Strategic and Digital Alignments Impact on Firm Performance PDF

Technology Alignment and Digital Alignment :Diagnosing Similar and Different Components and Their Impact on Business Performance Management Efrat Guli & Dr. Hanan Maoz & Dr. Amir Alaluf Department of Management...

Technology Alignment and Digital Alignment :Diagnosing Similar and Different Components and Their Impact on Business Performance Management Efrat Guli & Dr. Hanan Maoz & Dr. Amir Alaluf Department of Management Bar-Ilan University, Ramat Gan, Israel Abstract The article investigates how large organizations perceive the business value of digital technologies and applications, formulate digital strategic plans and projects for digital transformation. It draws upon recent body of knowledge in technology alignment (TA) and the business units, through which organizations improve their performance. The authors present definitions, research assumptions and evaluation criteria and provide a measurement model separating digital alignment (DA) from technology alignment (TA) and measuring their impact on firm performance. The research empirically confirmed the hypotheses that both are well differentiated and significant. The research uses the PLS-SEM empirical method suitable for creating and examining models engaged in predicting the impact of theoretical structures. The findings effectively illustrate the relationship between traditional technology management, and innovative digital management, which uses similar resources but operates via different models to impact firm performance. The major objective was aimed to provide insights on the management of digital transformation initiatives taken by large organizations by using two major instruments of technology alignment and digital alignment, and to provide knowledge for further theorization by prospective researchers, as well as management actions to be considered by practitioners. The authors also discuss the value and limitations of the model and research approach, and lays the foundations for a broader definition of a digital-business alignment construct, and call for further for corroboration of this definition in distinct business sectors and different sizes of organizations. Finally, the article offers an empirical way of doing research on the applied side of digital transformation that is timely, does justice to the phenomena under investigation, and provides insights for improving business performance by developing new business models. Keywords: strategic alignment, technology alignment, digital alignment, technology management, digital management, digitization, digitalization, business performance management, firm performance, organizational innovation. i 1. Introduction For two decades, there has been extensive research and discussion on technology-business alignment (Venkatraman et al., 1993a), dealing with management and Improvement of the value derived from technology investments (Chan et al., 2006; Oh et al., 2007a; Preston et al., 2009a; Sabherwal et al., 2001; Tallon, 2007; Tallon et al., 2011a). In recent years, and based on significant technological developments in internet and cloud computing, data, and artificial intelligence, which are based on a new conception of data digitizing, business organizations have begun to ask how business models can be meliorated based on new trends. Additionally, how to enhance the detection and response capabilities of business threats and marketing opportunities, thus creating new competitive advantages in the organization's markets. Moreover, there is a significant increase in product consumption in the digital markets, changes in consumption habits, new commerce, production, and work models in constantly changing conditions. The new digital space creates new threats and opportunities that require rapid changes, business and technological agility, and a new set of digital skills required to build innovative products, services, and new business models. The alignment between business strategies and the digital array turns out to be a new and central business imperative, coexists alongside traditional technology-business alignment, since the innovative digital toolkit is at its core technological (internet technologies, data management system), and even traditional technology (servers, networks, enterprise systems). It raises questions concerning the relationship essence between technology alignment and digital alignment. What form of organizational alignment should the organization develop to create an adequate interaction between these two investment values, technological and digital, to maximize the firm's goals achievement? And more. Numerous studies published in recent decades offer various models dealing with traditional technology- business alignment, and few are involved with the new parallel: digital-business alignment, which relies on technological developments in the fields of data management that enable it. The study deals with the reciprocal relationship between the two alignments in modern business organizations, offers a measurement model containing the main components of each alignment, and examines the meaning of their reciprocal interactions and their possible impact on firm performance. 1.1. Research Goals The research expands the growing body of knowledge of digital alignment in large organizations, its components, and its interactions with the more mature construct: technology alignment. The study presents a more distinct model that contains the set of connections and effects between the constructs and their impact on firm performance, especially in terms of supportive organizational structures and perceiving organizational innovation. The current research literature lacks sufficient distinction between the mature-technology and young- digital constructs. At the same time, they seem separate entities that influence how organizations recruit and leverage technology for management and development. The paper offers broad nominal definitions for both constructs and examines their interactions and the mutual contribution to the firm performance. The research proposes a new nomological model for examining managerial and technology alignments (Cronbach et al., 1955). The model links conceptual ideas (constructs) within a statistically analyzable network structure. The nomological network contains diverse reciprocal relationships between variables and offers a broad theoretical framework for analyzing relationships, directions, and impact intensities between manifested and latent variables. The nomological network alongside the SEM methodology (Structural Equations Modeling) lays a solid scientific basis between theoretical ideas and the ability to model and measure them using empirical evidence. The research offers a set of observed indicators of digital alignment. The paper discusses the nature of the relationship between technology alignment and digital alignment. Finally, the research used the nomological network to analyze the constructs' influence directions and intensity on the firm's performance in value accelerators, innovation, competitiveness, product management, and supply chains and markets. 2. Literature Review 2.1. Theoretical Background The early definition of the technology-business or technology-strategic alignment concept (Venkatraman et al., 1993b) was formulated as to how compatible is the technology management strategy for the goals and objectives of the organizational strategy, including in situations of organizational environment dynamism and applied aspects in various industries and sectors (Lee et al., 2014; Oh et al., 2007b; Weill et al., 2006). At the core of striving for high business technology alignment is the assumption that it is possible and essential creating a synergy of resources to achieve competitive, achievable, and sustainable advantages (Pavel; Andreev et al., 2009; Maoz et al., 2007). RBV (resource-based view) theory forms a solid basis in the technology management literature to examine the interdependence between all resources (inputs, processes) in the organization and the potential impact on the firm's performance (outputs, results) (Wade et al., 2004). According to RBV, the arrangement quality of all technological and digital resources creates value in the organizational structure and may sustain continuous technology alignment that helps the firm achieve its goals (Bardhan et al., 2013; Ravichandran et al., 2005). (Barney, 1991), 1991, argued that firms build competitive advantages based on arranging and adapting resources to business needs and building process capabilities from the same resources. (Pavel; Andreev et al., 2009), 2009 refined the possible impact on the organization's performance through different types of competitive advantages (CA) that the organization achieves. He distinguished between business advantages for the organization that are achievable in the short term (Attained CA) and sustainable long-term competitive advantages (Sustained CA), thus refining the technology alignment concept's components and conceptualizing the interaction model with the types of business competition. At the basis of RBV, resources and capabilities constitute a lever to improve the organization's Performance (Anthony et al., 2006; Eisenhardt et al., 2000; Preston et al., 2009b; Ray et al., 2004; Saggi et al., 2010), this way, the new digital alignment, along with traditional technology alignment, is combined as a modern link in connecting the various resources to improve the organization's performance. The term digitization under the RBV theory (Barney, 1991; Wernerfelt, 1984) focuses on describing digital resources, especially data resources, generated or collected in order to develop innovative digital products, running new processes digitally, or building new business models for consumption and commercialization by customers, suppliers, and employees. (Porter, 1985) (1985) describes the value chain of intra-organizational activities as creating unique and competitive organizational capabilities. Accordingly, digitization constitutes a new organizational capability in processing and analyzing data, creating innovative products, processes, and business models. (Suketu Gandhi et al., 2018) argue that digitization shapes organizational flexibility and new decision-making capabilities in an organization, all based on the intelligent use of data to streamline the existing business model. (Lai et al., 2008) refer to digitization as a value accelerator component in the classic production factors: materials, work, and capital. They illustrate the positive impact of digitization as a value booster for materials production (data, information, databases) in logistics. (Syam et al., 2018) complement them and show the impact of machine learning and artificial intelligence technologies on the efficiency of the manufacturing factor in marketing and sales. Recently, Ritter and Pederson(Ritter et al., 2019) emulated the discussion of digitization essence from technical perspectives to organizational capabilities and numbered three central ones in the organization, which are: a) organizational skills in data management b) managing access permissions and data use in the organization's ecosystem c) the ability to review, analyze and mine the data for decision making. It would appear that the components identifying internal digitization capabilities in the organization are the following: Data and information as a supportive resource for decision making within the organization's management (processing data, reviewing, analyzing, and mining it for improved decision making). Data and information as a major component for creating new business capabilities and models to manage the firm's operations and marketing. Data and information as value-accelerators to traditional production factors and processes. New organizational skills in data, information, knowledge, and insights processing (data science).  Data and information as a structure for security management of the organizational interaction in its ecosystem. Data and information as innovative elements for creating new digital products and services for customers and suppliers. In addition, organizational digitization may increase business attention to the external environment, which is deeply supported in the environmental dynamism theory defined as attention and prediction of environmental changes (Newkirk et al., 2006). At its core is the managers' challenge to quickly and frequently adopt new strategies and tactics, adapted to the external environment where threats and opportunities are constantly made (Yayla et al., 2012). In a dynamic environment, it is necessary to rearrange technology and digital capabilities and resources to exist on a long-term basis. Additionally, it is crucial to differentiate competitive short-term, or sustainable advantages over time (Pavel; Andreev et al., 2009) and make frequent and adapted changes in the organization's operational and strategic systems (Sull et al., 2015). Also, it's important to focus on exploring new opportunities for the organization (De Haes et al., 2011), make quicker decisions by reducing the time to absorb new information, process data, and leverage technological resources to create unique competitive knowledge (Van Den Bosch et al., 1999). Furthermore, improve management communication between business managers and IT managers, enabling innovation to bubble up in the organization as a dynamic and attentive process – just as the environment performs. The research in this paper expands this approach and presents digital-business alignment as complementary to technological-business alignment, creating a more profound organizational attention to the external environment. Complementary to the abovementioned is the Dynamic Capabilities Theory (D. J. Teece et al., 1997; D. Teece et al., 1994) argued that coping with a dynamic environment renews all types of organizational inventory (products, services, knowledge, capabilities, behavior, and more) and enables the provision of a renewable stream of innovative products and services. The dynamic capabilities emphasize the development of management and innovation capabilities, difficult to imitate by potential competitors, thus contributing crucially to improving the sustainable firm performance (Pavel; Andreev et al., 2009; Helfat et al., 2003). In the Dynamic Capabilities Theory lies a profound insight claiming that the organization must develop resources adaptability to the environment, thus creating innovation that copes with uncertainty, maintains competitiveness, and improves the firm's performance. The research presented assumes that technological and digital alignment lay a platform for improving the organization's dynamic capabilities, accelerating organizational innovation, and improving firm performance. Organizations' digitization processes are identified with innovation (Yuniarty et al., 2021). According to IBV (Innovation Based View) Theory, the ability to digitize may enable an attainable (short-term) business advantage or sustainable (long-term) competitive advantage (Pavel Andreev et al., 2019) by capturing various economic values arising from the organization's innovation processes.  Figure 1. Costello 2019 (A.Schumpeter, 1942; Joseph A. Schumpeter, 1934) saw organizational innovation as a dynamic resource arrangement in the organization, new and different, creating new processes and methods. (afua yank, 1998) referred to innovation as a new knowledge component integrated into products, processes, and services, and the mainstream, represented by Kuniyoshi, John, and Tadao in (Kuniyoshi et al., 1988) see innovation as a process of the birth of a new idea and its transformation into a new product, process, or service that improves the firm's performance. 2.2. Technology Alignment Technological development over the past 30 years has presented business executives with the challenge of strategic alignment between the business and technology domains for utilizing to the fullest the resources and technological investments, alongside improving the firm's internal performance (Bharadwaj et al., 2013a) or constantly improving business results (Yeow et al., 2018). This alignment is also known as technology alignment and includes alignment activities in planning and policy, alongside operational alignment in achieving excellence in operational, logistics, and finance processes (Yeow et al., 2018). (Luftman et al., 2017a)) note that over time, academia has developed a research body of knowledge in the fields of technology alignment. In essence, six significant variables (constructs) make up a technology alignment model: organizational communication, value creation, IT governance, business partnership, character and technology scope, and finally, technology management skills. The researchers emphasize the model's ability to predict the level of impact that technology alignment has on firm performance. 2.3. Digital Alignment While technology alignment focuses on efficiently managing technological resources for successfully implementing business strategy, digital alignment is a tool for redesigning products, processes, structures, and business models. It is aimed at business models' efficiency and effectiveness simultaneously and reaching beyond the organization's supply and demand chains (Bharadwaj et al., 2013a). A review of the research literature shows that few studies deal with digital-business alignment, and few distinguish it from the technology alignment that has been in the mainstream for decades. Essential references to digital alignment see it as part of IT strategies, more or less significant. In their article "Strategic IT Alignment: Twenty-Five Years on," (Coltman et al., 2015) mention digitization as part of the strategic option spectrum in the IT portfolio, that is, an essential part within the IT capabilities set and not separate from technology alignment. In recent years, digital alignment has been growing as a separate branch from the traditional technology alignment, highlighting the strategic independence of digital activity in the organization in light of significant technological changes in cloud computing, social networks, and technological consumerism.(Bharadwaj et al., 2013a; Coltman et al., 2015; Horlach et al., 2016; Kahre et al., 2017; Yeow et al., 2018) 2.4. Firm Performance Through Digital Innovation The digital innovation concept has received significant interest in recent years, both in academic and business discourses, as a young field of research combining theory and practice from several fields in social sciences and management (Nambisan et al., 2017), alongside the construction of practical models with the industry (Daniel Nylén, Jonny Holmström, 2014). It seems that the research directions in the field of digital innovation seek to estimate the impact digital innovation has on organizational and business transformations required to maintain or create competitive advantages. Technological, business, industrial or managerial innovation is not new, but "digitization of innovation" undermines the traditional assumptions of creating business value for the firm (e.g., (Daniel Nylén, Jonny Holmström, 2014; Yoo et al., 2012)) In previous decades it was based on creating value from resources, efficiency, knowledge accumulation, and more (Costello et al., 2011), to the current period in which there is a profound investigation of the conditions and mechanisms that enable digital innovation to re- combine resources and processes in creating value for a business organization. Digital innovation, in its new definitions, refers to the use of digital technology (databases, data structures, data algorithms, and data infrastructure) to create processes or business products that did not exist before (Nambisan et al., 2017). It is more complex than it sounds at first since traditional industries, which do not base the bulk of their business processes on data chains in information networks, digital transformation constitutes a great challenge of change management (Henfridsson et al., 2014; Svahn et al., 2017). (Richard J. Boland et al., 2007; Westergren et al., 2012) claim that, unlike the traditional value creation structure that includes IT organizational systems and applications, the business value is created from digital innovation through nonlinear, distributed, and dynamic control processes in communication network environments and big data. Digital innovation in theoretical research appears in (Henfridsson et al., 2018) in the infinite flexibility digital resources enable to create new business value spaces. (Henfridsson et al., 2014)is based on the intellectual work of (Joseph A. Schumpeter, 1934)(1934), who had conceptualized innovation's root in creating "new combinations" (recombinations). His argument is more evident today than ever before since today's digital technologies are editable (Kallinikos et al., 2013), can be reprogrammed (Yoo et al., 2012), and dynamically develop according to the forces of technology after an application period (Daniel Nylén, Jonny Holmström, 2014) to create new business value spaces. (Henfridsson et al., 2018) (2018) emphasize that the basic flexibility of data structures in digital technology enables continuous regeneration by constantly entering data in organizational and business structures - a feed that provide a permanent platform for a virtuoso cycle of innovation. The theoretical framework conclusion in this paper identifies creating business value for the organization from digital innovation as a concept (construct) encompassing three key components: 1) Value space: an evolving network of digital resources linked through business logic data structures designed to generate business value in the firm's areas of operations. 2) Digital resources: digital elements (data structures, algorithms) that serve as building blocks in the value spaces, designed to create and capture business value from data and information in digital innovation. A digital resource can be one of the four basic elements: digital devices, a digital network, digital services or content, and, of course, their combination. 3) Recombination (reintegration) of resources and processes in an organization through data structure design, innovative integration of digital resources, and utilizing the infinite flexibility of digital space to create new valuable spaces for users. 2.5. Structural Model The PLS-SEM structural equation model in this research consists of two sub-models: structural and measurement models. The structural model represents the connections between the latent variables and breaks down into the measurement model representing the connections between the observed indicators (data) and the latent variables. The research is modeling technology alignment and digital alignment in a nomological network (Cronbach et al., 1955). It compares symmetrical and asymmetrical components between the two types of alignments to create distinctions in the model and empirical analysis process. The structural model allows measuring the directions of impact and intensity between technology and digital alignment and firm performance based on the extensive literature survey reviewed above. The model proposed in this research addresses three latent variables in a complex relationship between dependent and independent variables, aiming to create new knowledge predicting between organizational investment in technology management and digital transformation and the level of organizational innovation. The structural model schema: Figure 2. A structural model for the Study of the relationship between Technology Alignment and Digital Alignment and Firm Performance measured by the level of Digital Innovation Technology alignment is commonly mentioned for its impact on firm performance. Rajiv and his partners in (Sabherwal et al., 2019) discuss at length the various functions of technology-business alignment on firm performance. Their research verifies that regardless of the organization's business environment, technology alignment directly improves its performance. In other studies, technology alignment was measured at the level of business flexibility it enables or the level of innovation it promotes in the organization (Cui et al., 2015; Luftman et al., 2017a; Tallon et al., 2011b). In all of the above cases, the effect of technology alignment was measured on the value proposition it creates in the organization and on the success scenarios it achieves. This paper expands the concept of technology alignment and focuses it on the value proposal it creates to improve firm performance in the fields of digital innovation, and claims that: H1: technology alignment positively affects firm performance Digital alignment is achieved when an organization's digitization processes are aligned with business needs to achieve the goals and objectives of the organization. Over the past decade, the research body's trend focuses on increasingly separating between the alignments, along with empirical evidence of a new, segregated effect of digital alignment on firm performance (Yeow et al., 2018), and it is claimed that: H2: Digital alignment positively affects firm performance 3. Research Method 3.1. Data Collection The data collection consisted of two main stages: a pilot and a complete survey. The pilot phase was designated to examine the questionnaire content and offer improved accessibility processes, early content, and structure validations. It was sent to four senior executives in various organizational positions (technologies, engineering, and senior management) and two senior academics from the management field. The pilot results improved the content's validity and clarity. The complete survey stage used a convenience sample which is considered a practical solution for collecting reliable data adapted to this study, focusing on the population of organizations' senior managers to which there is a natural difficulty in accessing surveys. The survey was distributed to 100 organizations and 50 professionals who were encouraged to expand the distribution circles to professional colleagues or business partners. Sixty-five questionnaires were collected, of which 59 were found as validated for empiric processing, and six were disqualified due to a lack of answers or unclear answers. Since the questionnaire was made for a single respondent, the CMB (Common Method Bias) problem that occurs when variations in responses are caused by the measurement tool (questionnaire), was handled in two parts: in the first one, before the survey was carried out, the measurement tool was handled using early procedural control components in the survey's planning and distribution, and in the second part, after collecting the data, the tool was calibrated through statistical control for all respondents and answers (Conway et al., 2010; Podsakoff et al., 2003). The first part (the survey planning and distribution stage) uses procedural remedies, including a close separation between the dependent and independent variables, protecting respondents' anonymity, reducing assessment concerns, and improving the scale system. In the second stage, after collecting the data, statistical control was performed via a pre-test to the level of data bias that checks the Variance Inflation Factor (VIF) values, preferably values less than 3.3. The results showed that all VIF data was under 3.3 and that the structural model was free of common method bias (CMB) (Kock, 2015). The survey distribution shows that of the 59 validated questionnaires, 18 (31%) from the high-tech sector, 18 (31%) from industry and commerce, 13 (22%) from services and others, and 10 (17%) from the real estate and finance sectors. The organization size parameter shows that 24 (41%) responses came from medium-sized organizations (51-1000 employees), 19 (32%) from small organizations (01-50 employees), and 16 (27%) from large organizations (1000+ employees). 20 (34%) of respondents are executive level, 15 (25%) VP level, 13 (22%) Middle Management level, and 11 (19%) Professional Management. 38 (64%) of respondents hold a business-oriented role, while 21 (36%) respondents have a technology-oriented role. Respondents' managerial and professional experience showed a division of 27 (46%) respondents with 20 years of management experience, 17 (29%) respondents with 11-20 years of experience, and 15 (25%) respondents with up to 10 years of experience. The respondents' level of education was divided according to 27 (46%) those with a master's degree, 17 (29%) undergraduate subjects, 9 (15%) respondents with only vocational education, and 6 (10%) Ph.D. graduates. 23 (39%) respondents with engineering and technological education, 21 (36%) have an education in business administration, and 15 (25%) in marketing and finance. 3.2. The measurement model As mentioned above, the measurement model represents the connections between the observed indicators (data) and the latent variables (constructs). The model offers a set of indicators for each of the latent variables and has performed a content validity test, ensuring the coverage of conceptual content in the research. The creation of measurement variables (indicators) is intended to ensure the structure's content validity and is based on a research and business literature survey and consultation with academic and industrial experts. The content validity test checks that the latent variables (constructs) cover the research's conceptual structure content sphere and that the measurement tool (the questionnaire) represents the indicators correctly. The measurement tools examination and its calibration are performed using the professional knowledge of academic and industrial experts. In addition to relying on a literature survey, we consulted three experts to determine the questionnaire's indicators and calibration. (Cui et al., 2015; Han et al., 2017; Ilmudeen et al., 2019; Ping-Ju Wu et al., 2014) Further to the literature review, we defined three latent variables with their relevant set of indicators within the measurement model as the followings: Technology alignment as the extent of fit between information technology and business strategy (Tallon et al., 2011a). IT governance, IT congruence, IT communication, and IT portfolio are the measured variables (indicators) for technology alignment as adopted by (Luftman et al., 2017b)and (Sabherwal et al., 2019) In order to measure the extent of leveraging digital resources to create the firm's targeted differential value, digital alignment was measured with speed of decision making, digital scope, digital scale, and sources of value creation (Bharadwaj et al., 2013b). Firm performance can be measured in various ways. Financial aspects such as sales, profits, and market shares are prevalent indicators. In this research, firm performance refers to digital technology use during the business innovation process or the result of business innovation (Nambisan et al., 2017). Respectively, digital innovation is measured with better performance optimization by implementing new business products or models, new practices by technology, greater effectiveness by better decision- making processes, and newly gained business insights by data. The following is the measurement model in the study: Figure 3. Measurement Model 3.3. Partial least squares (PLS) method This study uses the PLS-SEM method suitable for creating and examining models engaged in predicting the impact of theoretical structures (Henseler et al., 2014; Maoz et al., 2010; Reinartz et al., 2009). According to (Chin, 1998), the method is suitable to deal with a research model in which: 1) There are complex relationships between dependent variables (DVs, Criteria Variables) and independent Variables (Predictors, Explanatory Variables), 2) There are latent variables (LVs) that are difficult to directly observe, 3) There may be errors in latent or observed variables (indicators) due to the subject's innovation, and 4) the research model has a transition between exploratory research and confirmatory research, hence the use of the method provides an a priori statistical test for validating the theoretical hypotheses vis-à-vis the empirical data. Additionally, the method's advantage is that it is suitable to handling a small sample size (