP	0.154	0.050	3.090	0.002	31.06
Operational ITDC -> FP	0.022	0.050	0.444	0.657
BPI -> FP	0.207	0.044	4.654	0.000
CP -> FP	0.300	0.041	7.256	0.000

Coefficients of determination (R² outcome) measure the variance of endogenous variables, indicating the structural model's quality. All coefficients of determination demonstrate large effects, as shown in Tables 4 and 5. Cohen (1988) suggests the following classification for social and behavioral sciences: R² = 2% is a small effect, R² = 13% is a medium effect, and R² = 26% is a large effect.

Direct and indirect effects of IT-enabled dynamic capability and business process improvement

We checked all mediation analyses to identify direct and indirect effects of ITDC and BPI. We estimated direct effects without the potential mediator variables BPI and CP. Table 6 shows the significance analysis of path coefficients without the mediator (Ringle et al., 2012), using the bootstrapping procedure (845 cases per subsample, 5,000 subsamples, and no sign changes). Both relationships are statistically significant (p-value < 0.001).

Table 6
Path coefficient results estimated without the potential mediator

Relationship between variables	Path coefficient	t-value	p-value
Operational ITDC -> CP	0.381	14.082	0.000
Operational ITDC -> FP	0.343	11.082	0.000
Analytical ITDC -> CP	0.378	13.410	0.000
Analytical ITDC -> FP	0.377	12.916	0.000
BPI -> FP	0.490	18.174	0.000

We concluded that indirect effects should exist (Zhao, Lynch, & Chen, 2010). We included the mediator variable in the PLS-PM and checked the VAF (Table 7).

Table 7
Results of variance accounted for (VAF)

Relationship between latent variables	Indirect effect	Standard error	t-value	p-value	Direct effect	Total effect	VAF (%)
Operational ITDC -> BPI -> CP	0.173	0.007	2.432	0.015	−0.016	0.157	100
Operational ITDC -> BPI -> FP	0.057	0.027	0.822	0.411	0.022	0.079	72
Operational ITDC -> CP -> FP	−0.048	0.004	6.282	0.000	0.022	−0.026	0
Analytical ITDC -> BPI -> CP	0.180	0.002	9.577	0.000	0.021	0.201	90
Analytical ITDC -> BPI -> FP	0.060	0.023	6.789	0.000	0.154	0.214	28
Analytical ITDC -> CP -> FP	0.063	0.019	8.171	0.000	0.154	0.217	29
BPI -> CP -> FP	0.186	0.060	3.434	0.001	0.206	0.392	47

Due to significant indirect effects, we analyzed the VAF value as it determines the size of the ratio of indirect effect to total effect (Hair et al., 2013). Per the authors, when the VAF is less than 20%, almost no mediation takes place, while a VAF exceeding 80% results in large outcomes, and we can assume full mediation. When the VAF lies between 20 and 80%, we see partial mediation. Our results indicate full mediation by BPI in relationships between operational ITDC and CP, and between analytical ITDC and CP. No mediation by BPI and CP exists in the relationship between operational ITDC and FP. Partial mediation by BPI and FP exists in the relationship between analytical ITDC and FP. Partial mediation by CP exists in the relationship between BPI and FP. Only the mediation by BPI in the relationship between operational ITDC and FP was not significant (p-value < 0.05).

Effect size (f²) and predictive relevance (Q²)

We evaluated the quality of model adjustment using two other indicators' values: effect size (f²) or Cohen's indicator, and relevance or predictive validity (Q²) or Stone-Geisser indicator.

f² was obtained by including and excluding model constructs one by one. We evaluated the usefulness of each construct for the adjustment model. We considered values of 0.02, 0.15, and 0.35 small, medium, and large, respectively (Hair et al., 2013). We evaluated f² in terms of the ratio between the parts explained and not explained (f² = R²/ (1 - R²). Table 8 shows the values of f² and BPI when the excluded constructs had large effects on CP.

Table 8
Results of effect size (f)²

Effect of excluded exogenous variable on endogenous variable		R2 Included (%)	R2 excluded (%)	f2 effects size	Explanation
1	Operational ITDC -> BPI	28.7	25.6	0.04	small
	Analytical ITDC -> BPI	28.7	25.3	0.05	small
2	Operational ITDC -> CP	38.9	38.9	0.00	small
	Analytical ITDC -> CP	38.9	39.1	0.00	null
	BPI -> CP	38.9	12.8	0.43	large
3	Operational ITDC -> FP	31.1	31.1	0.00	null
	Analytical ITDC -> FP	31.1	30.2	0.01	null
	BPI -> FP	31.1	28.2	0.04	small
	CP -> FP	31.1	25.6	0.08	small

Q² evaluates the model's validity or how well the model measures what it was designed to measure (model prediction quality or accuracy of adjusted model) (Chin, 1998). The values should be greater than zero (Ringle et al., 2014). All Q² values are higher than zero (Table 9), supporting the model's predictive relevance regarding endogenous latent variables. We calculated the predictive relevance of exogenous variables on endogenous variables. In Cases 1 and 3, all exogenous variables show small or null predictive relevance for endogenous variables. The Q² for CP (Case 2) shows that BPI has medium predictive relevance (Table 9).

Table 9
Results of predictive relevance (Q²) and effect size (q²)

Effect excluded exogenous variable on endogenous variable		Q2 included	Q2 excluded	q2 effects size	Explanation
1	Operational ITDC -> BPI	0.150	0.136	0.02	small
	Analytical ITDC -> BPI	0.150	0.134	0.02	small
2	Operational ITDC -> CP	0.211	0.208	0.00	null
	Analytical ITDC -> CP	0.211	0.208	0.00	null
	BPI -> CP	0.211	0.065	0.18	medium
3	Operational ITDC -> FP	0.218	0.218	0.00	null
	Analytical ITDC -> FP	0.218	0.212	0.01	null
	BPI -> FP	0.218	0.198	0.03	small
	CP -> FP	0.218	0.178	0.05	small

Analyzing heterogeneous data structures by moderating control variables

We checked differences between sector moderation, split the heterogeneous dataset, and compared sector groups to understand and further differentiate findings (Table 10).

Table 10
Effect of sector groups

Sector	Relationship between variables	Path coefficient	Standard error	t-value	p-value	R2 (%)
Agribusiness	Operational ITDC -> BPI	0.225	0.250	0.898	0.369	26.5
	Analytical ITDC -> BPI	0.322	0.279	1.156	0.248
	Operational ITDC -> CP	0.460	0.212	2.172	0.030	43.2
	Analytical ITDC -> CP	−0.245	0.301	0.813	0.416
	BPI -> CP	0.489	0.180	2.716	0.007
	Operational ITDC -> FP	−0.236	0.321	0.735	0.462	25.4
	Analytical ITDC -> FP	−0.011	0.286	0.039	0.969
	BPI -> FP	0.333	0.261	1.273	0.203
	CP -> FP	0.335	0.252	1.329	0.184
Manufacturing	Operational ITDC -> BPI	0.298	0.079	3.768	0.000	27.6
	Analytical ITDC -> BPI	0.259	0.082	3.181	0.001
	Operational ITDC -> CP	0.075	0.064	1.172	0.241	46.8
	Analytical ITDC -> CP	−0.052	0.072	0.713	0.476
	BPI -> CP	0.671	0.049	13.706	0.000
	Operational ITDC -> FP	0.048	0.086	0.561	0.575	36.7
	Analytical ITDC -> FP	0.075	0.083	0.898	0.369
	BPI -> FP	0.175	0.075	2.331	0.020
	CP -> FP	0.410	0.068	6.061	0.000
Commerce	Operational ITDC -> BPI	0.423	0.143	2.959	0.003	28.7
	Analytical ITDC -> BPI	0.154	0.136	1.131	0.258
	Operational ITDC -> CP	−0.193	0.152	1.272	0.203	26.7
	Analytical ITDC -> CP	−0.008	0.183	0.045	0.964
	BPI -> CP	0.593	0.105	5.641	0.000
	Operational ITDC -> FP	0.068	0.180	0.379	0.705	24.1
	Analytical ITDC -> FP	0.263	0.155	1.699	0.089
	BPI -> FP	0.160	0.147	1.089	0.276
	CP -> FP	0.172	0.141	1.225	0.221
Services	Operational ITDC -> BPI	0.160	0.074	2.167	0.030	28.7
	Analytical ITDC -> BPI	0.389	0.069	5.677	0.000
	Operational ITDC -> CP	−0.086	0.069	1.260	0.208	26.7
	Analytical ITDC -> CP	0.119	0.072	1.650	0.099
	BPI -> CP	0.586	0.049	11.953	0.000
	Operational ITDC -> FP	−0.002	0.076	0.020	0.984	24.1
	Analytical ITDC -> FP	0.229	0.075	3.052	0.002
	BPI -> FP	0.223	0.069	3.227	0.001
	CP -> FP	0.251	0.065	3.861	0.000
Finance	Operational ITDC -> BPI	0.498	0.138	3.604	0.000	41.5
	Analytical ITDC -> BPI	0.166	0.155	1.074	0.283
	Operational ITDC -> CP	0.036	0.208	0.175	0.861	45.4
	Analytical ITDC -> CP	0.011	0.177	0.061	0.951
	BPI -> CP	0.643	0.117	5.499	0.000
	Operational ITDC -> FP	−0.062	0.205	0.303	0.762	29.6
	Analytical ITDC -> FP	0.336	0.178	1.890	0.059
	BPI -> FP	0.128	0.193	0.663	0.507
	CP -> FP	0.246	0.188	1.308	0.191

Agribusiness data show significant effects only for relationships between operational ITDC and CP, and between BPI and CP (p-value < 0.05). The manufacturing data exert no significant effects for relationships between operational ITDC and CP, and between analytical ITDC and CP (p-value < 0.05). The commerce data show no significant effects for relationships between operational ITDC and BPI, and between BPI and CPE (p-value < 0.010). The services data indicate no significant effects for relationships between operational ITDC and BPI, between analytical ITDC and BPI, and between operational ITDC and FP (p-value < 0.05). The finance data exert significant effects only for relationships between operational ITDC and BPI, and between BPI and CP (p-value < 0.05).

We compared all data groups to verify differences in path coefficients by PLS multigroup analysis (PLS-MGA) (Hair et al., 2013). We did not find statistically significant (p-value < 0.05) differences in the path coefficients between the datasets of finance and manufacturing, finance and commerce, and finance and agribusiness. Significant (p-value < 0.10) differences exist in path coefficients between the datasets of manufacturing and services, manufacturing and agribusiness, manufacturing and commerce, services and agribusiness, services and commerce, and services and finance. We verified significant (p-value < 0.05) differences in path coefficients between the datasets of commerce and agribusiness.

DISCUSSION AND CONCLUSION

This study provides empirical evidence regarding the effect of ITDC on corporate performance in economic turbulence. The results of a conceptual BSC model (Kaplan, 2010) evidence linkages between ITDC, BPI, CP, and FP in economic turbulence.

Large firm size decreases BPI and small companies perform better at innovation, operation, and post-sales activities. Large firm size increases FP, and these companies earn better revenue by creating customer satisfaction, loyalty, and retention. The results of our control variables (sector and firm size) testing show that sector moderates the relationships between BPI and CP, and between CP and FP.

We conduct a detailed analysis of heterogeneous data by comparing data groups' results and find interesting difference effects in relationships between operational ITDC and CP. ITDC has strong effects on understanding customer needs, making timely delivery (products and services), and retaining clients. Comparing representative datasets for manufacturing (339 cases) and services (317 cases) shows a strong difference effect (0.160) between CP and FP (i.e., the manufacturing sector has a higher path coefficient than the services sector).

Hypotheses H1 (operational ITDC -> BPI) and H2 (analytical ITDC -> BPI) show strong significance effects (p-value < 0.05) of path coefficients, confirming that IT creates many operational and analytical benefits (Melville et al., 2004; Tallon, 2008), enabling DC to build and reconfigure internal competencies in value chain activities and achieve corporate performance. Operational and analytical ITDC have the same effect on BPI for innovation, operations, and post-sales (i.e., there are no differences in path coefficients).

We examine the process in which ITDC positively affects CP through increased proficiency by changing BPI. Relationships between operational and analytical ITDC and CP are fully mediated by BPI, consistent with other studies (Kaplan, 2010; Kaplan & Norton, 2008; Melville et al., 2004; Tallon, 2008). The results highlight dynamic process management capabilities' importance in enhancing firm-level performance (Kim et al., 2011).

We examine the possible direct relationship between operational and analytical ITDC and FP. Operational ITDC -> FP shows no statistically significant effects. Analytical ITDC -> FP demonstrates significant effects. We identify partial mediation by BPI and CP for analytical ITDC (Q² > 0). We check the direct effects in these relationships without the mediator, and all results are statistically significant (p-value < 0.05). Although significant causality between the variables of ITDC and FP might exist, achieving consistent empirical findings is difficult when modeling based on the black box approach (Kim et al., 2011). Some studies using the black box approach cannot explain how IT investment and organizational performance are associated (Brynjolfsson, 1993; Brynjolfsson & Hitt, 1996).

Hypotheses H3 (BPI -> CP) and H4 (BPI -> FP) confirm the causal relationship conceptualized in the BSC framework (Kaplan, 2010). BPI has a stronger effect (0.623) on CP than on FP (0.294). We conclude that innovation, operations, and post-sales value chain activities focus more on generating value to satisfy customers than on reducing spending. We moderate BPI to confirm that IT enables DC to create benefits and improve business process performance (Schwarz et al., 2010). This is accomplished by understanding, analyzing, manufacturing, delivering (products and services), and improving client relationships, resulting in sustainable competitive advantage (Kaplan & Norton, 2008; Ramdani, 2012) in the uncertain Brazilian environment.

Hypothesis H5 (CP -> FP) confirms the causal relationship of the BSC framework (Kaplan & Norton, 1996). The empirical study confirms that delivery of market attributes promotes customer satisfaction, leading to client retention and added value to customers. It finds higher variation in FP (R² = 28.8%) and that the effects of CP and BPI on FP (0.302 and 0.294, respectively) are not different (p-value < 0.05).

We identify operational and analytical ITDC's benefits for corporate performance using survey-based research supported by empirical evidence. Our sample comprises Brazilian firms under economic turbulence (GDP = −3.8%); most previous studies use data from stable economies in North America and Europe. Few studies pertaining to Brazil explicitly model and empirically test the effects of ITDC on corporate performance using the BSC model in economic crisis.

LIMITATIONS AND FUTURE OPPORTUNITIES

This study's weakness is its latent variables, which require assumptions regarding measurement and may not reflect the realities of executive ITDC perceptions, business process improvement, customer performance, or financial performance.

Future research opportunities include qualitative research, specifically how and why control variables such as sector, firm size, and environmental challenge influence the ITDC regarding corporate performance. The study could be repeated using the following constructs: (1) impacts of IT resource adoption on corporate performance and (2) other effects of ITDC use on corporate performance. Researchers may compare structural effects to identify different influences and uses of IT adoption.

AUTHORS' NOTE

The PLS-MGA data group comparisons' questionnaire, tables of cross-group data by sector, and results are available from the first author.

• Evaluated by double blind review process. Scientific Editors: Adriana Roseli Wünsch Takahashi, Sergio Bulgacov, Claudia Cristina Bitencourt and Hale Kaynak

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Publication Dates

• Publication in this collection
  May-Jun 2017

History

• Received
  29 June 2016
• Accepted
  08 Feb 2017