Corporate Sustainable Longevity: Scale Development and Validation

Abstract

From the roots of corporate longevity that has been built by previous researchers, this article enhances existing work toward measuring the concept of corporate sustainable longevity (CSL) beyond an average firm’s age. By adopting the sequential exploratory mixed method, we performed this study in two phases: qualitative followed by quantitative. In the first phase, we used the Delphi method to verify and validate the thematic elements of the construct and generated a pool of items from the extant literature. In the second phase, we collected the data twice through a survey questionnaire (pilot N = 200 and final N = 271) and validated the scale through exploratory and confirmatory factor analyses. The results generated a five-factor structure of CSL with model fit indices, root mean square error of approximation (RMSEA) = 0.15, goodness-of-fit index (GFI) = 0.917, adjusted goodness-of-fit index (AGFI) = 0.901, normed fit index (NFI) = 0.938, incremental fix index (IFI) = 0.996 with comparative fit index (CFI) = 0.996 with satisfactory discriminant and convergent validities. Cronbach’s alpha ranging from 0.89 to 0.90 provided an evidence of internal consistency reliability of the CSL scale. This novel scale fills the gap in the literature by enabling practitioners and researchers in anticipating the firm’s ability to survive in the long run. The significance and limitations of the study are discussed in the end.

Keywords

corporate sustainable longevity scale development firm long-term survival firm survival corporate longevity corporate sustainability

Introduction

In the recent development of both theory and practice, there is a dynamic pursuance toward identifying and examining desired approaches and techniques to determine the sustainability of businesses, apart from profitability and profit growth (Cefis & Marsili, 2005; Gilbert, 1983; Glenn & Stahl, 2009). Researchers are also in constant pursuit of understanding the reasons behind business failures (Brito & Brito, 2014; Panza, Ville, & Merrett, 2018; Parker, Peters, & Turetsky, 2002). Such a thread of earlier work that encompasses various methodologies of inquiries led us to a broader context of inquiries. Why and how some businesses manage to thrive over the decades or, in some cases, centuries while many others simply vanished and could not survive to even the average firm age?

Most of the extant research is focused on corporate longevity that refers to the firm’s long life beyond the average firm age. However, research on “what enables a firm to survive longer” that corresponds to corporate sustainable longevity (CSL) is relatively limited. Moreover, current literature on CSL is implicit in nature and denotes it with corporate success, performance, sustainability, and other variables. The studies addressing the concept of CSL explicitly are a handful: see, for instance, Napolitano, Marino, and Ojala (2015) and Davis (2014). Even these studies ignore some important factors that play a vital role in CSL. Such factors are identified in the literature review section of this study.

Furthermore, the empirical evidence on what enables a firm to sustain longevity and how to measure the construct does not exist in the current literature. The existing literature is primarily focused on measuring the longevity of the firm in terms of age; see, for example, Audretsch, Houweling, and Thurik (2000); Williams and Jones (2010). Though CSL is a related concept, it is primarily different from “longevity” because it addresses the “ability” of a firm to sustain its longevity beyond average firm age. Some studies have partly contributed toward the conceptualization of CSL (Ibrahim, McGuire, & Soufani, 2009; Napolitano et al., 2015; K. R. Sharma & Dixit, 2017) and a few others have partly contributed to its operationalization (Fahed-Sreih & Djoundourian, 2006; Fatoki, 2013). However, these studies are neither converging toward a common concept nor are they thorough enough to incorporate all aspects of CSL. For instance, these studies do not address the financial strength and internal capabilities of a firm as determinants of CSL, while financial strength and internal capabilities are core elements of firm survival (Kalleberg & Leicht, 1991; Walsh & Beatty, 2007). Moreover, these studies did not provide an empirical evidence on the validity and reliability of these measures.

It can be concluded from the preceding discussion that existing research lacks a comprehensive conceptual framework of CSL as well as a reliable and valid instrument to measure the construct. Thus, the need for a reliable and valid measurement scale that encompasses the entire concept of CSL is pressing at the moment. Hence, this study endeavors to address this gap by extending the existing conceptual framework of CSL and consequently developing and validating a robust measurement scale. The proposed scale will enable the researchers and practitioners in foreseeing the longevity of a firm at any given point in time by examining the various factors internal to the firm. Moreover, it will enable managers to devise effective strategies for each specific area to achieve sustained longevity. However, it is imperative to note that this scale identifies the internal factors that affect the longevity of the firm. Though both internal and external factors play an equally important role in CSL, the external factors remain beyond the control of the firm’s management. As the nature and control of external factors are completely different, they require a separate investigation in a broader socioeconomic and political environment (Menguc, Auh, & Ozanne, 2010; Panza et al., 2018). This is also consistent with the current research trend to deal with either factor one at a time. Hence, the proposed scale will be constrained to internal organizational abilities. To achieve the objectives, we followed Hinkin, Tracey, and Enz’s (1997) step-by-step guidelines on scale development and validation. To provide further evidence on the validity of the scale, we performed discriminant and convergent validity tests.

This article is organized into five sections. Following the introduction, the “Literature review” section provides a literature review and identifies research gaps pertaining to CSL. The section, “Research method,” illustrates the research methods employed in this study and separately addresses qualitative and quantitative methods. In the section, “Findings and discussions,” the results of the qualitative and quantitative phases are discussed. The quantitative phase consists of two separate studies, that is, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The final section, “Conclusion and implications,” concludes this study and provides its implication, future research avenues, and limitations.

Literature Review

Though CSL is a novel term, the concept is well entrenched in the theory and literature. In its literal sense, CSL refers to the ability of a firm to sustain the longevity. This section provides the background of the study and discusses the state of existing literature in terms of definition, conceptualization, theoretical underpinning, and operationalization of CSL.

Background

Most of the firms do not survive in the long run and die before the average firm life or through the second generation in case of a family firm (Ahn, 2018; Cressy, 2006). Even the average firm age has decreased significantly in the past few decades because most firms fail to adapt to the increasing complexities (Panza et al., 2018; Reeves, Levin, & Ueda, 2016). Survival is the biggest common challenge for firms around the world. In fact, only a few family firms survive in the long run (Hillebrand, 2018). Specifically, only 30% of firms survive through the second generation, 13% survive the third generation, and only 3% survive beyond that (Ward, 2011). Contemporary globalization challenges, tough competition, technological advancements, and many more elements threaten the very survival of the firms.

Definition of CSL

The current literature on firm survival, longevity, or sustainability is clearly classified into two distinct categories: corporate longevity and CSL. Where CSL corresponds to “what enables the firm to achieve longevity,” corporate longevity refers to the firm’s long life beyond the average firm age (Williams & Jones, 2010). This definition, however, does not provide an explanation of how old a firm should be, to call it long-lived or a survivor. However, most researchers believe that a firm that succeeds in surviving beyond the average firm age in a geographic region or industry can be considered as a long-lived firm (Becsi, 2002; Ibrahim et al., 2009; Williams & Jones, 2010). It is important to realize that the average firm age varies as per geographic regions and industries. However, research related to CSL identifies the characteristics, abilities or capabilities, and core competencies or strategies a firm should possess to sustain its longevity. A handful of efforts have been made to conceptualize the CSL construct. However, the challenge remains in operationalizing the construct that interacts with multiple disciplines simultaneously.

The current literature implicitly equates CSL with numerous other dependent variables such as financial and nonfinancial performance (Napolitano et al., 2015), organizational success (Fatoki, 2013), profitability (Panza et al., 2018; Walsh & Beatty, 2007), corporate reputation (Nicolò, 2015), competitive advantage (Brito & Brito, 2014), and core competencies (Prahalad, 1993). With respect to the elements that enable a firm to sustain longer, the literature is contradictory. For instance, K. R. Sharma and Dixit (2017), while investigating the longevity of century-old firms identified five keys to the longevity of such firms: be conservative, be rooted, diversify sensibly, compete but also cooperate, and look back. To some extent, these aspects seem contrary and conservative to the established findings in the existing literature that suggest adaptation (Hatum & Pettigrew, 2004), fast learning, and timely response to environmental changes (Pishdad & Haider, 2013) and customers’ needs (E. W. Anderson, Fornell, & Lehmann, 1994).

In fact, such studies identified various strategies and dynamic capabilities for firms to achieve long-term survival or CSL. In the extant literature, success and firm performance have generally been equated with business survival and longevity, as reported by Napolitano et al. (2015). However, in management research, performance and business longevity are not considered as substitutes for each other. A good performance alone does not suffice in predicting the life expectancy of a firm. Multiple internal and external elements contribute to the business longevity. Moreover, the majority of strategy literature mainly focused on identifying the internal capabilities or elements to explain the long-term success, whereas management literature mainly focused on the external elements that affect the CSL (Napolitano et al., 2015). The internal factors of CSL refer to the actors and forces within the internal environment of the firm that influence the firm’s performance and its ability to survive in the long run. The frequently identified internal factors in the existing literature include firm’s various capabilities (Bessant & Francis, 1999; Teece, 2007), learning and growth prospects (Gupta, Guha, & Krishnaswami, 2013; Kaplan & Norton, 2000), human capital (Marimuthu, Arokiasamy, & Ismail, 2009; Oliveira & Roth, 2012), competitive strategies (Balestrero & Udo, 2013; Reeves et al., 2016), maturity stage (Napolitano et al., 2015; Panza et al., 2018), leadership (Marimuthu et al., 2009; Oliveira & Roth, 2012), and financial strength (Kalleberg & Leicht, 1991; Walsh & Beatty, 2007). Whereas, external factors include the type of industry, industry life cycle, level of competition (Napolitano et al., 2015), geographic location (Williams & Jones, 2010), and PESTEL (Political, Economic, Social, Technological, Environmental and Legal) factors (Panza et al., 2018). Based on the internal elements, the existing conceptual framework is discussed in the following section.

Conceptualization of CSL

Toward the conceptualization of the CSL, the studies explicitly addressing the internal elements of CSL are comparatively handful. Researchers, such as Napolitano et al. (2015), Fatoki (2013), Fahed-Sreih and Djoundourian (2006), and Becsi (2002) explicitly explored the elements of CSL. However, the existing conceptual framework lack certain vital elements that significantly contribute to CSL. We extend the existing conceptual framework by conducting an extensive and careful literature scan that yielded 31 thematic elements of CSL. In fact, the elements that contribute to CSL are numerous in the existing literature. Nonetheless, we clustered the similar concepts under an overarching central theme. Initially conceived thematic elements of CSL from the existing literature are discussed in the following paragraphs.

A carefully established long-term vision of the firm that has effectively been communicated throughout the organization serves as a milestone for CSL (Fatoki, 2013; Napolitano et al., 2015). Kaplan and Norton (2000), in their landmark performance tool “the balanced scorecard” have given foremost importance to the vision and organizational strategies apart from financial, customer, internal, and growth perspectives. A shared vision is an important aspect of the successful organization over the long run and central to the long-term success of an organization (Neff, 2015; Senge, 2006). Moreover, effective organizational strategies must be aligned with the shared vision.

The organizational success largely depends upon the ability of its leaders to anticipate the future needs and challenges and devising the right strategies accordingly (Boga & Ensari, 2009; Stearns, Carter, Reynolds, & Williams, 1995). Matzler and Bailom (2007)’s statement “the sails determine the course, not the wind” in their book Enduring Success: What Top Companies Do Differently, is an excellent illustration of organizational vision and strategies and the role of leadership.

Along with vision and strategy, customer relationship, quality products and services, human capital, access to bank credit, stakeholder relationship, adaptation, flexibility, management competency, use of technology, business location, government support, and risk-taking have been perceived as key determinants of firm survival (Fatoki, 2013). Williams and Jones (2010) also recognized firm strategy, apart from the capital structure, the age of its owners, the ethnicity of the family, and the structure of the family, as internal factors that affect the firm longevity in small family-owned businesses in the Jamaican context.

Earlier to this, Stadler (2007) presented four principles of enduring success, namely, “Exploit before you explore, diversify your business portfolio, remember your mistakes, and be conservative about change.” Similarly, Reeves et al. (2016) suggested six principles for corporate survival that included (a) maintaining heterogeneity of people, ideas, and endeavors; (b) sustaining a modular structure; (c) preserving redundancy among components; (d) expecting surprise, but reducing uncertainty; (e) creating feedback loops and adaptive mechanisms; and (f) fostering trust and reciprocity in their business ecosystems.

Other scholars emphasized the role of corporate governance (Ahmad & Omar, 2016; Kole & Lehn, 1997; Parker et al., 2002), leadership role (Reeves et al., 2016; K. R. Sharma & Dixit, 2018), management competency, and firm performance (Abraham, Karns, Shaw, & Mena, 2001; Fatoki, 2013; McDowell, Harris, & Geho, 2016; Mwobobia, 2012) in a firm’s ability to sustain longevity. Numerous scholars underscored adaptation, flexibility to change, and innovation as great promoters of CSL. For example, P. Sharma and Salvato (2013) acknowledged adaptation, continuity, and change as the core of family firm longevity. Among other factors, adaptation has been perceived as the key driver of CSL by Napolitano et al. (2015), Fatoki (2013), and Hatum and Pettigrew (2004).

Similarly, the financial aspects are also equally important and set the foundations for CSL. The main financial aspects in the current literature include profitability and productivity, financial strength or muscles, effective cash flow management, internal and external financial controls (aka auditing), and return on capital as the primary drivers of CSL; see, for example, Narver and Slater (1990), Kalleberg and Leicht (1991), Pérotin and Robinson (2004), Walsh and Beatty (2007), Kim and Gao (2013), Kroes and Manikas (2014), and Nwanyanwu (2015). The firm’s attitude and response toward its stakeholders, particularly the owners, shareholders, investors, customers, and employees, also play a vital role in the organizational success that ultimately leads to CSL (Berman, Wicks, Kotha, & Jones, 1999; Watson, 2007).

Among all others, customer satisfaction is the most important driver of the CSL (Kozak, 2018). Customers’ satisfaction serves as the heartbeat; its absence would mean immediate death of the organization, despite the existence of all other elements. Good customer relationship, customer-oriented organizational strategies, and customers’ perceived value (Grimonpont, 2016; Lavie, Haunschild, & Khanna, 2012) are essential ways to achieve customer satisfaction that ultimately leads to CSL. In fact, the customer satisfaction mainly relies on the firm’s employees. Employees’ knowledge, skills, and experiences (aka human capital) are important assets of the firm that requires proper management. Because human capital plays the chief role in the firm’s productivity and performance, it ultimately leads to CSL (Backman, Gabe, & Mellander, 2016; Oliveira & Roth, 2012).

Other researchers have examined some important internal business processes or capabilities and argued that if these exist, and are managed effectively, they would lead a firm to sustainable longevity. Chen, Chang, and Lin (2010) and Gondal and Shahbaz (2012), in two different empirical studies, found a positive relationship between interdepartmental integration and firm performance that leads to CSL. Tseng, Divinagracia, and Divinagracia (2009) and Golicic and Smith (2013) emphasized on sustainable production processes, whereas Bessant and Francis (1999) and Jung and Wang (2006) pointed out continuous improvement as an important driver of CSL. J. C. Anderson, Rungtusanatham, and Schroeder (1994) and Flynn, Schroeder, and Sakakibara (1995), however, believed in quality management, Katila and Ahuja (2002) and Cottrell and Nault (2004) on R&D, and Aubry and Hobbs (2011) and Irefin (2013) considered superior project management as a way to achieve success and survival.

The firm choice of geographic location nationally or internationally, location of production facilities, proximity to suppliers, intermediaries, and customers also determine the performance, success, and CSL. For instance, Fatoki (2013, p. 136) noted that “firms located in metropolitan areas may have higher chances of success than those located in rural areas.” Similarly, Stearns et al. (1995) predicted that a new firm will have better chances of survival in the upstream value chain as compared with downstream in the industry value chain. Williams and Jones (2010) in an empirical investigation revealed that the location of the firm has a positive impact on its longevity.

Theoretical Foundations of CSL

The theoretical foundations of CSL remain in two theories: the Resource-based View (RBV) theory of the firm (Wernerfelt, 1984) and Balanced Scorecard Framework (Kaplan & Norton, 2000). Specifically, the internal elements of CSL are aligned with the RBV theory of the firm that accentuates various types of resources of the firm. The resource-based view of the firm argues that the ability of a firm to develop certain capabilities enables the firm to achieve sustained competitive advantage (Barney, 1991). It further enables the firm to adapt to the changing competitive environment and improves its survival prospects (Esteve-Pérez & Mañez-Castillejo, 2008). Barney (1991) classified the tangible and intangible firm resources into three distinct categories: physical capital resources, human capital resources, and organizational capital resources. The physical capital resources may include the firm’s plants, tools and equipment, its geographical location and access to raw material, and physical technology in use. Human capital resources usually include employees’ experiences, skills, training, judgments, relationships, and intelligence. Organizational capital resources include organizational structure and its attributes, organizational culture, planning, coordinating, controlling, formal and informal mutual relationships, processes, and procedures. Thus, CSL with respect to its internal elements is consistent with the resource-based view that stresses the role of the firm’s internal resources in developing and sustaining competitive advantage and firm performance that lead to long-term firm survival (Coleman, Cotei, & Farhat, 2013; Esteve-Pérez & Mañez-Castillejo, 2008; Radzi, Nor, Nazri, & Ali, 2017).

CSL, as a result of exploratory factor analysis (performed in this study), appeared in a seamless harmony with the balanced scorecard framework developed by Kaplan and Norton (2000). The balanced scorecard theory provides a framework for devising organizational strategies that create the value for the organization, enhances firm performance, and enables the firm to succeed and survive. It is used as a tool to measure and enhance organizational performance. It is a comprehensive framework to enable firms in translating vision and strategies into a coherent set of performance measures. Based on the firm’s vision and strategy, it requires managers to consider four perspectives of the firm to improve performance. The internal elements identified in the extant literature correspond to the five perspectives identified in the balanced scorecard theory. Therefore, the theoretical foundations of CSL are also consistent with the balanced scorecard theory.

Thus, the conceptual framework of CSL is established in the existing literature, however, its operationalization and measurement scale needs attention. The next section explores the state of existing literature on efforts toward operationalization and development of measurement instrument for CSL.

Operationalization of CSL Construct

In the existing literature, several researchers contributed to the measurement of corporate longevity. However, an instrument to measure CSL does not exist yet. Even the studies on the measurement scale of corporate longevity are conflicting in nature. The diverging concepts and perspectives lead to the different measurement scales. For instance, Abraham et al. (2001), Fang, Chang, and Chen (2010), and Mwobobia (2012) intended to study survival and longevity but measured firm performance instead.

Corporate longevity is usually measured in terms of the time span of the firm beyond the average firm age in a specific industry and country. Almost all the existing studies measured the longevity in terms of firm age. However, in contrast to corporate longevity, CSL is a different concept in the sense that it deals with the “ability” of a firm to sustain its longevity. Although, an integrated conceptual framework (Davis, 2014; Napolitano et al., 2015) has been developed and some scholars, such as Fahed-Sreih and Djoundourian (2006, 2008) and Fatoki (2013) attempted to operationalize the construct. However, their work is more relevant to corporate longevity that measures firm’s age instead of measuring the ability of the firm to sustain longevity (CSL). Moreover, these studies neither covered all the aspects of firm longevity nor have the scales been validated.

It is clear from the above discussions that the existing concept of CSL ignores certain important internal elements that may significantly contribute to a firm’s sustainable longevity. Moreover, the existing scales measure the firm’s age instead of the ability to sustain a long life. In this article, we endeavored to fill these research gaps by extending the existing conceptual framework and subsequently developing a valid and reliable measurement scale of CSL. The following section elaborates the research method employed in this study.

Research Method

The purpose of this study is to develop and validate a psychometrically robust measurement scale for CSL. We adopted a sequential exploratory strategy of mixed-method research. A mixed-method research has two basic choices of strategy, as identified by Creswell (2013): sequential and concurrent. In a sequential strategy, both qualitative and quantitative methods are applied one after other, whereas, in a concurrent strategy both methods are applied simultaneously. The sequential strategy is further classified into sequential exploratory, sequential explanatory, and sequential transformative. In a sequential exploratory strategy, exploratory data are collected prior to quantitative data while in a sequential explanatory strategy, quantitative data is collected prior to the qualitative data, whereas in a sequential transformative strategy, either data can be collected first as supported by the theory and the findings are integrated at interpretation stage. As this study involves the field experts for their expert opinions during the scale development process prior to collecting quantitative data, the most appropriate method for this study is sequential exploratory. Accordingly, this study has two distinct phases: qualitative followed by quantitative.

In the first phase, after conceiving thematic elements of CSL from the existing literature, we adopted a Delphi method in which a panel of field experts was involved to develop consensus on the thematic elements and to perform content and face validity of the CSL construct. In the second phase, we collected data and performed exploratory factor analysis (EFA) to uncover the underlying dimensions of CSL. Subsequently, we performed a confirmatory factor analysis (CFA) to validate the measurement scale. The detailed methodological framework of the study is presented in Figure 1 while the following subsections explain each phase in detail.

Figure 1.

The methodological framework of the study.

Qualitative Phase

The method

A thorough scan of existing literature yielded 21 thematic elements that determine the ability of a firm to sustain its longevity. Thereafter, we adopted the Delphi method to obtain the opinions of a panel of field experts. The experts suggested 10 additional thematic elements that contribute to firm longevity. A Delphi method is a structured communication technique that is commonly used in qualitative research. The purpose of the Delphi method is to obtain the most reliable consensus of a group of experts (Dalkey & Helmer, 1963). This is achieved by getting their feedback in a series of controlled communications. Such communication takes multiple rounds until the subject matter gets a unanimous consensus of the panel. By following Dalkey and Helmer (1963) guidelines on the Delphi method, we formed a panel of 16 relevant field experts and conducted four rounds in total, as shown in Figure 1. The experts were asked whether the thematic elements and the corresponding items of the measurement scale of CSL were valid. We facilitated the process to seek their views in each round. At the end of each round, we prepared an anonymous summary of their opinions and sent it back to get their approval.

Participants

The participants of the qualitative study were a panel of 16 experts on the subject matter. The panel included four executives from the Security and Exchange Commission of Pakistan (SECP), four academicians from the Institute of Business Administration (IBA) Karachi, Pakistan, four strategic managers from the Small and Medium Enterprise Development Authority (SMEDA) of Pakistan, and four veteran owners of family firms. All the experts were qualified to verify the thematic elements of CSL. They hold required qualifications and vast experience of establishing and running or regulating (in case of SECP of SMEDA) the companies. The authors first contacted the professors at IBA who recommended four SECP and SMEDA executives and four business owners.

Quantitative Phase

The method

Once we acquired a unanimous agreement of field experts on thematic elements of CSL, and content validity and adequacy were established, we developed a survey instrument to operationalize the construct. By following Hinkin et al.’s (1997) step-by-step guide on scale development, we developed the survey questionnaire and requested face validity from the field experts. We then collected the data in two phases. In the first phase, we collected data (N = 200) to perform a pilot test by using EFA. In the second phase, we collected data (N = 271) to perform CFA to test the significance of the scale and to build construct validity. Confirmatory factor analysis (CFA) was performed on a sample of N = 271 to test the significance of the scale. The details of each step are presented in the following sections.

Instrument and questionnaire development

Based on thematic elements of the construct, we generated a pool of items from three sources: (a) adapted from the existing literature, (b) developed by the researchers based on latent traits of the construct, and (c) proposed by the panel of experts. Our initial pool included a total of 73 items, as shown in Figure 1. We presented the instrument to the panel of experts and several amendments were made based on their feedback, comments, and suggestions. As a result, we dropped 20 items from the initial pool and the rest were modified accordingly. We dropped another two items in the face validity that was conducted by business college professors of the Comsats University of Lahore. As a result, the pool reduced to 51 items for pilot data collection (see the appendix).

We took extensive care while formulating the items and ensured to use simple and shortest possible statements and language familiar to the respondents. Negatively worded, double-barreled, and ambiguous terms were avoided. A 5-point Likert-type scale was used in the measurement scale, where 1 represented strongly disagree and 5 represented strongly agree.

Pretest and pilot test

Prior to the pilot test, we performed a pretest of the instrument on a sample of 30 respondents to identify any potential problems in the instrument and data collection. A pilot test, however, was conducted to perform an exploratory factor analysis on a sample of 200 respondents. Further details on the pilot test are explained in the exploratory factor analysis section.

Population and sampling

We adopted a nonprobability sampling technique due to unavailability of a valid and reliable sampling frame or working population. A working population is an absolute list of elements from which sample may be drawn (Zikmund, Babin, Carr, & Griffin, 2012). The family-owned firms prevail across various firm types, such as publicly listed companies, large private firms, and SMEs. Though authentic lists for all types of firms are readily available from various institutions, however, neither these lists provide any information on family or nonfamily ownership, nor is a separate authentic list of family firms available. Moreover, due to the lack of family and nonfamily ownership information, it is not possible to develop a new list as working population. Thus, similar to Saunders’ (2011) recommendations, this study adopted judgmental nonprobability techniques. The judgmental or purposive sampling technique was considered the most suitable for this study because this enables the researcher to select appropriate cases that help in answering the research questions and meet the research objectives (Saunders, 2011). We selected the firms based on our judgment of the ownership structure, industry type, firm age, and firm size.

The target population for this study included family and nonfamily executives working at the top and middle level in the family-owned manufacturing and service companies of Pakistan. Mainly three factors motivated us to select family firms of Pakistan for data collection. These included (a) providing an evidence from one of the developing countries, as almost all the previous studies on CSL were conducted in developed countries from the West; (b) family firms are the dominant form of business in Pakistan (59%) like elsewhere in the region (Tahir, Saleem, & Aziz, 2014) and most of them die during first intergenerational transition (Afghan & Wiqar, 2007); and (c) availability of the data and access to some family firms for personal visits to collect the required data. We contacted all the selected companies, especially their CEOs, and requested them to complete at least five self-administered questionnaires from the managers of their firms. Pilot data were collected via online surveys. In this regard, a link was provided in the email to the CEOs. However, final data were collected by personally visiting the companies along with online surveys.

Findings and Discussions

Qualitative Phase

As discussed earlier, we conceived 31 thematic elements from the extant literature and field experts. These elements were sent to the panel members to provide expert opinion. Considering their comprehensive feedback, 31 elements were eventually converged into 11 overarching themes. The experts suggested merging and renaming similar elements into overarching major concepts as shown in Figure 2.

Figure 2.

Convergence of thematic elements of CSL.

The process took four rounds in total to reach a unanimous agreement. At the end of each round, we produced an anonymous summary of the previous round and provided it to the panel members for their approval. Meanwhile, we produced a pool of 73 items and provided it to experts to attain content validity and content adequacy of measurement scale. Based on their response, 20 items were dropped, and many others were modified and adjusted (see the appendix). The panel was again provided with the summary of the previous round. The questionnaire was then developed and in the last round the panel was contacted for the face validity of the questionnaire. Subsequently, we attuned the survey questionnaire accordingly and sent it to the panel for approval prior to data collection.

Quantitative Phase

The quantitative phase consists of two studies with separate data sets. Data collection and screening process were performed twice. In the first study, data were collected for a pilot test to run EFA and in the second study, data were collected to run CFA and to test the convergent and discriminant validity of the scale.

Study 1: Exploratory factor analysis

Data collection and data screening

For the pilot test, we contacted 970 firms by email and requested them to fill the online survey. As a result, we received 386 responses of which, 246 were completed and were processed for data screening. We excluded responses with missing values, outliers, unengaged manner of response (providing the same answer for all questions), duplicate responses or respondent spending less than 5 min in completing the survey. An expected minimum time to read and fill the survey was estimated at 5 min, where average response time remained 11 min. Therefore, we dropped the responses completed in less than 5 min. This process excluded another 46 responses, leaving an effective sample size of 200 responses that corresponds to 20.6% response rate, which is considered acceptable for similar studies (Debicki, Kellermanns, Chrisman, Pearson, & Spencer, 2016).

Descriptive statistics

After screening, 200 responses were considered for further data analysis in Phase 1 of data collection. Most of the respondents were male (88%), nonfamily managers (67%) having a master’s degree (55%) and aged between 26 and 45 years (88%). Most of the respondents (78%) were senior managers with experience of more than 5 years. With respect to participating firms, most (66%) firms were aged between 5 and 20 years, whereas, 24% were older than 20 years. Among them, 50% of firms were owned and managed by the first generation while 39% of firms were in the second generation.

Results and discussions

With the aim of uncovering the underlying dimensions of CSL, we performed an exploratory factor analysis on 51 items using SPSS Version 24. The pilot data were analyzed for factor reduction, using the principal component analysis (PCA) method of extraction and Promax method of rotation. Hair, Anderson, and Tatham (1994) recommend the use of PCA using Promax with Kaiser Normalization for new scale development. We set the eigenvalue greater than 1.0 as it is a good indication of measurement item validity. Based on experts’ opinion, we set a relatively higher cutoff value for factor loading (0.7) to obtain clear factors reduction and to attain a better convergent and discriminant validity. Similarly, we set the cutoff value of commonality score of each item at 0.60 to obtain a clear factor structure and a psychometrically robust measurement scale. After fulfilling the abovementioned criteria, we applied further scrutiny and excluded the items from the analysis that did not fulfill the following criteria: first, if less than three items were loaded on any factor; second, if any item was cross loaded on more than one factor with very close loading; third, if an item showed individual KMO value of less than 0.50; and fourth, if individual item reliability was lower than the minimum threshold of .6, which might create internal consistency issues of the measurement scale. Each factor was then checked for internal consistency with Cronbach’s alpha at .70 as suggested by Hair, Black, Babin, Anderson, and Tatham (2006).

Following the criteria mentioned above, in the first run of EFA, 13 items out of 51 did not load due to factor loading value less than 0.7. Excluding those 13 items, we performed the second run and, as a result, another two items did not load. We excluded three other items due to commonality value lower than 0.60 and two items due to individual item reliability lower than .60 (see the appendix for detail of items). In the third run, with 31 items in hand, we obtained a clear five-factor solution, with better factor loadings ranging between 0.75 and .946 (see Table 1). The eigenvalues for the five factors were obtained as 12.811, 3.217, 2.956, 2.313, and 1.578, respectively. The total variance explained by the five factors solution was 73.973, whereas percentage variance explained by individual factors remained as 41.324, 10.379, 9.537, 7.462, and 5.091, respectively. In addition, Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy with a value of 0.929 and a significant value of Bartlett’s Test of Sphericity showed that the data set was appropriate for factor analysis. Subsequently, we named the resulting factors based on prior knowledge (balanced scorecard framework) and according to the nature of the highly loaded items. The factors were named as financial strength, customer orientation, internal capabilities, strategic perspective, and learning and growth, respectively.

Table 1.

Summary of Exploratory Factor Analysis of CSL (N = 200).

Proposed names of factors	Financial strength	Customer orientation	Internal capabilities	Strategic perspective	Learning and growth
Eigenvalue	12.811	3.217	2.956	2.313	1.578
Cumulative percentage of variance explained	41.324	51.703	61.24	68.702	73.793
Instrument reliability Cronbach’s alpha	0.940	0.932	0.935	0.914	0.918
Items	Factors loadings
Our company. . .
. . . adopted adequate internal controls to prevent inaccurate financial reporting	0.946
. . . has the positive cash flow to meet its long-term debt payments	0.889
. . . has adequate financial resources to survive in the long run	0.863
. . . has a higher return on capital necessary for long-term survival	0.848
. . . has the positive cash flow to meet its short-term commitments	0.823
. . . is expected to maintain its profit margin to survive in the long run	0.816
. . . generated a higher profit margin in past years compared with industry average	0.797
. . . has been able to provide its products at low prices since long		0.895
. . . maintains long-term close relationships with the customers		0.861
. . . consistently searches for new low-cost locations for its long-term survival		0.845
. . . offers prices based on customers’ perception of value rather than costs		0.837
. . . provides multiple communication channels to the customers for a long-term relationship		0.829
. . . always strives to delight customers rather than only meeting customers’ needs		0.813
. . . has integrated processes/systems to interact with the customers for a long-term relationship		0.791
. . . remained more successful in waste reduction in the industry since long			0.932
. . . ensures an effective coordination of all departments in the product development process			0.880
. . . ensures that new product/service design is thoroughly reviewed before it is delivered			0.859
. . . offers high-quality products/services for long-term existence			0.833
. . . is considered the most efficient producer in the industry since long			0.793
. . . strategy for competitive advantage is based on the customers’ needs			0.779
. . . is driven by our beliefs about how we can create greater value for customers			0.778
. . . has well-articulated long-term vision				0.945
. . . has successfully implemented competitive strategies in the past 5 years				0.874
. . . has a clear vision for its future				0.828
. . . has a better strategy for long-term future sales growth				0.794
. . . has a dynamic strategy formulation and implementation system				0.753
. . . believes that we exist to serve the needs of our customers				0.750
. . . employees have the required level of skills for the assigned jobs					0.918
. . . employees are empowered in making long-lasting decisions					0.876
. . . continually improves all aspects of processes and systems for long-term survival					0.870
. . . searches for continued learning and improvement after the new installations					0.818

Note. Extraction method: Principal component analysis, rotation method: Promax with Kaiser Normalization. Cutoff value = 0.70, KMO = .929. CSL = corporate sustainable longevity; KMO = Kaiser–Meyer–Olkin.

The EFA uncovered five underlying dimensions of CSL, where each subscale contained more than three items with higher factor loadings and sound internal consistency measures ranging between .932 and .935 (considerably above the threshold value of .70) as shown in Table 1. The extracted five factors were then tested for goodness-of-fit measure, using a confirmatory factor analysis with a new data set.

Study 2: Confirmatory factor analysis

Data collection and data screening

For this study, we contacted a total of 1,230 firms via emails and personal visits that yielded 537 responses of which 112 incomplete responses were dropped, leaving 425 responses. We applied a similar data-screening process as we did in Study 1, which eliminated 154 responses. Finally, we obtained an effective sample size of 271 with a response rate of 22%. We believe that the response rate was improved a little due to the personal visits of 40 firms out of 1,230.

Data normality test

Following data screening, we performed data normality test through skewness and kurtosis test as well as through Shapiro–Wilk test, as shown in Table 2. A normal distribution of the data requires values of skewness and kurtosis between −2 and +2 (Gravetter & Wallnau, 2014). Similarly, in the Shapiro–Wilk test, the value for each variable should be insignificant, that is, significance or p value > .05 for a normally distributed data. The results of this study showed that skewness and Kurtosis values for most of the variables were out of the recommended range. Likewise, the results of the Shapiro–Wilk test further strengthen the findings by showing significant values for all the variables. Thus, the results of these tests showed that the data in this study were not normally distributed.

Table 2.

Data Normality Test.

Descriptive				Shapiro–Wilk
Item	N	Skewness	Kurtosis	Statistic	df	Significant
Vis1	271	−3.717	0.233	0.885	271	.000
Vis2	271	−3.563	−0.594	0.880	271	.000
Vis3	271	−4.790	−0.053	0.868	271	.000
Vis4	271	−2.524	−1.024	0.887	271	.000
Vis5	271	−2.945	−0.160	0.887	271	.000
Vis6	271	−3.423	−0.244	0.886	271	.000
Fin1	271	−2.899	−0.188	0.886	271	.000
Fin2	271	−3.863	0.394	0.880	271	.000
Fin3	271	−3.188	−0.656	0.892	271	.000
Fin4	271	−3.000	−0.090	0.887	271	.000
Fin5	271	−3.733	−0.145	0.881	271	.000
Fin6	271	−4.971	2.652	0.857	271	.000
Fin7	271	−2.142	−0.766	0.882	271	.000
Cus1	271	−2.249	−0.378	0.886	271	.000
Cus2	271	−4.484	−0.850	0.874	271	.000
Cus3	271	−3.423	−0.130	0.887	271	.000
Cus4	271	−3.559	−0.122	0.883	271	.000
Cus5	271	−3.812	0.091	0.880	271	.000
Cus6	271	−3.808	0.070	0.883	271	.000
Cus7	271	−2.488	−1.209	0.891	271	.000
Int1	271	−4.200	0.638	0.865	271	.000
Int2	271	−3.030	−0.502	0.885	271	.000
Int3	271	−3.772	0.169	0.875	271	.000
Int4	271	−2.912	−0.670	0.879	271	.000
Int5	271	−3.677	0.687	0.873	271	.000
Int6	271	−4.528	1.036	0.864	271	.000
Int7	271	−4.131	1.101	0.873	271	.000
Lrn1	271	−4.533	0.217	0.878	271	.000
Lrn2	271	−4.101	−0.687	0.881	271	.000
Lrn3	271	−3.977	−0.317	0.875	271	.000
Lrn4	271	−4.578	0.271	0.876	271	.000

Note. Vis = strategic perspective; Fin = financial strength; Cus = customer orientation; Int = internal capabilities; Lrn = Learning and growth.

Descriptive statistics

Of the 271 respondents, 78% were male of whom 70% were nonfamily managers who were mainly aged older than 36 years (51%) and the majority (60%) held a master’s degree. Of all firms, 41% were managed by the second generation, whereas, 36% of firms were in the first generation. Only 23% firms managed to survive till the third generation and beyond.

Results and discussions

To verify and cross-validate the five-factor solution of CSL that emerged as a result of EFA, we performed a confirmatory factor analysis on the data collected in the second phase (N = 271). We carried out CFA using a covariance-based structural equation modeling (CB-SEM) tool, Amos, for testing the measurement model fit. The use of CB-SEM is generally preferred for normally distributed data. However, Jannoo, Yap, Auchoybur, and Lazim (2014) in an empirical research provided an evidence that CB-SEM can be used for a nonnormally distributed data if the sample size is 50 or above. We utilized model fit indices such as relative chi-square (χ²/df ), goodness of fit index (GFI), adjusted goodness-of-fit index (AGFI), comparative fit indices (CFIs), root mean square residual (RMR), normed fit index (NFI), incremental fit index (IFI), and the root mean square error of approximation (RMSEA). Tabachnick and Fidell (2007) and Kline (2005) recommended the value of (χ²/df ) in ranges of 1 to 2 or 1 to 3 as an indicator of a good fit between model and the sample data. The recommended values for GFI, AGFI, NFI, and IFI should be greater than 0.90, whereas CFI value should be greater than 0.95. The recommended values of RMSEA and RMR are less than 0.08 for a good model fit (Hu & Bentler, 1999). The reliability of the scale for each factor was measured through Cronbach’s alpha with a recommended threshold value of α ≥ .07 (Hair et al., 2006).

The initial model showed a relatively poor fit with values lower than the recommended level for all the model fit indices apart from relative chi-square (χ²/df = 0.833 with a p value = .00) and RMSEA (0.056). However, the p value for a measurement model should be insignificant above the .05 threshold. The initial model showed a GFI = 0.868, AGFI = 0.846, NFI = 0.868, IFI = 0.935, CFI = 0.935, and RMR = 0.110. The poor fit suggested exclusion of the items that caused the poor model fit. We examined the values of standardized residual covariance for individual items and excluded three items with significantly higher standardized residual covariance than the threshold value of 0.40. We dropped the items one after the other in an iterative run of CFA until it yielded the values of model fit indices within the acceptable limits (see the appendix for deleted items).

The final model (see Table 3) had a relative chi-square (χ²/df = 1.057) with p value = .224, indicating a lower discrepancy between the matrices, where recommended value for χ²/df is greater than 1 and less than 2 and p value should be greater than .05. The results for GFI = 0.917, AGFI = 0.901, NFI = 0.938, IFI = 0.996, and CFI = 0.996 were all above the recommended value of 0.90, indicating a better model fit that shows the ability of scale for generalization to the population or, in other words, parsimony of the instrument. The value of RMSEA (0.15) indicated that the model fit was not by chance. Similarly, the result for RMR (0.32) showed insignificant outliers in the data.

Table 3.

Validity and Reliability of the Measurement Scale (N = 271).

Scales		Loadings	(α)	AVE	CR
Strategic perspective (Vis)			0.913	0.682	0.914
Vis3	Our company has well-articulated long-term vision.	0.929
Vis2	Our company has a clear vision for its future.	0.837
Vis4	Our company has successfully implemented competitive strategies in the past 5 years.	0.800
Vis1	Our company believes that we exist to serve the needs of our customers.	0.793
Vis6	Our company has a better strategy for long-term future sales growth.	0.760
Customer orientation (Cus)			0.942	0.732	0.942
Cus2	Our company has been able to provide its products at low prices since long.	0.894
Cus5	Our company provides multiple communication channels to the customers for a long-term relationship.	0.885
Cus4	Our company maintains long-term close relationships with the customers.	0.875
Cus6	Our company has integrated processes/systems to interact with the customers for a long-term relationship.	0.840
Cus7	Our company always strives to delight customers rather than only meeting the customers’ needs.	0.825
Cus3	Our company consistently searches for new low-cost locations for its long-term survival.	0.810
Financial strength (Fin)			0.923	0.633	0.923
Fin5	Our company generated a higher profit margin in the past 5 years compared with the industry average.	0.876
Fin3	Our company has the positive cash flow to meet its short-term commitments.	0.850
Fin4	Our company has the positive cash flow to meet its long-term debt payments.	0.808
Fin2	Our company has a higher return on capital necessary for long-term survival.	0.779
Fin7	Our company adopted adequate internal controls to prevent inaccurate financial reporting.	0.757
Fin6	Our company is expected to maintain its profit margin to survive in the long run.	0.754
Fin1	Our company has adequate financial resources to survive in the long run.	0.733
Learning and growth (Lrn)			0.901	0.697	0.902
Lrn4	Our company’s employees have the required level of skills for the assigned jobs.	0.875
Lrn2	Our company searches for continued learning and improvement after the new installations.	0.855
Lrn1	Our company continually improves all aspects of processes and systems for long-term survival.	0.844
Lrn3	Our company’s employees are empowered in making long-lasting decisions.	0.760
Internal capabilities (Int)			0.898	0.598	0.899
Int6	Our company is considered as the most efficient producer in the industry since long.	0.828
Int4	Our company ensures that new product/service design is thoroughly reviewed before it is delivered.	0.795
Int5	Our company offers high-quality products/services for long-term existence.	0.780
Int3	Our company ensures an effective coordination of all departments in the product development process.	0.771
Int1	Our company strategy for competitive advantage is based on the customers’ needs.	0.757
Int7	Our company remained more successful in waste reduction in the industry since long.	0.703

Note. χ²/df = 1.057, GFI = 0.917, AGFI = 0.901, NFI = 0.938, IFI = 0.996, CFI = 0.996, and RMSEA = 0.15, RMR = 0.32. AVE = average variance extracted; CR = composite reliability; GFI = goodness-of-fit index; AGFI = adjusted goodness-of-fit index; NFI = normed fit index; IFI = incremental fit index; CFI = comparative fit indices; RMSEA = root mean square error of approximation; RMR = root mean square residual.

Though the results of EFA and CFA established the basic construct validity and reliability of the CLS scale, we provide additional evidence of scale validity through convergent and discriminant validity tests. We ensured the convergent validity by setting a relatively higher cutoff value of 0.70 for factor loadings. Whereas, the discriminant validity was performed to check the degree to which each factor was empirically different from others. The discriminant validity was determined by comparing the square root of the average variance extracted (AVE) with squared correlation coefficients between each pair of the factors. A greater AVE value of the factors, compared with their relevant squared correlation coefficients, provide evidence of the discriminant validity of the construct.

Convergent and discriminant validity

Relatively higher standardized factor loadings (values ranging from 0.70 to 0.92, at significance p < .5; see Figure 3) with an internal consistency (Cronbach’s alpha ranging from .89 to .94) provided sufficient evidence that the specified indicators truly represent the relevant constructs (see Table 3). The assessment of higher loadings, in comparison with insignificant weights of indicators to their respective latent variables, yielded a first-order reflective measurement model, as shown in Figure 3, namely, the confirmatory measurement model for CSL. To establish the convergent validity, the average variance extracted (AVE) for each factor should be greater than 0.5 and composite reliability (CR) should be above the minimum criteria (CR > .70). Moreover, the CR values should be greater than AVE values. As shown in Table 3, the results of this study indicated that all the three recommended criteria were met to establish the convergent validity of the CSL scale.

Figure 3.

The confirmatory measurement model for CSL.

Furthermore, to examine the discriminate validity of the constructs, AVE value of each construct was compared against the squared correlation coefficients between the constructs. The bold values in parentheses in Table 4 represent the AVE of the constructs, whereas, other values show squared correlations coefficients between the constructs. It is apparent from Table 4 that AVE values were larger than the squared correlations coefficients between the constructs. Moreover, covariance among unobserved variables is reasonably lower than the threshold of 0.80 (see Figure 3). Thus, according to Hair et al. (2006), discriminant validity of the CSL measurement scale could be claimed. The following section provides an assessment of second-order construct.

Table 4.

Discriminant Validity.

Factors	Financial strength	Vision and strategy	Customer orientation	Internal capabilities	Learning and growth
Financial strength	(0.633)
Vision and strategy	.277	(0.682)
Customer orientation	.340	.171	(0.732)
Internal capabilities	.371	.300	.242	(0.598)
Learning and growth	.118	.204	.088	.112	(0.697)

Note. The bold values in parentheses represent the AVE of the constructs, while others show squared correlations coefficients between the constructs. AVE = average variance extracted.

Assessing second order construct

Once construct validity of measurement was established at first order, we assessed the reliability and validity of the model at second-order construct. The analysis showed that the second-order model was reflective as well—thus generating a reflective-reflective second-order model. The loadings of the first-order latent construct on a second-order construct (CSL) are provided in Table 5. The results have shown that all the first-order constructs were fairly loaded (> 0.70 threshold value) to their respective second-order constructs with acceptable reliability (> .60), the cumulative percentage of variance explained (54.479), and Eigenvalue (2.724). Moreover, all the loadings were significant (> 2.57) at a 99% confidence interval.

Table 5.

Loadings at Second Order Construct.

Items (first order construct)	Loadings
Eigenvalue	2.724
Cumulative percentage of variance explained	54.479
Instrument reliability Cronbach’s alpha	.787
Financial strength	0.810
Internal capabilities	0.782
Strategic perspective	0.763
Customer orientation	0.740
Learning and growth	0.709

Final Validation Result

To conclude, we validated the CSL construct qualitatively through field experts’ panel and quantitatively through EFA, CFA, and convergent and discriminant validity tests. Consequently, CSL finally emerged as a five-dimensional construct with 28 validated items in total. The five dimensions included strategic perspective with five items, customer orientation with six items, financial strength with seven items, learning and growth perspective with four items, and internal capabilities with six items (see Table 3). Hence, the measurement model of CSL successfully fulfilled construct validity criteria as well as reliability requirements.

Conclusion and Implications

In this study, we endeavored to refine the conceptual framework of CSL and consequently developed and validated its measurement scale. Based on an extensive literature review and subsequent agreement of experts, we induced new thematic elements that were missing in the existing conceptual framework of CSL mainly developed by Napolitano et al. (2015). The inclusion of new thematic elements is supported by the existing theory and literature. The novel themes included sustainable production (Golicic & Smith, 2013; Tseng et al., 2009), shared vision (Neff, 2015; Senge, 2006), stakeholder relationship (Berman et al., 1999; Watson, 2007), human capital (Backman et al., 2016; Oliveira & Roth, 2012), knowledge capital (Agarwal, Echambadi, Franco, & Sarkar, 2004; Darroch, 2005), and interdepartmental integration (Chen et al., 2010; Gondal & Shahbaz, 2012).

With respect to scale development and validation, we conducted a mixed-method sequential exploratory research. We established the initial construct validity of the scale through a panel of 16 field experts who ensured content adequacy, content validity, and face validity in multiple rounds. As a result, the originally conceived 31 thematic elements were converted into 11 overarching themes, based on which a survey questionnaire was designed to collect the data for pretest and pilot test. Consequently, exploratory (EFA) and confirmatory (CFA) analyses were conducted to validate the scale. CSL emerged as a five-dimensional construct as a result of EFA. The dimensions included strategic perspective, customer orientation, financial strength, learning and growth perspective, and internal capabilities. While the CSL concept is rooted in Resource-Based View (RBS) theory, the resulting five-factor solution seems to be in harmony with the Balanced Scorecard Framework (Kaplan & Norton, 1996, 2000). The Balanced Scorecard is a renowned strategic management tool and is used to enhance firm performance that eventually leads to sustainable longevity. Consequently, we performed construct validity of the CSL scale through CFA on a separate data set. We ensured further reliability and validity of the scale through convergent and discriminant validity tests as well as model fit indices (RMSEA = 0.15, GFI = 0.917, AGFI = 0.901, NFI = 0.938, IFI = 0.996, and CFI = 0.996). The satisfactory results of model fit indices, discriminant and convergent validities, and Cronbach’s alpha (ranging from .89 to .90) provided evidence of a psychometrically valid and reliable measurement scale of CSL.

Thus, this study contributes to the body of knowledge by extending the existing conceptual framework of CSL and by operationalizing the construct for the first time. In a broader sense, the results suggest that managers should devise organizational strategies in five areas identified in this study to achieve the ability to sustain firm longevity. Overall, the results of this study enable managers to predict the ability of the firm to sustain its longevity. Each dimension of CSL has important implications for the managers. The strategic perspective accentuates that firms with shared vision (Neff, 2015), and the ability to execute the organizational strategies successfully, are likely to sustain longevity. It encapsulates the role of leadership in devising the right strategies based on careful market analysis (Stearns et al., 1995). The second dimension covers the broader spectrum of customer orientation. It is obvious that a firm cannot exist without customers. Thus, customer satisfaction, loyalty, perception, relationship, and customer-focused strategies are core variables that play a vital role in a firm’s very existence and its ability to sustain the longevity. Similarly, a financially strong firm, with consistent profitability and return on capital, effective cash flow management, and strict internal and external financial controls enable it to not only grow faster and outperform the competitors but also guarantees sustainable longevity. The fourth dimension, learning and growth, is primarily concerned with organizational ability to learn and adapt to the rapidly changing globalized environment. For this purpose, a proactive research and development unit with the ability to anticipate the forthcoming consumer needs and respond in a timely manner provide a competitive advantage to the firm that ultimately leads to sustainable longevity. The internal capabilities dimension deals with organizational resources and capabilities. From physical to financial and from technological to human resources, all are valuable assets of the firm serving as key success factors that the firm can transform into core competencies to achieve competitive advantage. Efficient resource utilization, managerial competencies, superior quality, and project management and sustainable production are considered the core organizational capabilities. Whereas, firm technology, culture, interdepartmental integration, human capital, and firm experience curve are considered the core resources of the organization to build upon the competencies. Relatively stronger internal capabilities enable the firm to achieve competitive advantage over its competitors and at the same time enhance performance and its prospects for sustainable longevity.

This novel piece of work on CSL opens new avenues for future research. The future research can focus on testing the CSL scale in various firm types and geographical contexts. Moreover, the researchers can explore the relationships of a wide range of variables with CSL as antecedents or consequences. As we focused only on the internal aspects of the firm that contribute to firm longevity, future research might explore the external aspects to conceptualize a holistic view of CSL. Subsequently, the holistic concept of CSL can be operationalized by following the methodological framework employed in this study. It is important to note that this study has certain limitations. As managers of the firm do not have direct control over the external environmental factors as they do over the internal, this study focused mainly on the internal aspects of the firm that enable it in achieving CSL. Moreover, this study was conducted in privately held firms in a specific geographic perspective. Therefore, its application to other types of firms, especially the publicly listed firms and firms operating in geographic regions with different characteristics, may require adaptation. However, these gaps invite the attention of scholars to extend the prospects of CSL and cross-validate the scale across the geographic regions and in different types of industry.

Footnotes

Appendix

Detail of Items Excluded From Analysis at Various Stages.

List of excluded items	Specific reason
I. Dropped in content validity
1. Our company is expected to grow its profit higher than the industry average in the next 5 years.	In the third round of the Delphi Method, the experts did not approve the inclusion due to redundancy. Some items were rephrased and some other were merged.
2. Our company has been successful in maintaining a higher credit rating score in the past 5 years
3. Our company has the ability to utilize its resources available to meet its short-term commitments.
4. Our company has an ability to meet its principal payments on long-term debt.
5. Our company has higher assets growth compared with the industry average in the past 5 years.
6. Our company tends to outperform its competitors.
7. Our company is able to identify the market opportunities prior to the competitors.
8. Our company seizes the market opportunities prior to the competitors.
9. Our company has more prospects for future expansion in other markets compared with competitors.
10. Our company’s strategy for competitive advantage is based on the understanding of customers’ needs.
11. Our company rapidly responds to competitive actions that threaten it.
12. Our company always ensures that the right leaders are in place to meet the company’s goals.
13. Our company’s management is committed to the company.
14. In our company, managers possess the necessary qualification for the position they hold.
15. In our company, managers possess the necessary skills for the positions they hold.
16. In our company, the top management is capable of anticipating the future challenges.
17. In our company, managers are successful in achieving the organizational objectives.
18. Our company is sensitive to external environmental changes.
19. Our company frequently adapts to new technologies.
20. Our company is quite flexible in accepting the changes.
II. Dropped in face validity
1. Our company has adequate credit availability to survive longer.	Not approved by experts
2. Our company is expected to continue its sales growth in the long run.	Not approved by experts
III. Dropped in EFA
1. Our company has an ability to offer quality products/services at competitive prices in the long run.	Items not loaded at 0.70 cutoff value
2. Our company believes that customer satisfaction is vital for long-term survival.
3. Our company’s products/services are superior to competitors in terms of meeting customers’ needs.
4. Our company’s financial performance is audited by external auditors.
5. Our company integrates all business functions to transform customer needs of products/services.
6. Our company clearly allocates the project tasks and responsibilities.
7. Our company reviews project tasks on a periodic basis.
8. Our company uses measurements to determine the status of project tasks.
9. Our company maximizes the automation of processes for long-term growth.
10. Our company has good credibility in providing the product/services required for long-term survival.
11. Our company pays close attention to product/service quality for long-term results.
12. Our company ensures that owners’/investors’ investments are profitable in the long-term.
13. Our company respects the financial interests of all its owners/investors in the long run.
14. Our company’s managers are committed to achieving long-term organizational goals.
15. Our company has required managerial competency to run the company in the long run.	Dropped due to commonality value less than 0.60
16. Our company engages in dynamically changing itself to better serve its customers.
17. Our company is successful in the maximum utilization of resources for long-term survival.
18. Our company continuously organizes management development programs for long-term results.
IV. Dropped in internal consistency
1. Our company employees are given proper training for the assigned jobs.	Cronbach’s α <.60
2. Our company communicates with owners/investors in a transparent manner for a long-term relationship.	Cronbach’s α <.60
V. Dropped in CFA
1. Our company has a dynamic strategy formulation and implementation system.	Did not conform to construct validity
2. Our company has an ability to offer quality products/services at competitive prices in the long run.
3. Our company’s strategy for competitive advantage is based on the customers’ needs.

Note. EFA = exploratory factor analysis; CFA = confirmatory factor analysis.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Shabir Ahmad

Author Biographies

Shabir Ahmad is research scholar at Azman Hashim International Business School, Universiti Teknologi Malaysia. His research focuses on corporate survival strategies, corporate governance, corporate social responsibility and family businesses. His work has been published in International Journal of Law and Management.

Rosmini Omar is an associate professor of management at Azman Hashim International Business School, Universiti Teknologi Malaysia. Her work focuses specifically on issues of business and management in emerging economies that include the development of small and medium enterprises, talent management, and organizational behavior. Currently, she is pursuing an Erasmus+ Capacity Building for Higher Education grant project in assessing and improving research performance in South East Asian universities.

Farzana Quoquab is an associate professor at Azman Hashim International Business School, Universiti Teknologi Malaysia. She has published five books and eight book chapters. She has published numerous articles in peer-reviewed international journals and severs as editorial board members of several international journals. Her current research interest includes Consumer Behavior, Services Marketing, Green & Sustainability Marketing, Social Marketing, Macro Marketing, and Work Ethics & Values.

References

Abraham

S. E.

Karns

L. A.

Shaw

Mena

M. A.

(2001). Managerial competencies and the managerial performance appraisal process. Journal of Management Development, 20, 842-852.

Afghan

Wiqar

(2007). Succession in family businesses of Pakistan: Kinship culture and Islamic inheritance law. Lahore, Punjab, Pakistan: Centre for Management and Economic Research, Lahore University of Management Sciences.

Agarwal

Echambadi

Franco

A. M.

Sarkar

M. B.

(2004). Knowledge transfer through inheritance: Spin-out generation, development, and survival. Academy of Management Journal, 47, 501-522.

Ahmad

Omar

(2016). Basic corporate governance models: A systematic review. International Journal of Law and Management, 58, 73-107.

Ahn

S.-Y.

(2018). Founder succession, the imprint of founders’ legacies, and long-term corporate survival. Sustainability, 10(5), 1485.

Anderson

E. W.

Fornell

Lehmann

D. R.

(1994). Customer satisfaction, market share, and profitability: Findings from Sweden. Journal of Marketing, 58, 53-66.

Anderson

J. C.

Rungtusanatham

Schroeder

R. G.

(1994). A theory of quality management underlying the Deming management method. Academy of Management Review, 19, 472-509.

Aubry

Hobbs

(2011). A fresh look at the contribution of project management to organizational performance. Project Management Journal, 42, 3-16.

Audretsch

D. B.

Houweling

Thurik

A. R.

(2000). Firm survival in the Netherlands. Review of Industrial Organization, 16, 1-11.

10.

Backman

Gabe

Mellander

(2016). Effects of human capital on the growth and survival of Swedish businesses. Journal of Regional Analysis and Policy, 46, 22-38.

11.

Balestrero

Udo

(2013). Organizational survival: Profitable strategies for a sustainable future. United States: McGraw-Hill.

12.

Barney

(1991). Firm resources and sustained competitive advantage. Journal of Management, 17, 99-120.

13.

Becsi

(2002). Endogenous longevity and the value-maximizing firm. Economics Bulletin, 5(7), 1-7.

14.

Berman

S. L.

Wicks

A. C.

Kotha

Jones

T. M.

(1999). Does stakeholder orientation matter? The relationship between stakeholder management models and firm financial performance. Academy of Management Journal, 42, 488-506.

15.

Bessant

Francis

(1999). Developing strategic continuous improvement capability. International Journal of Operations & Production Management, 19, 1106-1119.

16.

Boga

Ensari

(2009). The role of transformational leadership and organizational change on perceived organizational success. The Psychologist-Manager Journal, 12, 235-251.

17.

Brito

R. P.

Brito

L. A. L.

(2014). Dynamics of competition and survival. Brazilian Administration Review, 11, 64-85.

18.

Cefis

Marsili

(2005). A matter of life and death: Innovation and firm survival. Industrial and Corporate Change, 14, 1167-1192.

19.

Chen

C.-H.

Chang

Y.-Y.

Lin

M. J. J.

(2010). The performance impact of post-M&A interdepartmental integration: An empirical analysis. Industrial Marketing Management, 39, 1150-1161.

20.

Coleman

Cotei

Farhat

(2013). A resource-based view of new firm survival: New perspectives on the role of industry and exit route. Journal of Developmental Entrepreneurship, 18(1), 1350002.

21.

Cottrell

Nault

B. R.

(2004). Product variety and firm survival in the microcomputer software industry. Strategic Management Journal, 25, 1005-1025.

22.

Cressy

(2006). Why do most firms die young? Small Business Economics, 26, 103-116.

23.

Creswell

J. W.

(2013). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). Singapore: Sage.

24.

Dalkey

Helmer

(1963). An experimental application of the Delphi method to the use of experts. Management Science, 9, 458-467.

25.

Darroch

(2005). Knowledge management, innovation and firm performance. Journal of Knowledge Management, 9, 101-115.

26.

Davis

(2014). Reflections on corporate longevity. McKinsey Quarterly, 3, 118-122.

27.

Debicki

B. J.

Kellermanns

F. W.

Chrisman

J. J.

Pearson

A. W.

Spencer

B. A.

(2016). Development of a Socioemotional Wealth Importance (SEWi) scale for family firm research. Journal of Family Business Strategy, 7, 47-57.

28.

Esteve-Pérez

Mañez-Castillejo

J. A.

(2008). The resource-based theory of the firm and firm survival. Small Business Economics, 30, 231-249.

29.

Fahed-Sreih

Djoundourian

(2006). Determinants of longevity and success in Lebanese family businesses: An exploratory study. Family Business Review, 19, 225-234.

30.

Fahed-Sreih

Djoundourian

(2008). Correlates of longevity: A regression model. Journal of Business and Entrepreneurship, 20, 61-71.

31.

Fang

C.-H.

Chang

S.-T.

Chen

G.-L.

(2010). Competency development among Taiwanese healthcare middle manager: A test of the AHP approach. African Journal of Business Management, 4, 2845-2855.

32.

Fatoki

(2013). The determinants of longevity of micro enterprises in South Africa. Journal of Economics, 4, 133-143.

33.

Flynn

B. B.

Schroeder

R. G.

Sakakibara

(1995). The impact of quality management practices on performance and competitive advantage. Decision Sciences, 26, 659-691.

34.

Gilbert

(1983). Corporate profitability and corporate longevity. Paper presented at the Fifth Institutional Technology Conference, Houston, TX, USA.

35.

Glenn

Stahl

(2009). Organisational agility: How business can survive and thrive in turbulent times. A report from the Economist Intelligence Unit, 1, 1-25. Retreived from DellEMC website: https://www.emc.com/collateral/leadership/organisational-agility-230309.pdfs

36.

Golicic

S. L.

Smith

C. D.

(2013). A meta-analysis of environmentally sustainable supply chain management practices and firm performance. Journal of Supply Chain Management, 49, 78-95.

37.

Gondal

U. H.

Shahbaz

(2012). Interdepartmental communication increases organizational performance keeping HRM as a mediating variable. Journal of Asian Business Strategy, 2(6), 127-141.

38.

Gravetter

Wallnau

(2014). Introduction to the t statistic. In Essentials of statistics for the behavioral sciences (8th ed.). Belmont: Cengage Learning.

39.

Grimonpont

(2016). The impact of customer behaviour on the business organization in the multichannel context (retail). Journal of Creating Value, 2, 56-69.

40.

Gupta

P. D.

Guha

Krishnaswami

S. S.

(2013). Firm growth and its determinants. Journal of Innovation and Entrepreneurship, 2, 1-15.

41.

Hair

J. F.

Anderson

R. E.

Tatham

R. L.

(1994). Multivariate data analysis with readings (4th ed.). London, UK: Macmillan Publishing.

42.

Hair

J. F.

Black

W. C.

Babin

B. J.

Anderson

R. E.

Tatham

R. L.

(2006). Multivariate data analysis (6th ed.). Upper Saddle River, NJ: Pearson Prentice Hall.

43.

Hatum

Pettigrew

(2004). Adaptation under environmental turmoil: Organizational flexibility in family-owned firms. Family Business Review, 17, 237-258.

44.

Hillebrand

(2018). Innovation in family firms—A generational perspective. Journal of Family Business Management. Advance online publication. doi:10.1108/JFBM-04-2018-0011

45.

Hinkin

T. R.

Tracey

J. B.

Enz

C. A.

(1997). Scale construction: Developing reliable and valid measurement instruments. Journal of Hospitality & Tourism Research, 21, 100-120.

46.

L.-t.

Bentler

P. M.

(1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6, 1-55.

47.

Ibrahim

A. B.

McGuire

Soufani

(2009). An empirical investigation of factors contributing to longevity of small family firms. Global Economics & Finance Journal, 2(2), 1-21.

48.

Irefin

I. A.

(2013). Effects of project management on the performance of a construction firm in Nigeria. American International Journal of Contemporary Research, 3(6), 54-58.

49.

Jannoo

Yap

Auchoybur

Lazim

(2014). The effect of nonnormality on CB-SEM and PLS-SEM path estimates. International Journal of Mathematical and Computational Sciences, 8, 285-291.

50.

Jung

J. Y.

Wang

Y. J.

(2006). Relationship between Total Quality Management (TQM) and Continuous Improvement of International Project Management (CIIPM). Technovation, 26, 716-722.

51.

Kalleberg

A. L.

Leicht

K. T.

(1991). Gender and organizational performance: Determinants of small business survival and success. Academy of Management Journal, 34, 136-161.

52.

Kaplan

R. S.

Norton

D. P.

(1996). The balanced scorecard: Translating strategy into action. Massachusetts: Harvard Business School Press.

53.

Kaplan

R. S.

Norton

D. P.

(2000, September-October). Focusing your organization on strategy-with the balanced scorecard. Harvard Business Review, 2, 1-62.

54.

Katila

Ahuja

(2002). Something old, something new: A longitudinal study of search behavior and new product introduction. Academy of Management Journal, 45, 1183-1194.

55.

Kim

Gao

(2013). Does family involvement increase business performance? Family-longevity goals’ moderating role in Chinese family firms. Journal of Business Research, 66, 265-274.

56.

Kline

R. B.

(2005). Principles and practice of structural equation modeling (2nd ed.). New York, NY: Guilford Press.

57.

Kole

Lehn

(1997). Deregulation, the evolution of corporate governance structure, and survival. The American Economic Review, 87, 421-425.

58.

Kozak

(2018). Determinants of business survivability: Literature review. International Journal of Synergy and Research, 6, 183-191.

59.

Kroes

J. R.

Manikas

A. S.

(2014). Cash flow management and manufacturing firm financial performance: A longitudinal perspective. International Journal of Production Economics, 148, 37-50.

60.

Lavie

Haunschild

P. R.

Khanna

(2012). Organizational differences, relational mechanisms, and alliance performance. Strategic Management Journal, 33, 1453-1479.

61.

Marimuthu

Arokiasamy

Ismail

(2009). Human capital development and its impact on firm performance: Evidence from developmental economics. The Journal of International Social Research, 2, 265-272.

62.

Matzler

Bailom

(2007). Enduring success: What top companies do differently. New York: Palgrave Macmillan.

63.

McDowell

W. C.

Harris

M. L.

Geho

P. R.

(2016). Longevity in small business: The effect of maturity on strategic focus and business performance. Journal of Business Research, 69, 1904-1908.

64.

Menguc

Auh

Ozanne

(2010). The interactive effect of internal and external factors on a proactive environmental strategy and its influence on a firm’s performance. Journal of Business Ethics, 94, 279-298.

65.

Mwobobia

F. M.

(2012). Role of business management into the success and survival of small businesses: The case of star learning centre in Botswana. International Journal of Business Administration, 3(1), 93-112.

66.

Napolitano

Marino

Ojala

(2015). In search of an integrated framework of business longevity. Business History, 57, 955-969.

67.

Narver

J. C.

Slater

S. F.

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

68.

Neff

J. E.

(2015). Shared vision promotes family firm performance. Frontiers in Psychology, 6, 646.

69.

Nicolò

(2015). Towards a theory on corporate reputation and survival of young firms. Procedia Economics and Finance, 22, 296-303.

70.

Nwanyanwu

(2015). Cashflow and organizational performance in Nigeria: Hospitality and print media industries perspectives. European Journal of Business, Economics and Accountancy, 3(3), 66-70.

71.

Oliveira

Roth

A. V.

(2012). Service orientation: The derivation of underlying constructs and measures. International Journal of Operations & Production Management, 32, 156-190.

72.

Panza

Ville

Merrett

(2018). The drivers of firm longevity: Age, size, profitability and survivorship of Australian corporations, 1901–1930. Business History, 60, 157-177.

73.

Parker

Peters

G. F.

Turetsky

H. F.

(2002). Corporate governance and corporate failure: A survival analysis. Corporate Governance: The International Journal of Business in Society, 2(2), 4-12.

74.

Pérotin

Robinson

(2004). Employee participation, firm performance and survival. West Yorkshire, England: Emerald Group.

75.

Pishdad

Haider

(2013, July 28-August 1). Responding to external and internal changes: Organizational responsiveness pressures in institutionalising ERP systems. Paper presented at the Technology Management in the IT-Driven Services (PICMET), San Jose, CA.

76.

Prahalad

C. K.

(1993). The role of core competencies in the corporation. Research-Technology Management, 36(6), 40-47.

77.

Radzi

K. M.

Nor

Nazri

Ali

S. M.

(2017). The impact of internal factors on small business success: A case of small enterprises under the FELDA scheme. Asian Academy of Management Journal, 22, 27-55.

78.

Reeves

Levin

Ueda

(2016, January-February). The biology of corporate survival. Harvard Business Review, 94, 46-55.

79.

Saunders

M. N.

(2011). Research methods for business students (5th ed.). Harlow, UK: Pearson Education.

80.

Senge

P. M.

(2006). The fifth discipline: The art & practice of the learning organization. New York, NY: Doubleday.

81.

Sharma

K. R.

Dixit

M. R.

(2017). Live long and prosper: The search for business longevity. Strategic Direction, 33(11), 1-3.

82.

Sharma

K. R.

Dixit

M. R.

(2018). Longevity challenges and leadership interventions: Strategy journeys of two Indian banks. Business History, 60, 178-201.

83.

Sharma

Salvato

(2013). Family firm longevity: A balancing act between continuity and change. New York, NY: Cambridge University Press.

84.

Stadler

(2007, July/August). The four principles of enduring success. Harvard Business Review, 85(7/8), 62-72.

85.

Stearns

T. M.

Carter

N. M.

Reynolds

P. D.

Williams

M. L.

(1995). New firm survival: Industry, strategy, and location. Journal of Business Venturing, 10, 23-42.

86.

Tabachnick

B. G.

Fidell

L. S.

(2007). Using multivariate statistics (5th ed.). Massachusetts: Allyn & Bacon, Inc.

87.

Tahir

S. H.

Saleem

M. S.

Aziz

(2014). Impact of family ownership concentration on value of firm: A family and non-family comparative analysis of automobile companies listed in KSE (Pakistan). Research Journal of Commerce & Behavioural Science, 3(6), 8-18.

88.

Teece

D. J.

(2007). Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28, 1319-1350.

89.

Tseng

M.-L.

Divinagracia

(2009). Evaluating firm’s sustainable production indicators in uncertainty. Computers & Industrial Engineering, 57, 1393-1403.

90.

Walsh

Beatty

S. E.

(2007). Customer-based corporate reputation of a service firm: Scale development and validation. Journal of the Academy of Marketing Science, 35, 127-143.

91.

Ward

J. L.

(2011). Keeping the family business healthy: How to plan for continuing growth, profitability, and family leadership. New York: Palgrave Macmillan.

92.

Watson

(2007). Modeling the relationship between networking and firm performance. Journal of Business Venturing, 22, 852-874.

93.

Wernerfelt

(1984). A resource-based view of the firm. Strategic Management Journal, 5, 171-180.

94.

Williams

Jones

(2010). Factors associated with longevity of small, family-owned firms. International Journal of Entrepreneurship, 14, 37-57.

95.

Zikmund

Babin

Carr

Griffin

(2012). Business research methods. Ohio: Cengage Learning.