Establishing a partnership between top and IT managers: A necessity in an era of digital transformation

Anton Manfreda (Department of Business Informatics and Logistics, Faculty of Economics, University of Ljubljana, Ljubljana, Slovenia)
Mojca Indihar Štemberger (Department of Business Informatics and Logistics, Faculty of Economics, University of Ljubljana, Ljubljana, Slovenia)

Information Technology & People

ISSN: 0959-3845

Publication date: 5 August 2019



A poor relationship between top management and IT personnel is often denoted as a business–IT gap. In an era of digital transformation, bridging this gap and establishing a strong relationship between business and IT are more important than ever before. The purpose of this paper is thus to examine a particular link between business and IT managers – a partnership relationship – together with the factors facilitating it.


A partnership construct is developed based on interdisciplinary studies and transferred to the business–IT context since it is not generally used in IT disciplines. The model was empirically tested with structural equation modelling using data obtained from 221 IT managers in Slovenian companies.


The results show that both the perceived value of IT and the business orientation of the IT department exert a positive influence on the partnership, while a mere technology-oriented IT department has a negative effect on the partnership relationship. Furthermore, the paper also presents the prerequisites for a business-oriented IT department.


In this digitalisation era, IT is becoming even more important for its strategic role in organisations. There is thus a strong need to bridge the business–IT gap. Despite significant efforts made to close this gap, it remains a major issue. This research contributes to understanding the business–IT gap and presents the key factors for ensuring a partnership relationship is in place. The study also combines the views of social exchange theory and knowledge-based theory and upgrades findings concerning the influence of social facilitators on collaboration outcomes.



Manfreda, A. and Indihar Štemberger, M. (2019), "Establishing a partnership between top and IT managers: A necessity in an era of digital transformation", Information Technology & People, Vol. 32 No. 4, pp. 948-972.



Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited

1. Introduction

The business–IT relationship has been the subject of research for over 50 years. Several studies have been conducted to examine and improve the relationship between top management and IT personnel or to emphasise business–IT strategic alignment (Gerow et al., 2015). In the last few decades, the role of IT personnel has changed substantially due to the growing importance of IT, with a gap emerging from different perceptions by business departments and IT department regarding the role of IT personnel (Nord et al., 2007). This poor relationship between top managers and IT personnel is often referred to as a business–IT gap and denotes the lack of understanding between them (Coughlan et al., 2005; Peppard and Ward, 1999). The latter is particularly relevant in the last few years with IT gaining a strategic role in organisations due to digitalisation and its expansion into different business areas. Business performance is ever more dependent on the effective use of IT (Gerth and Peppard, 2016).

Given the impact of a business–IT relationship on the success of implementing IT (Luftman et al., 1999) and the company’s overall performance (Chan and Reich, 2007), several authors have made considerable efforts to bridge the gap and improve the business–IT relationship. Yet many organisations today still face difficulties closing this gap (Alaceva and Rusu, 2015) and, therefore, calls are made for a strong relationship between business and IT to improve the extent to which they appreciate each other. A strong business–IT relationship takes a special form, namely a partnership relationship, since a partnership is recommended for companies in order to attract valuable customers, increase profits (Teng, 2003) and obtain a competitive advantage. This is particularly important in an era of digital transformation where proper collaboration between business and IT is crucial (Bharadwaj et al., 2013). The challenges of digital transformation, which refers to mutual effects of digital innovations, practices and values (Hinings et al., 2018), require partners to significantly expand their cooperation capability (Bondar et al., 2017).

However, the professional and academic literature lacks research on how to provide sufficient conditions for establishing a relationship that will ensure better collaboration between top management and IT personnel (Benlian and Haffke, 2016) and enable the IT to be used as a competitive advantage. The core focus in the literature is on business-to-business partnerships or relationships in supply chains (Cheng, 2011), whereas detailed research on the term partnership in the business–IT context is lacking. Yet, it has been appealed that IT and business experts should have a proper relationship (Järveläinen, 2013). Moreover, it has been claimed that the business–IT partnership is the most important factor for successfully implementing IT since it makes the process of adopting the IT easier (Tian et al., 2010) and also positively influences the strategic contribution (Zardini et al., 2016). Yet, the literature does not clearly set out the factors that lead to a partnership relationship.

The purpose of this paper is thus to examine the partnership relationship between top managers and IT personnel and to identify factors important for that partnership. Since the term partnership is typically not used in the business–IT relationship literature, indicators measuring the partnership construct on the organisational level, i.e. measuring the partnership relation between organisations, were applied to the relationship between top management and IT personnel. Furthermore, to start a collaboration some form of social exchange is needed (Malmström and Johansson, 2016) and social exchange theory as one of the most influential conceptual paradigms for understanding workplace behaviour (Cropanzano and Mitchell, 2005) was applied in the paper together with the knowledge-based theory of the firm. After all, social exchange theory explains why individuals are prepared to collaborate and is hence also useful for explaining business–IT partnerships.

The paper is divided into four main parts. First, the theoretical background is provided for the constructs used in the proposed model, namely, partnership, the role of IT personnel, the role of knowledge and skills and the perceived value of the IT. In the second part, the research instrument and research methods are presented, followed by the data analysis and the results. Finally, implications are discussed and directions for future research are outlined.

2. Literature review

2.1 The business–IT partnership and social exchange theory

The term partnership is generally used in management disciplines to simply describe the relations between companies or organisations. It is recommended that companies establish partnership relations in order to create top products, attract valuable customers and increase profits (Teng, 2003). Additional risks such as growing global operations and cost reductions also force companies to form partnerships and participate in supply-chain collaboration (Zeng and Yen, 2017). In a model of partnership success, Mohr and Spekman (1994) employed several attributes important for successful business-to-business partnerships, namely, commitment, coordination, interdependence and trust. These attributes make the partnering organisations aware of their interdependence and willing to act towards a valuable relationship (Tuten and Urban, 2001). Similarly, a study examining cross-sector social partnership success showed that the success of partnerships in socially responsible businesses depends on sharing the same values with their non-profit partners, and therefore, contributes to trust and commitment (Barroso-Méndez et al., 2016).

However, attempts have already been made to place the term partnership in the business–IT relationship context. Partnership in the business–IT context was first mentioned in the early 1990s when claiming that simply ensuring an appropriate alignment with global business drivers is not a guarantee of success. Therefore, organisations should apply different approaches to manage the obstacles, namely managing project risk, utilising partnerships and building global infrastructure (Ives et al., 1993); yet the term partnership in this study was merely specified as one of the most important risk management approaches, without defining it. In the business–IT context, a partnership may signify the organisational ability to bring cross-functional efforts together in deploying the IT for the purpose of creating new business opportunities (Tian et al., 2010). Nevertheless, the effective use of IT resources depends on the relationship between the IT department and business departments within the organisation (Bassellier et al., 2001). An attempt to present measures to define the business–IT partnership was done in a study (Tian et al., 2010) using four items to measure a cross-functional partnership: mutual understanding, mutual trust, mutual involvement and conflict resolution. These measures were taken from a study (Ravichandran and Lertwongsatien, 2005) examining the influence of IT capabilities and resources on a company’s performance. Business–IT partnership has also been defined as how the IT department and business departments perceive each other’s contribution, including the role of the IT in strategic business planning and sharing the rewards and risk between the IT department and the business functions (Chen, 2010). However, the research focused on the role of partnership maturity in connection with alignment maturity constructs for the IT strategic alignment, and therefore, the research covered neither the business–IT partnership nor the factors important for creating such a partnership. Measuring partnership maturity in this research was developed based on the strategic alignment model (Luftman, 2000; Sledgianowski et al., 2006) and therefore included business’ perceptions of the role of the IT, the role of the IT in strategic business planning, the integrated sharing of risks and the effectiveness of partnership programmes.

Partnership related to the IT and business has also been used in research expressing principles of good IT governance (Chris, 2005), claiming that efficient governance is similar to an enterprise-wide partnership between business and IT where both sides have the right understanding of each other. However, the research offered neither definition of such a partnership nor the indicators to measure it or the factors influencing it. Similarly, it has been shown that a good relationship between top management and the IT manager is crucial for a healthy business–IT partnership (Benlian and Haffke, 2016). However, the question of how to achieve business–IT partnership remains unanswered. The same applies to a research (Siurdyban, 2014) examining the business–IT partnership from a business perspective focusing on the link between IT management and business process management. That research was based on a single case study and therefore suggested a detailed analysis of such a partnership as a future research opportunity.

In today’s era of digital transformation, collaborating relationships are attributed with extra importance. The key capability of the digital age is the ability to deliver new business models, where collaboration even with one’s competitors is perceived as a new challenge (Berman, 2012). Nevertheless, digital transformation requires partners to significantly increase their cooperation capability (Bondar et al., 2017). Proper collaboration within the organisation is hence not seen as a competitive advantage but as a need. Yet, to start a collaboration, some form of social exchange is needed (Malmström and Johansson, 2016). Social exchange theory has already shown its potential to describe collaborations in innovation projects (Brass et al., 2004) and social exchange was proposed as a determining element of the success or failure of collaboration projects (Malmström and Johansson, 2016). Still, social exchange theory is considered one of the most influential conceptual paradigms for understanding workplace behaviour (Cropanzano and Mitchell, 2005) and is developed on a cost-benefit viewpoint based on self-interest as a combination of economic and psychological needs (Homans, 1958). Thus, individuals contribute resources only where their contributions will be reciprocated (Blau, 1964). Applying social exchange theory, a recent study (Malmström and Johansson, 2016) examined the role of social facilitators in project success and presented trust, commitment and congruence as three interrelated factors that facilitate productive collaboration. The theory explains why employees are prepared to collaborate, and therefore, it is also useful for explaining partnerships between departments within an organisation, including business–IT partnerships.

The partnership construct in our research was thus developed based on studies on the partnership between organisations (Luftman, 2000; Mohr and Spekman, 1994; Teng, 2003) and attempts to define partnership in the business–IT context (Chen, 2010; Tian et al., 2010). In particular, above-presented measures for successful business-to-business partnerships (Mohr and Spekman, 1994) were combined with measures for cross-functional partnership (Tian et al., 2010).

2.2 The role of IT personnel in organisations

The role of IT personnel has changed considerably in the last few decades. While in the 1970s the IT department was understood as a closed unit completely ignored by management (Keen, 1991), it has become increasingly important with the growth of technology and systems for general business use (Nord et al., 2007). Consequently, an ambiguity regarding the role of IT personnel appeared since IT managers were uncertain whether their role was to participate in business process redesign or to merely support business departments within the organisation (Ward and Peppard, 1996). Moreover, it was not even clear whether IT personnel represent a strategic resource or simply an expense (Earl and Feeney, 1994). It was even argued that this ambiguity negatively influenced the business–IT relationship (Ward and Peppard, 1996), resulting in the IT department’s lower credibility (Alaceva and Rusu, 2015).

It has also been found that significant differences between business personnel and IT personnel stem from top management’s perception that IT personnel are technology oriented and unable to communicate properly (Willcoxson and Chatham, 2006). These differences create problems in establishing a partnership relationship since they increase the business–IT gap which arises from a lack of understanding between the management and IT sides of the organisation (Coughlan et al., 2005). Moreover, top management’s perception that IT managers are not good with the decision-making process in uncertain circumstances (Willcoxson and Chatham, 2006) shows that IT departments are still treated as a supporting function in the organisation, not a business partner or strategic resource.

Furthermore, since IT managers are more task oriented, their focus is on the service-providing orientation of the IT department rather than its strategic-decisions orientation, which adds to problems in the business–IT relationship (Willcoxson and Chatham, 2006). Nevertheless, even though the IT can transform the business, top management often perceives the IT department as holding a secondary status within the organisation. However, in order to improve the contribution of IT to competitiveness, IT department should not be managed separately from the rest of the organisation (Peppard, 2018).

It was claimed decades ago that the growth of electronic commerce will improve the status of IT personnel since technology will be recognised as a source of revenue rather than a cost, and the IT will thus become part of the business and not a mere support function (Gantz, 1997). Several business improvement programmes like business process redesign have also been considered as having an important impact on the role of IT personnel (Kakabadse and Korac-Kakabadse, 2000). However, despite the mentioned predictions, IT managers may still be viewed by other executives as technical service providers or support persons (Gerth and Peppard, 2016). Moreover, recent research has shown that IT managers spent almost two-thirds of their time focusing on IT-related activities, while the share of non–IT-related business activities is declining (Kappelman et al., 2018).

Our first hypothesis is thus proposed based on the literature review claiming that a partnership refers to mutual understanding, mutual trust, mutual involvement and conflict resolution (Tian et al., 2010), along with claims that the ambiguous role of the IT department and service-oriented IT department resulted in lowering the IT department’s credibility (Alaceva and Rusu, 2015). Besides, based on our findings from in-depth interviews with IT managers revealing that the language spoken by technology-oriented IT personnel is generally unclear to top management and their claims that individuals in a technology-oriented IT department concentrate more on how to technically implement a project and less about the business implications in terms of changes the organisation must make to improve its performance, we postulate the following hypothesis:


The technology orientation of the IT department has a negative impact on the partnership between top management and IT personnel.

After all, it is claimed that insufficient knowledge on both sides produces a poor business–IT relationship, poor communication and consequently the weak alignment of IT with business needs (Martin et al., 2004), while it was shown decades ago that improper understanding of business needs leads to unsuccessful IT implementation and influences the IT personnel’s credibility within the organisation (Doll and Ahmed, 1983).

However, in the 1990s the focus of IT personnel’s role moved from managing just a technical perspective, i.e. from being merely technology oriented to managing a relationship perspective (Venkatraman and Loh, 1994). A recent study showed that even the role of IT managers has changed in the last decade and reflects both the IT infrastructure and the organisational strategy (Chun and Mooney, 2009), signifying that both aspects, namely technology orientation and business orientation, are being covered.

Moreover, IT alignment is claimed to be helpful in coordinating IT with firm decisions; however, it is neither a sufficient nor a necessary prerequisite for effective integration (Baker and Niederman, 2014). It is therefore essential to present the IT and the IT department as a means for achieving business goals and not simply as a support department (Coughlan et al., 2005). Hence, the IT manager’s role is to ensure that the IT is seen as a strategic resource that provides value to the organisation, which can be achieved by establishing the strategic role of the IT instead of merely a supporting role. It has also been suggested that the role of IT personnel should be clearly defined in order to improve the business–IT relationship. This includes defining the contribution of IT personnel, aligning the IT objectives with the business objectives and sharing knowledge with top management (Nord et al., 2007). Therefore, we hypothesise:


The business orientation of the IT department has a positive impact on the partnership between top management and IT personnel.

2.3 The knowledge and skills of IT personnel

The discussion of the importance of different knowledge and skills is as old as the IT field itself, although in the 1980s the importance of technical skills rather than business and managerial ones was emphasised (Byrd and Turner, 2001). This view slowly changed in the 1990s when it became apparent that IT personnel need a combination of business, technical and interpersonal skills (Mata et al., 1995). It is essential that IT managers and IT personnel have various skills and capabilities (Lerouge et al., 2005; Parolia et al., 2007). Yet technical skills were still the most important for IT managers (Byrd and Turner, 2001), probably given that most IT managers generally had a technical background (Chatham and Patching, 2000). However, it had been argued before that IT managers lack communication skills and therefore special efforts should be devoted to improving those skills (Todd et al., 1995). The stereotype of a technically oriented IT manager as someone with lower interpersonal skills nevertheless finds little empirical support (Enns et al., 2003), although the results highlight the need for more research on this topic. Moreover, since the technology is dramatically changing, also IT managers have to recognise that a diverse set of knowledge and skills is now needed (Dumeresque, 2014). Besides, new digital technologies require different skills comparing to prior waves of transformative technologies; yet the main focus of IT managers is on IT, while the implications of new technologies for business are in domain of chief digital officer (Singh and Hess, 2017). Consequently, some organisations are replacing IT managers with chief digital officers; however, merely changing the job titles is not believed to resolve the problems (Gerth and Peppard, 2016).

Considering the view of the knowledge-based theory of the firm and its proposition that the knowledge applied to a business activity is affected by the organisational manner in which individuals are involved (Conner and Prahalad, 1996) and that people are seen as the only true agents in business and all intangible relations are merely a result of human action (Sveiby, 2001), while strategy formulation and consequently the department orientation start with the competence of people (Sveiby, 2001), we postulate:


An IT manager with a high level of technological knowledge and skills has a positive impact on the technology orientation of the IT department.

It was claimed decades ago that IT professionals of the twenty-first century would have to be multi-skilled and possess a combination of technical, business and interpersonal knowledge in order to adjust to new opportunities, properly analyse problems and implement business processes utilising new information technology (Farwell et al., 1992). However, rapid changes in the last few years have had a large impact on the knowledge and skills required of both IT personnel and the business side (Raju, 2014). It has been suggested that IT personnel should obtain business skills in addition to technical skills for the success of IT implementation and the overall organisational competitiveness (Yu-Yin et al., 2016). Moreover, due to these rapid changes IT manager needs a wide range of operational and strategic management capabilities to successfully face with the digital transformation making the job of IT managers particularly demanding in any organisation (Kappelman et al., 2018). The IT manager’s knowledge and skills are thus important factors in the business–IT relationship since differences here in individuals on both sides are often seen as the primary reason for misunderstanding between top managers and IT managers. Acquiring business and managerial skills by IT managers is an important element of achieving top management support (Indihar Štemberger et al., 2011) since the ability of the IT manager to communicate in business terms is crucial (Gerth and Peppard, 2016), eases the process of presenting the IT department as a means for realising business goals (Coughlan et al., 2005) and enables presenting ideas clearly (Ibrahim and Cigdem, 2018). Considering the knowledge-based theory, the shown impact of the quality of senior leadership on IT assimilation (Armstrong and Sambamurthy, 1999) and the influence of competencies on strategy formulation (Sveiby, 2001), we hypothesise:


The IT manager’s business knowledge and skills have a positive impact on the business orientation of the IT department.


The IT manager’s managerial knowledge and skills have a positive impact on the business orientation of the IT department.

Notwithstanding the above, it has been shown that IT personnel can successfully present and implement IT projects merely by possessing a wider range of skills and knowledge (Byrd and Turner, 2001) as they are often divided between service users who expect technical skills and top management who expect sufficient communication skills.

2.4 The perceived value of IT

Examining influence of IT on business value remains a key challenge for IT researchers (Lin et al., 2015; Luo et al., 2012). Due to important role of IT, it is particularly critical to present the value of investing in IT since understanding the impact of IT encourages ideas for future IT applications (Agarwal and Lucas, 2005). Therefore, several researchers have been motivated to understand the influence of applying IT within firms on improved organisational performance (Melville et al., 2004). The creation of business value in IT projects requires a high level of business–IT alignment practices (Vermerris et al., 2014), while the IT should represent an essential component of the strategy as technology by itself does not contribute to organisational performance, although it contributes as part of an overall system that improves the creation of economic value (Piccoli and Ives, 2005).

It is argued that IT enables business process reengineering, strategic alliances and competitive advantages (Avison et al., 1999) and can thus represent value to the organisation, particularly in supply-chain planning (Fuchs and Otto, 2015). In addition, IT generates business value by enabling business processes (Škrinjar and Trkman, 2013) and enables organisations to perform their functional activities better than the competition (Luo et al., 2012), reduces the cost per task, the time required per task, and meets a higher standard of quality (Raz and Goldberg, 2006). Therefore, organisations must develop systemic capabilities to enhance their IT business value by ensuring that IT and other organisational elements function together as a synergistic whole (Cao et al., 2016).

Research examining factors encouraging managers to form a business-to-business partnership (Tuten and Urban, 2001) revealed several categories ranked by importance, namely, a desire for lower costs, including less duplication of unnecessary work; providing increased services, including satisfying customer needs satisfactorily; enhancing competitive advantages; improving organisational performance, including market share and profitability; increasing the quality of products and services; and gaining different benefits from a partner including a reliable source of supply. These factors extended Mohr and Spekman’s (1994) model and denote the antecedents of the business-to-business partnership relationship since they signify the expectations a potential partner has regarding each particular partnering relationship (Tuten and Urban, 2001). Thus, we propose the following hypothesis:


The perceived value of IT positively influences the partnership between top management and IT personnel.

Nevertheless, if no benefits are expected from the partnership relationship then there is no intention to form a partnership. Thus, the most important antecedents of the partnership between organisations, namely expectations of lower costs, the increased quality of services, competitive advantages and increased profitability, were transferred to the business–IT relationship and used in the paper to form a construct of the perceived value of the IT as an important factor in the partnership relationship.

Figure 1 shows the conceptual model of the business–IT partnership relationship along with the proposed hypotheses.

To test the proposed hypotheses, seven constructs were defined: the business knowledge of the IT manager; the managerial knowledge of the IT manager; the technological knowledge of the IT manager; the perceived value of the IT; business-oriented IT personnel; technology-oriented IT personnel; and a partnership relation. The first four model constructs are exogenous latent variables, while the last three are endogenous latent variables.

3. Research methodology

3.1 Research instrument

A questionnaire for IT managers was developed to empirically test the proposed model. To ensure content validity, the questionnaire was based on previous findings in the literature (Byrd and Davidson, 2003; Ward and Mitchell, 2004) and previous research (Indihar Štemberger et al., 2011; Kovačič, 2001; Manfreda and Indihar Štemberger, 2014). Pretesting was conducted using ten semi-structured interviews with selected IT managers who were later also included in the study. Based on the pretesting phase, a set of measurement items used in previous research was modified in order to better align it with the context of the current study, namely items measuring the role of IT personnel and items measuring the knowledge and skills of IT managers were expanded and formed with more indicators. All latent variables in the model were measured by items using a seven-point Likert scale. All of these items along with a short description are presented in Tables AI and AII.

The construct knowledge and skills of the IT manager were thus measured by 16 variables (knl1–knl16). Furthermore, 13 variables were used to measure the construct role of IT personnel in assessing the importance of the different tasks (role1–role13). The construct perceived value of the IT was measured based on an extended Mohr and Spekman model (Tuten and Urban, 2001) using the antecedents of the business-to-business partnership relationship and transferring them to the business–IT context. Therefore, perceived value was measured by four variables identifying the importance of the IT (imp1–imp4). Finally, measures for successful business-to-business partnerships, that is commitment, coordination, interdependence and trust (Mohr and Spekman, 1994), were combined with measures for cross-functional partnership, namely mutual understanding, mutual trust, mutual involvement and conflict resolution (Tian et al., 2010) and used for the partnership construct in our research. Partnership was thus measured by 11 variables related to the relationship between top management and IT personnel (part1–part11).

3.2 Data collection and sample characteristics

The research question was empirically tested using data from medium and large Slovenian companies. For inclusion in the research, a company had to satisfy at least two of the following criteria: at least 50 employees; a net turnover exceeding EURO 8,800,000; and an asset value above EURO 4,400,000. According to the described legislative criteria, there were 1,495 companies and the IT managers from all of these companies were invited to participate in the research. Companies that completely outsourced all their IT activities or where no one was formally involved in IT were excluded from the research.

Altogether, a total of 221 IT managers agreed to participate in the research, representing a 14.8 per cent response rate, with all the data valid for the analysis. The data collection concluded in 2015. The respondents’ profile is shown in Table I.

3.3 Research methods

A combined exploratory and confirmatory approach was used in the research. In the field of information systems, exploratory techniques are generally applied for measurement purposes and the results of exploratory studies are later used in further confirmatory analysis (Koufteros, 1999). Exploratory factor analysis using SPSS 19.0 was thus undertaken to verify the construct validities of the measurement model. A principal axis factoring extraction method with a varimax rotation was used to examine whether the questionnaire items measure the defined model.

In the confirmatory analyses, structural equation modelling (SEM) and the LISREL 8.80 tool were used to empirically verify the model and the hypotheses. SEM as a confirmatory method is used to verify that the proposed relations between the latent variables and relations between the latent and observed variables are consistent with the empirical data (Diamantopoulos and Siguaw, 2000). Since it is a covariance-based method, SEM compares a covariance matrix that is generated from a particular sample with a covariance matrix that is generated by a proposed model (Wayment and Cordova, 2003).

4. Data analysis and results

4.1 Exploratory analysis

The purpose of the exploratory factor analysis was to examine the extent to which the items in the measurement instrument are related to the hypothesised latent constructs. Factor loadings for the variables included in the partnership model are presented in Tables AI and AII and are divided into two tables simply to allow a clearer presentation of the factors.

Considering the guidelines for identifying significant factor loadings based on sample size, the limit of 0.40 is appropriate for a sample size larger than 200, although values above 0.50 are desired to also ensure practical significance (Hair et al., 1998). Therefore, loadings greater than 0.50 are used to represent a specific factor.

As evident from Tables AI and AII, three factors were identified measuring knowledge and skills, namely Factor 2 that consists of several managerial skills and therefore represents managerial knowledge and skills (MANknl), Factor 4 that includes variables measuring technological knowledge and skills (TECknl), while Factor 7 represents business knowledge and skills (BUSknl). The fourth, namely Factor 6, includes variables measuring the importance and value of the IT and therefore represents the perceived value of the IT (ValIS). Considering endogenous variables, Factor 1 consists of several variables measuring the partnership relationship, and therefore represents the business–IT partnership (PART). Furthermore, two factors measure the role of the IT department, namely Factor 3 that represents the business role of the IT department (BUSori), while Factor 5 represents the technological and supportive role of the IT department (TECori). Just two items loaded on to Factor 8 and this factor was therefore not included in the SEM. The item role4 was included in Factor 5 since it also represents the technological role of the IT department.

The value of the calculated Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy is above 0.8, thus, indicating a reliable factor analysis since values above 0.5 are acceptable (Kaiser, 1974) and values greater than 0.8 are considered as very good (Hutcheson and Sofroniou, 1999). Furthermore, Cronbach’s α was calculated to determine the internal consistency reliability of the identified factors. Values above 0.7 are generally accepted (Kline, 1999); however, in exploratory studies values below 0.7 and above 0.50 are also considered to be acceptable (Hair et al., 1998; Nunnally, 1967).

As Table II shows, Cronbach’s α for all identified factors in the proposed partnership model is above the recommended value, signifying the high reliability of the identified factors.

We also tested the data for the common method bias using Harman’s single-factor test. The test showed that the common method bias does not exist, since in exploratory factor analysis using unrotated solution a single factor explained only 32 per cent and not the majority of the variance. It is also possible to assess the convergent and discriminant validity of the measures using exploratory analysis since in general convergent and discriminant validity are achieved when measurement items load high onto their respective constructs and low onto other constructs (Yi et al., 2006), although it has been claimed that exploratory factor models do not provide an explicit test statistic for assessing convergent and discriminant validity (Koufteros, 1999; O’Leary-Kelly and Vokurka, 1998; Segars and Grover, 1993) as constructs represented by a set of indicators do not correspond directly to factors in exploratory analysis (Gerbing and Anderson, 1988). Therefore, convergent and discriminant validity is assessed in the confirmatory analysis below.

4.2 Confirmatory analysis using SEM

Based on the confirmatory analysis, some indicators were removed from the original model since their loadings were small and therefore do not represent reliable measures of the latent variables. Measurement items with completely standardised loadings below 0.6 were dropped from the modified model. Four items were thus dropped, namely role4, role5, role6 and role7. These modifications merely slightly improved the model fit and not the model itself as they only drop some measures for two latent variables, namely BUSori and TECori. Figure 2 shows the path diagram for the partnership model with the completely standardised parameter estimates. Parameters were estimated using a maximum likelihood method as a default estimation method in Lisrel.

Before interpreting the results, the model fit was examined since it represents the consistency of a hypothesised model and the data (Diamantopoulos and Siguaw, 2000). More specifically, testing the model fit presents the statistical process of comparing the covariance in the observed data with the expected covariance in the hypothesised model (Iriondo et al., 2003).

4.2.1 Overall fit assessment

Several fit indices have been developed to measure the overall model fit; however, there is no agreement on the overall model fit index (Hayduk, 1996). These indices depend on the estimation procedure, the sample size and model complexity (Byrne, 1998) and should be used with caution (Mulaik et al., 1989). Therefore, in Table III fit indices that are generally used with reference values are presented and explained below the table.

All indices indicate a good overall model fit, except the p-value for χ2 statistics and goodness-of-fit index (GFI). However, in the large samples the χ2 statistic is often significant (smaller than 0.05), even though the model has a good fit (James et al., 1982; Marsh et al., 1988), especially if the sample size exceeds 200 respondents (Hair et al., 1998). Furthermore, in large samples almost any model will be rejected considering just the p-value for χ2 statistics (Long, 1983) and therefore use of the χ2 statistic is appropriate for sample sizes between 100 and 200 (Hair et al., 1998). Thus, χ2 statistics in comparison with degrees of freedom is used to test the model (Diamantopoulos and Siguaw, 2000). A model fit is achieved when the ratio between the χ2 statistics and degrees of freedom is lower than 5 (Wheaton et al., 1977), while more restrictive rules suggest that the ratio should be lower than 3 (Kline, 2011) or even below 2 (Carmines and McIver, 1981; Hair et al., 1998).

The next index that is below the reference value is GFI. However, it has been claimed that the GFI index also depends on the sample size (Marsh et al., 1988) and furthermore that GFI is particularly useless in large samples and when the number of indicators is large, so its use should be reconsidered (Sharma et al., 2005). It has also been claimed that there is no absolute cut-off level for accepting GFI, although higher values indicate a better fit (Hair et al., 1998).

The last index that is close to the recommended value is the standardised root mean square residual (standardised RMR) where values below 0.05 are indicators of a good fit (Browne and Cudeck, 1993; Byrne, 1998) or values close to 0.08 (Hu and Bentler, 1998), although it has been claimed that values below 0.10 also indicate a good model fit (Kline, 2005).

The next index in the table is the root mean square error of approximation (RMSEA). The index is considered one of the most informative fit indices (Diamantopoulos and Siguaw, 2000), yet the recommended values for this index vary. It has been claimed that a reference value for a good model fit is around 0.06 (Hu and Bentler, 1999) or below 0.08 (Hair et al., 1998; Jarvenpaa et al., 2000), while some suggest that values below 0.05 indicate a good fit, values below 0.08 a reasonable fit, values between 0.08 and 0.10 a mediocre fit and values above 0.10 a poor fit (Browne and Cudeck, 1993; Diamantopoulos and Siguaw, 2000; MacCallum et al., 1996).

The expected cross-validation index (ECVI) focuses on the overall error. There is no reference value for the ECVI; however, it is suggested to select the model with the smallest ECVI and therefore the value of the index should be smaller than the value of the saturated and independence models (Diamantopoulos and Siguaw, 2000). The same is true for Akaike’s information criterion (AIC) and the consistent AIC (CAIC). Furthermore, the normed fit index (NFI), non-normed fit index (NNFI), comparative fit index (CFI) and incremental fit index (IFI) measure the difference of fitting the model compared to the baseline model, where values close to 1 represent a good fit (Jöreskog and Sörbom, 1993).

It is claimed that the χ2 test, standardised RMR, GFI and CFI, RMSEA and ECVI indices satisfy the criteria to assess the overall model fit (Diamantopoulos and Siguaw, 2000); however, the χ2 per degree of freedom, CFI and NNFI are generally used to assess the model fit (Koufteros, 1999).

Considering the presented indices and underlying limitations, it is possible to conclude that the model has a good overall fit.

4.2.2 Assessing the measurement model

Assessment of the measurement model refers to the relationships between the latent variable and its indicators with the purpose of determining the validity and reliability of the measures used to represent the latent variables. Validity signifies whether an indicator measures what it is designed to measure, while reliability refers to the consistency of the measurement signifying whether a set of construct indicators is consistent in their measurements (Hair et al., 1998).

To achieve the validity of the indicators, the relationship between each latent variable and its indicators should be significantly different from zero. Table IV presents indicators for endogenous latent variables with Lisrel estimates and t-values. Since the t-values exceed 2.58, all the relations are significantly different from zero (0.01 significance level), and thus, construct validity is achieved.

Likewise, Table V presents indicators for the exogenous latent variables with the estimates and t-values. These t-values are also larger than 2.58 and therefore construct validity is achieved.

In both tables, completely standardised loadings are also presented. In the completely standardised solution, both measurable indicators and latent variables are standardised and it is therefore possible to compare the validity of different indicators (Diamantopoulos and Siguaw, 2000). The tables show that controlling the performance of IT projects is the most valid indicator for the business-oriented role of IT personnel and commitment to a good relationship is the most valid indicator of a partnership, while top management’s reliance on IT personnel is the least valid indicator of a partnership.

Tables IV and V also present the squared multiple correlation (R2) for the indicators in the partnership model representing the share of variance in the indicator explained by the latent variable (Diamantopoulos and Siguaw, 2000), where high values signify a high level of reliability.

Besides the individual indicator reliability, the composite reliability (CR) measuring the reliability of the constructs was calculated. Values for CR should exceed 0.6 (Bagozzi and Yi, 1988), although a commonly cut-off value for acceptable reliability is 0.70 (Hair et al., 1998). In addition, average variance extracted (AVE) that refers to the amount of variance that is captured by a construct in relation to the amount of variance that is caused by the measurement error (Fornell and Larcker, 1981) was also calculated. AVE values should exceed 0.50, signifying that the variance due to the measurement error is smaller than the variance captured by the construct. Both measures are presented in Table VI. In the proposed model, all constructs highly exceed the recommended values for CR, and therefore, the indicators of each construct provide a reliable measurement. Furthermore, with one exception, AVE is larger than 0.5 for all latent variables, indicating that more than half the variance in the indicators is captured by the underlying latent variable. The only exception is BUSknl due to the lower squared multiple correlations of its four indicators. However, the value of AVE for BUSknl is still close to the recommended value.

The last assessment of the measurement model refers to discriminant validity. It presents a test of whether the latent variable explains the variance of its own indicators better than the variance of other latent variables. A discriminant validity test using the Fornell–Larcker criterion (Fornell and Larcker, 1981) is presented in Table VI. According to that criterion, AVE values are compared to the squared correlation between each pair of latent variables.

Since the AVE values on the diagonal for each latent variable are higher than the squared correlation between that latent variable and all other latent variables, discriminant validity is confirmed.

4.2.3 Assessment of the structural model

The last part of the model fit assessment is the structural model fit, which mainly refers to the significance of the estimated coefficients in the structural part of the model (Hair et al., 1998). The purpose is to examine whether the data support the theoretical relationships in the conceptualisation model (Diamantopoulos and Siguaw, 2000). Therefore, the signs of the parameters representing the relationship between latent variables, the statistical significance and magnitude of the estimated parameters and the squared multiple correlation for the structural equations were examined.

In the partnership model, the signs of all parameters are consistent with the hypothesised relationships between the latent variables. Furthermore, all parameters are statistically significant at the 0.01 significance level, except MANknl which is significant at the 0.05 level. Considering the relative impact of the estimated parameters, it is evident from Figure 2 that BUSori has the largest impact on PART. Finally, with the exception for TECori, where R2 is just 0.06, the R2 for other endogenous variables are quite high, namely 0.36 for BUSori and 0.48 for PART. The latter indicates that the independent latent variables (BUSori, TECori and valIS) explain 48 per cent of the variance in the PART latent variable.

5. Discussion

5.1 Findings and implications

Considering the overall model fit, the measurement model fit and the structural model fit, the confirmatory analysis confirmed all six proposed hypotheses. The results indicate several important findings of the research. The first is the definition of the term partnership in the context of the business–IT relationship. The partnership construct was developed using interdisciplinary studies and transferred to the business–IT relationship. The research confirmed that the most influential items are respect of the top management, trust, mutual reliance, long-term cooperation, commitment to a good relationship and open and honest communication. Our study thus upgrades the findings from research examining the influence of social facilitators on collaboration outcomes by adding more facilitators of the partnership relationship to the existing social facilitators already conceptualised, namely trust, commitment and congruence (Malmström and Johansson, 2016). These facilitators relate to the general belief of reciprocity in social exchange theory since higher levels of trust make partners willing to take risks; a high level of commitment enables resources to be invested to strengthen the relationship, while a high level of congruence enables the collaborating partners to agree on the outcome of the collaboration.

However, the most valuable finding is that a partnership relationship can be achieved through business-oriented IT personnel and the perceived value of the IT. It was found that these two factors have the largest positive influence on the partnership relationship. The research also shows that the technology orientation of IT personnel has a negative influence on the business–IT partnership, although the impact of that influence is relatively small. The paper thus upgrades a recent study (Kuegler et al., 2015) by adding a further counterweight to the technological-centric approach to IT research. Nevertheless, the finding that empathising appropriate IT infrastructure, user support and concern for IT security as indicators of technology-oriented IT personnel negatively influence the partnership confirms the claim that the IT department is perceived by other executives only as a technical service provider (Gerth and Peppard, 2016) and may worsen the IT department’s credibility (Alaceva and Rusu, 2015). However, the factor technology-oriented IT department remains quite unexplained in the research, suggesting additional items besides technological knowledge and skills are influencing it, although it was confirmed that giving a preference to technological knowledge and neglecting business and managerial knowledge do not improve the business–IT relationship. Still, it was shown (Allen et al., 2008) that organisations prefer IT personnel who possess a wider knowledge set.

IT managers should therefore improve their managerial knowledge and particularly their business knowledge and skills since this should shift their attention more towards a business-oriented IT department. This does not mean that technology is not important, but emphasises that merely having technology-oriented IT departments that neglect the importance of the business role is creating the gap between IT personnel and top management. Our findings thus confirm previous studies which stressed that business and managerial knowledge and skills are important for IT managers (Indihar Štemberger et al., 2011; Parolia et al., 2007) and upgrade them with the partnership concept.

IT managers should especially improve their knowledge and skills related to risk management and know the individual functional areas because these have been found to be the most influential measures of business knowledge and skills. Similarly, knowledge of project management and communication and coordination skills should be improved as the most influential measures of managerial knowledge. Nevertheless, effective communication increases the level of knowledge sharing and understanding across business and IT units and thus promotes alignment (Charoensuk et al., 2014) and organisations should take organisational forms that enable proper knowledge exploitation to occur (Nonaka et al., 2014). Eventually, an organisation has to create a culture of communication by displaying trust and openness in which business and IT managers are able to communicate more efficiently (Alaceva and Rusu, 2015). The issue is not merely to properly organise IT department, but rather to create the environment for integrating enterprise-wide knowledge (Peppard, 2018).

Despite the fact that top management’s characteristics are claimed to be particularly important for success in implementing IT (Lin et al., 2014), our study focused on the IT management side and thus presented factors IT managers should be aware of in order to achieve a partnership relationship with the top management. Since top management’s characteristics are hard to change (Benlian and Haffke, 2016), IT managers should primarily focus on the factors presented above, even though it is claimed that top management should have an active part in aligning the IT with the organisational strategy and using the IT to create organisational value (Krotov, 2015). Still, collaborative behaviour has been found to exert an important impact on an organisation’s success and to be an important factor to motivate workers to exchange their knowledge (Koriat and Gelbard, 2014).

Assuming that knowledge is a private good, an owner has to decide whether to share it. Therefore, to encourage an owner to share their knowledge in terms of social exchange theory the owner must be convinced it is worth exchanging it for some kind of resource (Hall, 2003). The central tenet of social exchange theory is that comparing rewards with the costs of maintaining the relationship is a key criterion for determining whether to continue the relationship (Jeong and Oh, 2017). However, costs and rewards in social exchange are affected by the possession of knowledge. It is therefore even more important that IT managers possess proper business knowledge. It may be concluded that mere knowledge congruence between the IT manager and top management enables an efficient social exchange by reducing the costs of the exchange. Two sets of knowledge are “traded as a straightforward swap” (Hall, 2003). After all, knowledge sharing is perceived as a critical support mechanism in knowledge-intensive activities (Zahedi et al., 2016).

Finally, in order to improve the business orientation of the IT department, IT managers should emphasise strategic IT planning and focus on the importance of controlling the performance of IT projects. In contrast, simply emphasising the establishment and provision of appropriate IT infrastructure as the main indicator of a technology-oriented IT department and simultaneously neglecting the importance of the business role lead to IT personnel being treated merely as a supporting function and not a strategic resource. The latter should be avoided since the IT department should be presented as a mean for achieving business goals and not as a support department (Coughlan et al., 2005). Furthermore, IT managers should also make particular efforts to assure that the IT will enable successful business performance and will enable a competitive advantage to be obtained since this has been found to be an influential measure of the perceived value of the IT. Yet simply ensuring the proper utilisation of both IT assets and IT management has a critical role in the creation of value (Wang et al., 2015).

In response to the era of digitalisation, many companies are now replacing IT managers with chief digital officers to implement digital initiatives mainly because new digital technologies require different skills comparing to prior waves of transformative technologies, and therefore, IT managers may not be qualified enough to take the responsibility of digital transformation (Singh and Hess, 2017). However, the job specification for chief digital officers is actually a mirror of the job description for the IT manager (Gerth and Peppard, 2016), while it is also claimed that the chief digital officer positions may only be a temporary phenomenon (Singh and Hess, 2017). The latter is a consequence of the poor relationship between top management and IT managers. Therefore, the focus in companies should primarily be on creating a genuine partnership between business and IT, and not merely creating new job titles. After all, new technology will always be emerging, while non-technological challenges and issues remain the same.

5.2 Limitations and further research

The research findings are constrained by the sample being limited to a single country. Moreover, the study results do not present the situation of a specific industrial sector, although the purpose of this paper was to confirm the hypotheses in general and not as applied to any particular industrial sector.

The research also shows that further study of the business–IT partnership relationship is justified and still necessary. Since the perceived value of the IT has been found to be an important factor for creating a partnership relationship, future research should analyse this factor in detail and present the factors that influence it. Similarly, because the technology-oriented IT department was not thoroughly explained by the presented indicators, it is suggested that this factor should be studied in greater detail.

Although the discriminant validity of the proposed model was confirmed, it should be mentioned that the correlation between managerial and business knowledge is relatively high, suggesting that either further research on these two constructs should be conducted or that both constructs should be considered as a single construct only. Nonetheless, the distinction between managerial and business knowledge is quite vague. This study tried to make the distinction between them and their individual impacts even clearer, yet the decision to have two separate constructs is discussable and should also be examined. Moreover, since knowledge congruence between the IT manager and top management was found to be important, future research should focus on examining the level of technical knowledge possessed by top management.

Further research could also examine differences between industry sectors and the business–IT partnership relationship within different industry sectors. The study could also be repeated in a different region to cross-validate it. Further research examining the influence of culture on the business–IT relationship might also lead to an important improvement of the presented partnership model. More specifically, testing whether cultures that emphasise the importance of hierarchy and leadership differ from cultures stressing the importance of a flat organisational structure and collaboration could provide valuable information about the creation of a business–IT partnership. Nevertheless, future research should also test the applicability of this research to the relationship in other spheres within companies, that is, the relationship between top management and other non-business spheres in the company.

6. Conclusion

The paper presented the term partnership in the business–IT context and contributed to understanding the main factors important for achieving a partnership relationship between top management and IT personnel. The results confirm that the business orientation of the IT department and the perceived value of the IT have a positive influence on the partnership, while merely a technology-oriented IT department does not contribute to the partnership relationship. Furthermore, the paper also presented the prerequisites that lead to business orientation of the IT department. The results are important for IT managers and business managers in order to improve their mutual understanding, and consequently the relationship between them. Establishing a strong business–IT relationship in the era of digital transformation is crucial since IT is rapidly expanding in everyday life and business.


Conceptual model of the business–IT partnership relationship

Figure 1

Conceptual model of the business–IT partnership relationship

Path diagram of the partnership model

Figure 2

Path diagram of the partnership model

Profile of the respondents

Number of respondents
Method of participation
Paper questionnaire 45.2
Online questionnaire 54.8
Company hierarchy – position of IT manager
Member of management board 12.7
Directly subordinated to the top management 60.5
Indirectly subordinated to the top management 26.8
Organisation of IT department
Separate IT department 43.4
IT department is part of another organisational unit 23.3
Only individuals involved in IT 26.0
No formal involvement 7.3

Scale reliability of factors in the partnership model

Factor Description Label Cronbach’s α
1 Partnership relation PART 0.956
2 Managerial knowledge and skills MANknl 0.897
3 Business orientation of IT personnel BUSori 0.875
4 Technological knowledge and skills TECknl 0.846
5 Technological orientation of IT personnel TECori 0.737
6 Perceived value of IT valIS 0.849
7 Business knowledge and skills BUSknl 0.786

Fit indices of the partnership model

Fit indices Model value Reference value Overall model fit
χ2 1,281.41 Not applicable
p-value for χ2 0.000 >0.05 No
χ2/df 1.962 <5.00 (3.00) Yes
Standardised RMR 0.084 <0.10 (0.05) Acceptable
RMSEA 0.069 <0.10 (0.05) Yes
ECVI 7.109 <ECVI saturated (7.230)
<ECVI independence (96.19)
AIC 1,457.41 <AIC saturated (1,482.00)
<AIC independence (19,883.89)
CAIC 1,838.26 <CAIC saturated (4,688.96)
<CAIC independence (19,883.89)
NFI 0.934 >0.90 Yes
NNFI 0.963 >0.90 Yes
CFI 0.966 >0.90 Yes
GFI 0.752 >0.90 No
IFI 0.966 >0.90 Yes

Validity and reliability assessment of the partnership model – Lambda-Y

Latent variable Indicator Estimate t-value Completely standardised loadings R2
BUSori role8 0.69 11.68 0.76 0.57
role9 0.81 11.94 0.77 0.59
role10 0.71 11.52 0.75 0.56
role11 0.76 10.07 0.67 0.45
role12 0.88 12.51 0.80 0.64
role13 0.91 13.99 0.87 0.76
PART part1 0.77 12.43 0.77 0.59
part2 0.49 10.53 0.67 0.46
part3 0.85 15.65 0.91 0.82
part4 0.72 14.57 0.86 0.74
part5 0.87 15.44 0.90 0.81
part6 0.79 13.82 0.83 0.69
part7 0.74 12.88 0.79 0.62
part8 0.77 14.34 0.85 0.73
part9 0.87 15.79 0.91 0.83
part10 0.86 14.94 0.88 0.77
part11 0.91 11.22 0.71 0.50
TECori role1 1.12 15.92 0.94 0.88
role2 0.81 11.62 0.73 0.54
role3 0.85 13.18 0.81 0.66

Validity and reliability assessment of the partnership model – Lambda-X

Latent variable Indicator Estimate t-value Completely standardised loadings R2
MANknl knl7 0.75 12.69 0.77 0.59
knl8 0.81 12.59 0.77 0.59
knl9 0.91 14.02 0.82 0.68
knl10 0.75 12.65 0.77 0.59
knl11 0.69 13.74 0.81 0.66
knl12 0.53 10.46 0.67 0.45
BUSknl knl13 0.74 9.84 0.67 0.44
knl14 0.95 11.19 0.73 0.54
knl15 0.77 9.99 0.67 0.45
knl16 0.77 9.69 0.66 0.43
ValIS imp1 0.75 11.10 0.70 0.49
imp2 0.78 10.94 0.69 0.48
imp3 1.03 14.07 0.83 0.69
imp4 1.16 16.57 0.92 0.85
TECknl knl1 1.12 10.10 0.66 0.44
knl2 1.29 13.26 0.81 0.66
knl3 1.28 14.63 0.87 0.75
knl4 1.02 12.00 0.75 0.57

Reliability and discriminant validity of the partnership model

Discriminant validity
CR AVE Latent variable BUSori PART TECori MANknl BUSknl ValIS TECknl
0.898 0.595 BUSori 0.595
0.960 0.687 PART 0.343 0.687
0.869 0.692 TECori 0.001 0.033 0.692
0.897 0.593 MANknl 0.269 0.187 0.004 0.593
0.777 0.466 BUSknl 0.340 0.219 0.001 0.449 0.466
0.869 0.627 ValIS 0.120 0.271 0.000 0.264 0.298 0.627
0.857 0.604 TECknl 0.014 0.016 0.058 0.076 0.019 0.009 0.604

Factor loadings and item description for the exogenous variables

Factor (KMO=0.900)
Short description 1 2 3 4 5 6 7 8
imp1 Enabling quality services 0.367 0.112 0.179 −0.020 0.186 0.665 0.033 −0.014
imp2 Enabling operations with lower costs 0.212 0.178 0.160 0.037 0.030 0.709 0.233 0.057
imp3 Enabling successful business performance 0.289 0.164 0.147 −0.033 −0.074 0.784 0.073 0.016
imp4 Enabling competitive advantage 0.328 0.224 0.207 0.035 0.008 0.780 0.102 0.021
knl1 Programming −0.239 −0.241 0.050 0.633 −0.002 −0.081 −0.085 0.341
knl2 Operating systems −0.121 −0.116 −0.029 0.826 0.236 −0.060 −0.039 −0.085
knl3 Databases −0.123 −0.122 −0.048 0.867 0.026 0.031 0.044 0.133
knl4 Telecommunications and networks −0.096 0.019 −0.107 0.841 0.057 0.059 0.063 −0.033
knl5 IT solutions (ERP) on the market 0.076 0.248 0.018 0.320 0.231 0.100 0.369 0.417
knl6 IT governance frameworks 0.064 0.374 0.132 −0.030 −0.026 0.005 0.301 0.662
knl7 Planning and organising 0.223 0.719 0.129 −0.068 0.039 0.089 0.199 0.166
knl8 Motivation 0.190 0.724 0.090 −0.091 0.028 0.255 0.160 0.032
knl9 Project management 0.214 0.791 0.083 −0.120 −0.003 0.128 0.167 0.079
knl10 Team working 0.144 0.784 0.181 0.044 −0.072 0.183 0.064 0.097
knl11 Communication and coordination 0.197 0.791 0.178 −0.143 0.055 0.024 0.166 −0.072
knl12 Knowing business processes 0.284 0.530 0.090 −0.072 0.003 0.032 0.370 −0.092
knl13 Knowing relevant legislation 0.204 0.217 0.002 0.045 0.081 0.084 0.723 −0.046
knl14 Risk management 0.170 0.373 0.229 −0.147 0.037 0.106 0.577 −0.024
knl15 Knowing individual functional areas 0.106 0.231 0.263 0.074 0.056 0.131 0.673 −0.043
knl16 Knowing business competitors 0.259 0.216 0.098 0.006 −0.210 0.154 0.615 0.235

Factor loadings and item description for the endogenous variables

Short description 1 2 3 4 5 6 7 8
part1 Independent IT personnel 0.718 0.296 0.252 0.016 −0.131 0.147 −0.031 −0.101
part2 Top management relies on IT personnel 0.573 0.280 0.319 −0.037 −0.027 0.181 0.081 −0.079
part3 Top management respects the work of IT personnel 0.894 0.125 0.150 −0.022 0.055 0.065 0.144 −0.056
part4 Trusting IT personnel to perform obligations in a quality way 0.839 0.130 0.164 −0.022 0.019 0.076 0.144 −0.057
part5 Mutual reliance 0.831 0.198 0.189 −0.083 0.033 0.085 0.208 −0.098
part6 Involvement in the company’s development 0.780 0.111 0.240 −0.099 −0.067 0.272 0.055 0.057
part7 Aligned objectives 0.725 0.114 0.171 −0.135 −0.037 0.265 0.207 0.097
part8 Long-term cooperation 0.843 0.125 0.052 −0.101 −0.025 0.153 0.124 0.025
part9 Commitment to a good relationship 0.890 0.141 0.082 −0.108 0.003 0.101 0.096 0.033
part10 Open and honest communication 0.844 0.177 0.072 −0.151 −0.007 0.119 0.114 −0.017
part11 Involvement in formulating business strategies 0.711 0.045 0.180 −0.090 −0.153 0.206 0.013 0.125
role1 Establishing the appropriate infrastructure −0.085 −0.056 0.040 0.089 0.870 0.039 −0.044 0.134
role2 Providing user support 0.004 0.118 0.037 0.034 0.832 0.072 0.033 0.051
role3 Concern for IT security −0.151 −0.031 0.204 0.066 0.819 −0.043 −0.064 0.195
role4 Developing IT solutions −0.062 −0.085 0.200 0.180 0.317 0.088 −0.230 0.634
role5 Cooperating with external suppliers 0.095 −0.022 0.145 0.146 0.520 −0.015 0.267 −0.230
role6 Identifying IT needs −0.031 0.221 0.608 0.139 0.259 0.067 −0.189 0.111
role7 Formulating IT architecture −0.094 0.023 0.512 0.134 0.308 −0.061 −0.145 0.433
role8 On-time concluding IT projects 0.262 0.071 0.686 −0.157 0.047 0.099 0.288 0.006
role9 Proper IT organisation 0.275 0.187 0.654 −0.132 0.202 0.100 0.161 0.062
role10 Implementing projects in a cost-specified range 0.330 0.108 0.719 0.037 0.100 0.169 0.089 −0.037
role11 Improving and redesigning business processes 0.210 0.024 0.627 −0.019 −0.106 0.233 0.292 0.113
role12 Strategic IT planning 0.382 0.223 0.664 −0.062 −0.019 0.147 0.089 0.010
role13 Controlling the performance of IT projects 0.318 0.136 0.763 −0.137 0.061 0.130 0.103 0.115

Appendix 1

Table AI

Appendix 2

Table AII


Agarwal, R. and Lucas, H.C. Jr (2005), “The information systems identity crisis: focusing on high-visibility and high-impact research”, MIS Quarterly, Vol. 29 No. 3, pp. 381-398.

Alaceva, C. and Rusu, L. (2015), “Barriers in achieving business/IT alignment in a large Swedish company: what we have learned?”, Computers in Human Behavior, Vol. 51, Part B, pp. 715-728.

Allen, M.W., Armstrong, D.J., Reid, M.F. and Riemenschneider, C.K. (2008), “Factors impacting the perceived organizational support of IT employees”, Information & Management, Vol. 45 No. 8, pp. 556-563.

Armstrong, C.P. and Sambamurthy, V. (1999), “Information technology assimilation in firms: the influence of senior leadership and IT infrastructures”, Information Systems Research, Vol. 10 No. 4, pp. 304-327.

Avison, D.E., Cuthbertson, C.H. and Powell, P. (1999), “The paradox of information systems: strategic value and low status”, The Journal of Strategic Information Systems, Vol. 8 No. 4, pp. 419-445.

Bagozzi, R.P. and Yi, Y. (1988), “On the evaluation of structural equation models”, Journal of the Academy of Marketing Science, Vol. 16 No. 1, pp. 74-94.

Baker, E.W. and Niederman, F. (2014), “Integrating the IS functions after mergers and acquisitions: analyzing business–IT alignment”, The Journal of Strategic Information Systems, Vol. 23 No. 2, pp. 112-127.

Barroso-Méndez, M.J., Galera-Casquet, C., Seitanidi, M.M. and Valero-Amaro, V. (2016), “Cross-sector social partnership success: a process perspective on the role of relational factors”, European Management Journal, Vol. 34 No. 6, pp. 674-685.

Bassellier, G., Reich, B.H. and Benbasat, I. (2001), “Information technology competence of business managers: a definition and research model”, Journal of Management Information Systems, Vol. 17 No. 4, pp. 159-182.

Benlian, A. and Haffke, I. (2016), “Does mutuality matter? Examining the bilateral nature and effects of CEO–CIO mutual understanding”, The Journal of Strategic Information Systems, Vol. 25 No. 2, pp. 104-126.

Berman, S.J. (2012), “Digital transformation: opportunities to create new business models”, Strategy & Leadership, Vol. 40 No. 2, pp. 16-24.

Bharadwaj, A., El Sawy, O.A., Pavlou, P.A. and Venkatraman, N.V. (2013), “Digital business strategy: toward a next generation of insights”, MIS Quarterly, Vol. 37 No. 2, pp. 471-482.

Blau, P.M. (1964), Exchange and Power in Social Life, Transaction Publishers, New Brunswick, NJ.

Bondar, S., Hsu, J.C., Pfouga, A. and Stjepandić, J. (2017), “Agile digital transformation of system-of-systems architecture models using Zachman framework”, Journal of Industrial Information Integration, Vol. 7, pp. 33-43, available at:

Brass, D.J., Galaskiewicz, J., Greve, H.R. and Tsai, W. (2004), “Taking stock of networks and organizations: a multilevel perspective”, Academy of Management Journal, Vol. 47 No. 6, pp. 795-817.

Browne, M.W. and Cudeck, R. (1993), “Alternative ways of assessing model fit”, in Bollen, K.A. and Long, J.S. (Eds), Testing Structural Equation Models, SAGE Publications, Newbury Park, CA, pp. 136-162.

Byrd, T.A. and Davidson, N.W. (2003), “Examining possible antecedents of IT impact on the supply chain and its effect on firm performance”, Information & Management, Vol. 41 No. 2, pp. 243-255.

Byrd, T.A. and Turner, D.B. (2001), “An exploratory analysis of the value of the skills of IT personnel: their relationship to IS infrastructure and competitive advantage”, Decision Sciences, Vol. 32 No. 1, pp. 21-54.

Byrne, B.M. (1998), Structural Equation Modeling with LISREL, PRELIS, and SIMPLIS: Basic Concepts, Applications, and Programming, Lawrence Erlbaum Associates, Mahwah, NJ.

Cao, G., Duan, Y., Cadden, T. and Minocha, S. (2016), “Systemic capabilities: the source of IT business value”, Information Technology & People, Vol. 29 No. 3, pp. 556-579.

Carmines, E.G. and McIver, J.P. (1981), “Analyzing models with unobserved variables: analysis of covariance structures”, in Bohrnstedt, G.W. and Borgatta, E.F. (Eds), Social Measurement: Current Issues, SAGE Publications, Beverly Hills, CA, pp. 65-115.

Chan, Y.E. and Reich, B.H. (2007), “IT alignment: an annotated bibliography”, Journal of Information Technology, Vol. 22 No. 4, pp. 316-396.

Charoensuk, S., Wongsurawat, W. and Khang, D.B. (2014), “Business–IT alignment: a practical research approach”, The Journal of High Technology Management Research, Vol. 25 No. 2, pp. 132-147.

Chatham, R. and Patching, K. (2000), Corporate Politics for IT Managers: How to Get Streetwise, Butterworth-Heinemann, Boston, MA.

Chen, L. (2010), “Business–IT alignment maturity of companies in China”, Information & Management, Vol. 47 No. 1, pp. 9-16.

Cheng, J.-H. (2011), “Inter-organizational relationships and information sharing in supply chains”, International Journal of Information Management, Vol. 31 No. 4, pp. 374-384.

Chris, G. (2005), “IT governance – are boards and business executives interested onlookers or committed participants?”, Australian Accounting Review, Vol. 15 No. 3, pp. 5-10.

Chun, M. and Mooney, J. (2009), “CIO roles and responsibilities: twenty-five years of evolution and change”, Information & Management, Vol. 46 No. 6, pp. 323-334.

Conner, K.R. and Prahalad, C.K. (1996), “A resource-based theory of the firm: knowledge versus opportunism”, Organization Science, Vol. 7 No. 5, pp. 477-501.

Coughlan, J., Lycett, M. and Macredie, R.D. (2005), “Understanding the business–IT relationship”, International Journal of Information Management, Vol. 25 No. 4, pp. 303-319.

Cropanzano, R. and Mitchell, M.S. (2005), “Social exchange theory: an interdisciplinary review”, Journal of Management, Vol. 31 No. 6, pp. 874-900.

Diamantopoulos, A. and Siguaw, J.A. (2000), Introducing LISREL, SAGE Publications, London.

Doll, W.J. and Ahmed, M.U. (1983), “Diagnosing and treating the credibility syndrome”, MIS Quarterly, Vol. 7 No. 3, pp. 21-32.

Dumeresque, D. (2014), “The chief digital officer: bringing a dynamic approach to digital business”, Strategic Direction, Vol. 30 No. 1, pp. 1-3.

Earl, M.J. and Feeney, D.F. (1994), “Is your CIO adding value?”, Sloan Management Review, Vol. 35 No. 3, pp. 11-20.

Enns, H.G., Huff, S.L. and Golden, B.R. (2003), “CIO influence behaviors: the impact of technical background”, Information & Management, Vol. 40 No. 5, pp. 467-485.

Farwell, D.W., Kuramoto, L., Lee, D., Trauth, E.M. and Winslow, C. (1992), “A new paradigm for IS”, Information Systems Management, Vol. 9 No. 2, pp. 7-14.

Fornell, C. and Larcker, D.F. (1981), “Evaluating structural equation models with unobservable variables and measurement error”, Journal of Marketing Research, Vol. 18, pp. 39-50.

Fuchs, C. and Otto, A. (2015), “Value of IT in supply chain planning”, Journal of Enterprise Information Management, Vol. 28 No. 1, pp. 77-92.

Gantz, J. (1997), “E-commerce puts IS in the fast lane”, Computerworld, Vol. 31 No. 5, p. 33.

Gerbing, D.W. and Anderson, J.C. (1988), “An updated paradigm for scale development incorporating unidimensionality and its assessment”, Journal of Marketing Research, Vol. 25, pp. 186-192.

Gerow, J.E., Thatcher, J.B. and Grover, V. (2015), “Six types of IT–business strategic alignment: an investigation of the constructs and their measurement”, European Journal of Information Systems, Vol. 24 No. 5, pp. 465-491.

Gerth, A.B. and Peppard, J. (2016), “The dynamics of CIO derailment: how CIOs come undone and how to avoid it”, Business Horizons, Vol. 59 No. 1, pp. 61-70.

Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C. (1998), Multivariate Data Analysis, 5th ed., Prentice-Hall, Upper Saddle River, NJ.

Hall, H. (2003), “Borrowed theory”, Library & Information Science Research, Vol. 25 No. 3, pp. 287-306.

Hayduk, L. (1996), LISREL Issues, Debates, and Strategies, Johns Hopkins University Press, Baltimore, MD.

Hinings, B., Gegenhuber, T. and Greenwood, R. (2018), “Digital innovation and transformation: an institutional perspective”, Information and Organization, Vol. 28 No. 1, pp. 52-61.

Homans, G.C. (1958), “Social behavior as exchange”, American Journal of Sociology, Vol. 63 No. 6, pp. 597-606.

Hu, L. and Bentler, P.M. (1998), “Fit indices in covariance structure modeling: sensitivity to underparameterized model misspecification”, Psychological Methods, Vol. 3 No. 4, pp. 424-453.

Hu, L. and Bentler, P.M. (1999), “Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives”, Structural Equation Modeling: A Multidisciplinary Journal, Vol. 6 No. 1, pp. 1-55.

Hutcheson, G. and Sofroniou, N. (1999), The Multivariate Scientist, SAGE Publications, London.

Ibrahim, A. and Cigdem, T. (2018), “Investigation of employers’ performance expectations for new IT graduates in individual and team work settings for software development”, Information Technology & People, Vol. 31 No. 1, pp. 199-214.

Indihar Štemberger, M., Manfreda, A. and Kovačič, A. (2011), “Achieving top management support with business knowledge and role of IT/IS personnel”, International Journal of Information Management, Vol. 31 No. 5, pp. 428-436.

Iriondo, J.M., Albert, M.A.J. and Escudero, A. (2003), “Structural equation modelling: an alternative for assessing causal relationships in threatened plant populations”, Biological Conservation, Vol. 113 No. 3, pp. 367-377.

Ives, B., Jarvenpaa, S.L. and Mason, R.O. (1993), “Global business drivers: aligning information technology to”, IBM Systems Journal, Vol. 32 No. 1, pp. 143-161.

James, L.R., Mulaik, S.A. and Brett, J.M. (1982), Causal Analysis: Assumptions, Models, and Data, SAGE Publications, Beverly Hills, CA.

Järveläinen, J. (2013), “IT incidents and business impacts: validating a framework for continuity management in information systems”, International Journal of Information Management, Vol. 33 No. 3, pp. 583-590.

Jarvenpaa, S.L., Tractinsky, N. and Vitale, M. (2000), “Consumer trust in an internet store: a cross cultural validation”, Information Technology and Management, Vol. 1 Nos 1-2, pp. 45-71.

Jeong, M. and Oh, H. (2017), “Business-to-business social exchange relationship beyond trust and commitment”, International Journal of Hospitality Management, Vol. 65, pp. 115-124.

Jöreskog, K.G. and Sörbom, D. (1993), LISREL 8: Structural Equation Modeling with the SIMPLIS Command Language, Scientific Software International, Chicago, IL.

Kaiser, H. (1974), “An index of factorial simplicity”, Psychometrika, Vol. 39 No. 1, pp. 31-36.

Kakabadse, A. and Korac-Kakabadse, N. (2000), “Leading the pack: future role of IS/IT professionals”, Journal of Management Development, Vol. 19 No. 2, pp. 97-155.

Kappelman, L., Johnson, V., McLean, E. and Maurer, C. (2018), “The 2017 SIM IT issues and trends study”, MIS Quarterly Executive, Vol. 17 No. 1, pp. 53-88.

Keen, P.G.W. (1991), Shaping the Future: Business Design through IT, Harvard Business School Press, Boston, MA.

Kline, P. (1999), The Handbook of Psychological Testing, 2nd ed., Routledge, London.

Kline, R.B. (2011), Principles and Practice of Structural Equation Modeling, Guilford Press, New York, NY.

Kline, T.J.B. (2005), Psychological Testing: A Practical Approach to Design and Evaluation, SAGE Publications, Thousand Oaks, CA.

Koriat, N. and Gelbard, R. (2014), “Knowledge sharing motivation among IT personnel: integrated model and implications of employment contracts”, International Journal of Information Management, Vol. 34 No. 5, pp. 577-591.

Koufteros, X.A. (1999), “Testing a model of pull production: a paradigm for manufacturing research using structural equation modeling”, Journal of Operations Management, Vol. 17 No. 4, pp. 467-488.

Kovačič, A. (2001), “Business renovation projects in Slovenia”, Business Process Management Journal, Vol. 7 No. 5, pp. 409-419.

Krotov, V. (2015), “Bridging the CIO–CEO gap: it takes two to tango”, Business Horizons, Vol. 58 No. 3, pp. 275-283.

Kuegler, M., Smolnik, S. and Kane, G. (2015), “What’s in IT for employees? Understanding the relationship between use and performance in enterprise social software”, The Journal of Strategic Information Systems, Vol. 24 No. 2, pp. 90-112.

Lerouge, C., Newton, S. and Blanton, J.E. (2005), “Exploring the systems analyst skill set: perceptions, preferences, age, and gender”, Journal of Computer Information Systems, Vol. 45 No. 3, pp. 12-23.

Lin, T.-C., Ku, Y.-C. and Huang, Y.-S. (2014), “Exploring top managers’ innovative IT (IIT) championing behavior: integrating the personal and technical contexts”, Information & Management, Vol. 51 No. 1, pp. 1-12.

Lin, W.T., Chen, Y.H. and Shao, B.B.M. (2015), “Assessing the business values of information technology and e-commerce independently and jointly”, European Journal of Operational Research, Vol. 245 No. 3, pp. 815-827.

Long, J.S. (1983), Covariance Structure Models: An Introduction to LISREL, SAGE Publications, Beverly Hills, CA.

Luftman, J., Papp, R. and Brier, T. (1999), “Enablers and inhibitors of business–IT alignment”, Communications of the AIS, Vol. 1 No. 1, pp. 1-33.

Luftman, J.N. (2000), “Assessing business–IT alignment maturity”, Communications of the Association for Information Systems, Vol. 4 No. 14, pp. 1-50.

Luo, J., Fan, M. and Zhang, H. (2012), “Information technology and organizational capabilities: a longitudinal study of the apparel industry”, Decision Support Systems, Vol. 53 No. 1, pp. 186-194.

MacCallum, R., Browne, M. and Sugawara, H. (1996), “Power analysis and determination of sample size for covariance structure modeling”, Psychological Methods, Vol. 1 No. 2, pp. 130-149.

Malmström, M.M. and Johansson, J. (2016), “Social exchange in collaborative innovation: maker or breaker”, Journal of Innovation and Entrepreneurship, Vol. 5 No. 4, pp. 1-20.

Manfreda, A. and Indihar Štemberger, M. (2014), “Factors causing the relationship gap between top management and IS personnel”, Journal of Enterprise Information Management, Vol. 27 No. 2, pp. 107-121.

Marsh, H.W., Balla, J.R. and McDonald, R.P. (1988), “Goodness-of-fit indexes in confirmatory factor analysis: the effect of sample size”, Psychological Bulletin, Vol. 103 No. 3, pp. 391-410.

Martin, V.A., Hatzakis, T., Lycett, M. and Macredie, R. (2004), “Building the Business/IT relationship through knowledge management”, Journal of Information Technology Cases and Applications, Vol. 6 No. 2, pp. 27-47.

Mata, F.J., Fuerst, W.L. and Barney, J.B. (1995), “Information technology and sustained competitive advantage: a resource-based analysis”, MIS Quarterly, Vol. 19 No. 4, pp. 487-505.

Melville, N., Kraemer, K. and Gurbaxani, V. (2004), “Information technology and organizational performance: an integrative model of it business value”, MIS Quarterly, Vol. 28 No. 2, pp. 283-322.

Mohr, J. and Spekman, R. (1994), “Characteristics of partnership success: partnership attributes, communication behavior, and conflict resolution techniques”, Strategic Management Journal, Vol. 15 No. 2, pp. 135-152.

Mulaik, S.A., James, L.R., Van Alstine, J., Bennett, N., Lind, S. and Stilwell, C.D. (1989), “Evaluation of goodness-of-fit indices for structural equation models”, Psychological Bulletin, Vol. 105 No. 3, pp. 430-445.

Nonaka, I., Kodama, M., Hirose, A. and Kohlbacher, F. (2014), “Dynamic fractal organizations for promoting knowledge-based transformation – a new paradigm for organizational theory”, European Management Journal, Vol. 32 No. 1, pp. 137-146.

Nord, J.H., Nord, D.G., Cormack, S. and Cater-Steel, A. (2007), “An investigation of the effect of Information Technology (IT) culture on the relationship between IT and business professionals”, International Journal of Management & Enterprise Development, Vol. 4 No. 3, pp. 265-292.

Nunnally, J.C. (1967), Psychometric Theory, McGraw-Hill Education, New York, NY.

O’Leary-Kelly, S.W. and J. Vokurka, R. (1998), “The empirical assessment of construct validity”, Journal of Operations Management, Vol. 16 No. 4, pp. 387-405.

Parolia, N., Goodman, S., Li, Y. and Jiang, J.J. (2007), “Mediators between coordination and IS project performance”, Information & Management, Vol. 44 No. 7, pp. 635-645.

Peppard, J. (2018), “Rethinking the concept of the IS organization”, Information Systems Journal, Vol. 28 No. 1, pp. 76-103.

Peppard, J. and Ward, J. (1999), “‘Mind the gap’: diagnosing the relationship between the IT organisation and the rest of the business”, The Journal of Strategic Information Systems, Vol. 8 No. 1, pp. 29-60.

Piccoli, G. and Ives, B. (2005), “Review: IT-dependent strategic initiatives and sustained competitive advantage: a review and synthesis of the literature”, MIS Quarterly, Vol. 29 No. 4, pp. 747-776.

Raju, J. (2014), “Knowledge and skills for the digital era academic library”, The Journal of Academic Librarianship, Vol. 40 No. 2, pp. 163-170.

Ravichandran, T. and Lertwongsatien, C. (2005), “Effect of information system resources and capabilities on firm performance: a resource-based perspective”, Journal of Management Information Systems, Vol. 21 No. 4, pp. 237-276.

Raz, O. and Goldberg, A. (2006), “How to make IT work: cognitive perspectives for better information technologies performance”, European Management Journal, Vol. 24 Nos 2-3, pp. 199-205.

Segars, A.H. and Grover, V. (1993), “Re-examining perceived ease of use and usefulness: a confirmatory factor analysis”, MIS Quarterly, Vol. 17 No. 4, pp. 517-525.

Sharma, S., Mukherjee, S., Kumar, A. and Dillon, W.R. (2005), “A simulation study to investigate the use of cutoff values for assessing model fit in covariance structure models”, Journal of Business Research, Vol. 58 No. 7, pp. 935-943.

Singh, A. and Hess, T. (2017), “How chief digital officers promote the digital transformation of their companies”, MIS Quarterly Executive, Vol. 16 No. 1, pp. 1-17.

Siurdyban, A. (2014), “Understanding the IT/business partnership: a business process perspective”, Information Systems Frontiers, Vol. 16 No. 5, pp. 909-922.

Škrinjar, R. and Trkman, P. (2013), “Increasing process orientation with business process management: critical practices”, International Journal of Information Management, Vol. 33 No. 1, pp. 48-60.

Sledgianowski, D., Luftman, J.N. and Reilly, R.R. (2006), “Development and validation of an instrument to measure maturity of IT business strategic alignment mechanisms”, Innovative Technologies for Information Resources Management, Vol. 19 No. 3, pp. 18-33.

Sveiby, K.-E. (2001), “A knowledge-based theory of the firm to guide in strategy formulation”, Journal of Intellectual Capital, Vol. 2 No. 4, pp. 344-358.

Teng, B.S. (2003), “Collaborative advantage of strategic alliances: value creation in the value net”, Journal of General Management, Vol. 29 No. 2, pp. 1-22.

Tian, J., Wang, K., Chen, Y. and Johansson, B. (2010), “From IT deployment capabilities to competitive advantage: an exploratory study in China”, Information Systems Frontiers, Vol. 12 No. 3, pp. 239-255.

Todd, P.A., McKeen, J.D. and Gallupe, R.B. (1995), “The evolution of IS job skills: a content analysis of IS job”, MIS Quarterly, Vol. 19 No. 1, pp. 1-28.

Tuten, T.L. and Urban, D.J. (2001), “An expanded model of business-to-business partnership formation and success”, Industrial Marketing Management, Vol. 30 No. 2, pp. 149-164.

Venkatraman, N. and Loh, L. (1994), “The shifting logic of the IS organization: from technical portfolio to relationship portfolio”, Information Strategy, Vol. 10 No. 2, pp. 5-11.

Vermerris, A., Mocker, M. and Van Heck, E. (2014), “No time to waste: the role of timing and complementarity of alignment practices in creating business value in IT projects”, European Journal of Information Systems, Vol. 23 No. 6, pp. 629-654.

Wang, Y., Shi, S., Nevo, S., Li, S. and Chen, Y. (2015), “The interaction effect of IT assets and IT management on firm performance: a systems perspective”, International Journal of Information Management, Vol. 35 No. 5, pp. 580-593.

Ward, J. and Peppard, J. (1996), “Reconciling the IT/business relationship: a troubled marriage in need of guidance”, The Journal of Strategic Information Systems, Vol. 5 No. 1, pp. 37-65.

Ward, M.A. and Mitchell, S. (2004), “A comparison of the strategic priorities of public and private sector information resource management executives”, Government Information Quarterly, Vol. 21 No. 3, pp. 284-304.

Wayment, H.A. and Cordova, A. (2003), “Mental models of attachment, social strain, and distress following a collective loss: a structural modeling analysis”, Current Research in Social Psychology, Vol. 9 No. 2, pp. 18-32.

Wheaton, B., Muthen, B., Alwin, D.F. and Summers, G.F. (1977), “Assessing reliability and stability in panel models”, Sociological Methodology, Vol. 8 No. 1, pp. 84-136.

Willcoxson, L. and Chatham, R. (2006), “Testing the accuracy of the IT stereotype: profiling IT managers’ personality and behavioural characteristics”, Information & Management, Vol. 43 No. 6, pp. 697-705.

Yi, M.Y., Jackson, J.D., Park, J.S. and Probst, J.C. (2006), “Understanding information technology acceptance by individual professionals: toward an integrative view”, Information & Management, Vol. 43 No. 3, pp. 350-363.

Yu-Yin, W., Tung-Ching, L. and Han-Huei, T.C. (2016), “Encouraging IS developers to learn business skills: an examination of the MARS model”, Information Technology & People, Vol. 29 No. 2, pp. 381-418.

Zahedi, M., Shahin, M. and Ali Babar, M. (2016), “A systematic review of knowledge sharing challenges and practices in global software development”, International Journal of Information Management, Vol. 36 No. 6, pp. 995-1019.

Zardini, A., Rossignoli, C. and Ricciardi, F. (2016), “A bottom-up path for IT management success: from infrastructure quality to competitive excellence”, Journal of Business Research, Vol. 69 No. 5, pp. 1747-1752.

Zeng, B. and Yen, B.P.C. (2017), “Rethinking the role of partnerships in global supply chains: a risk-based perspective”, International Journal of Production Economics, Vol. 185, pp. 52-62.

Corresponding author

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