The Impact of Dynamic Capability on Business Sustainability of Organizations

Abstract

A firm’s dynamic capability is its ability to sense, seize and reconfigure its internal competencies to address any external changes to the business environment. Social customer relationship management (SCRM) is an organization’s marketing capability to use social media services and technologies to connect more closely with their customers and meet customers’ needs. Few studies have examined the influence of organization’s dynamic capability on its business sustainability. There is also no study which investigated the moderating role of SCRM on the relationship between an organization’s dynamic capability and its business sustainability, although there is a growing interest among researchers, academicians and practitioners to understand this relationship. Therefore, the aim of this study is to examine the impact of dynamic capability on an organization’s business sustainability, with SCRM as the moderator. With the help of dynamic capability view theory and the literature, a model has been developed conceptually. Later, the model is validated with the structural equation modelling technique by considering 302 respondents from different types of Indian organizations. The study finds that an organization’s dynamic capability has considerable impact on business sustainability. Also, the study confirms that SCRM has a significant moderating role in the relationship between organizations’ dynamic capability and business sustainability.

Keywords

Dynamic capability Social customer relationship management Sustainability Sensing ability Seizing ability Reconfiguring ability Dynamic capability view

Introduction

Sustainability is a concept which is closely associated with the modern business environment (Madsen & Ulhøi, 2021). Previously, scholars had to distinguish between profitable organizations and sustainable organizations, but modern business philosophy considers sustainability not merely essential for survival but as an opportunity for investment (Basile et al., 2021; Smyczek et al., 2020). Although the philosophy behind the concept of sustainability of an organization has not changed over the years, the modern style of business seems to be compelling it to change (Christofi et al., 2021) in that sustainability should not be constructed as something to obey but something which is needed to be exploited (Bhattacharya & Chatterjee, 2020; Festa et al., 2009; Quttainah, 2020).

Peterson et al. (2021) opined that sustainability is a profitable interaction that establishes a nexus between societal marketing and social marketing. Social marketing attempts to integrate social issues into commercial marketing strategies, whereas societal marketing is a business-driven, profit-oriented marketing process (Andrianova & Yeletskikh, 2012). Thus, it is obvious that for organizations to achieve sustainability they must develop abilities to meet their customers’ changing needs in the dynamic business environment. To assess what the needs of the customers are, close interaction with the customers is necessary (Hatton et al., 2017). Thus, organizations must have abilities to perceive the changing markets and to develop their relationship with the customers to see to their needs (Chatterjee, 2019a; Nguyen & Chatterjee, 2021). In this scenario, it is argued that organizations must possess dynamic abilities and the ability to easily interact with customers (Ma & Kim, 2021).

Dynamic capability view (DCV) theory (Teece et al., 1997) explains how to develop dynamic abilities to adapt to changing markets and to take recourse in social customer relationship management (SCRM) to closely interact with customers to meet their ever-changing needs. Dynamic capability can be developed by enhancing sensing, seizing and reconfiguring abilities (Teece, 2012, 2014), which could impact organizations’ sustainability. Social media usage has impacted the customer behaviour revolutionizing the CRM style (Bowen & McCain, 2015; Nguyen et al., 2019; Rana, 2019; Trainor, 2012). The concept of dynamic capability has attracted scholars and practitioners in the field of management strategy (Eisenhardt & Martin, 2000) and has expanded to other management fields including entrepreneurship, management information systems, marketing, technology and innovation management, HR management, and operational management (Schilke et al., 2018). However, the literature in this context has remained fragmented and dissimilar (Baía & Ferreira, 2019) especially regarding the impact of dynamic capabilities on business sustainability of the organizations, moderated by boundary conditions of SCRM capabilities, thus leaving a gap in the extant literature. To fill this gap, the aim of this study is to address the following research questions (RQs).

RQ1: How can an organization’s dynamic capability impact its business sustainability?

RQ2: Is there any moderating impact of social customer relationship management on organization sustainability?

To address the research questions, the inputs of 302 respondents have been statistically analyzed. Also in this context, a theoretical model has been proposed, which has been duly tested by the factor-based partial least squares structural equation modelling technique. To theoretically establish the empirical findings, DCV theory (Teece et al., 1997) has been applied to explain how the dynamic capabilities (Teece, 2012) could impact business sustainability of the organizations under the moderating impact of SCRM ability.

The remaining parts of the article have been organized as follows. The second section describes the literature review, followed by discussions on the theoretical background along with hypotheses development in the third section. Next, in the fourth section, the methodology of the present study has been enumerated, followed by analysis of data and the results in the fifth section. After that, the sixth section discusses the results, implications, limitations and future scope arising from this study.

Literature Review

Organizations’ sensing, seizing and reconfiguring abilities are often used to explain the heterogeneity of organizational performance (Harel et al., 2021; Teece et al., 1997). To achieve sustainability, organizations need to react and respond to customers’ diverse needs and develop their products or services accordingly, which requires them to have dynamic capabilities (Chatterjee, 2019b; Koronios et al., 2020; Morgan et al., 2018). They can also develop these abilities by enhancing their innovative abilities (Ekeledo & Sivakumar, 2004; Ghosh & Chaudhuri, 2019; Morgan et al., 2018). Organizations must cope with continuously changing market environments and even thrive by exploring the available business opportunities (Alshirah et al., 2020; Lu & Ramamurthy, 2011). The rapid growth of information technology (IT) in business operations helps organizations to adjust and reshape their dynamic ability to succeed in becoming sustainable (Bharadwaj, 2000; Prahalad & Krishnan, 2002).

Scholars differ in their opinions on how dynamic capabilities help organizations to become sustainable (Peteraf, 1993; Quttainah, & Said, 2021; Rana, 2021). Organizations do not want to alter their existing practices abruptly when the market environment changes; they try to address the situation with their existing resources while planning future business practices to achieve sustainable business growth (Ludwig & Pemberton, 2011). To address dynamic market environments, organizations have to adjust their strategic learning cycle by sensing, seizing and reconfiguring the available opportunities to achieve sustainability (Jeannet & Schreuder, 2015; Teece, 2007).

Eisenhardt and Martin (2000) and Zahra (2006) have demonstrated that these three types of organizational abilities need to be developed so an organization can meet new challenges and integrate new strategic assets, including customer feedback, to ensure its sustainability. At the same time, close interactions with customers might also help organizations to develop those abilities. Therefore, in order to better manage customer relations to know more about their habits, likes and dislikes, social media can play a vital role (Gayathri et al., 2012; Ghosh & Chaudhuri, 2020), which is the main concept behind SCRM (Awasthi & Sangle, 2012). In fact, organizations’ interactions with customers have drastically changed with the introduction of social media, which substantially influences business activities that help organizations to develop sustainable business growth (Aral, 2011; Chatterjee, 2020; Ghosh et al., 2021; Todri & Adamopoulos, 2014). How an SCRM system could facilitate the relationship between an organization and the factors that predict its sustainability has not been studied extensively, and this present study has taken a holistic attempt to fill this gap.

Theoretical Background and Hypotheses Development

Theoretical Background

The present study has investigated how different dynamic capabilities could impact organization sustainability under the moderating effects of SCRM ability. From this perspective, the authors deemed that DCV theory (Teece et al., 1997) can help identify the factors which could influence organizational sustainability to address changing market environments. DCV is conceptualized as ‘high-level routine (or collection of routines) that, together with its implementing input flows, confers upon an organization’s management a set of decision options for providing significant outputs of a particular type’ (Winter, 2003, p. 991). Dynamic capability (DC) is considered a higher-order capability to explain sustainability and competitive advantage in volatile, changing markets (Eckstein et al., 2015). DC can be conceptualized with the help of three salient dimensions which are sensing capability, seizing capability, as well as reconfiguring capability (Teece, 2014). These capabilities help organizations to be sustainable by reducing costs (Wilden et al., 2013), increasing profitability (Teece, 2007), and generating new products and processes according to the needs of the customers in the changing business environments (Pezeshkan et al., 2016). Augier and Teece (2009) demonstrated that ‘if a firm possesses resources/competencies but lacks dynamic capabilities, it has a chance to make a competitive return for a short period, but superior returns cannot be sustained’ (p. 412). Thus, organizations’ dynamic abilities, which are comprised of sensing, seizing and reconfiguring capabilities, are perceived to ensure organizational sustainability. The impacts of the three abilities on organizational sustainability are perceived to be strengthened if organizations and customers interact more. As already discussed, dynamic capabilities such as sensing, seizing and transforming abilities are perceived to impact the sustainability of organizations, which is considered a contemporary mantra for modern business (Madsen & Ulhøi, 2021) and is considered not only a necessity for survival of organizations but also a lucrative opportunity for investment (Smyczek et al., 2020). Business sustainability is affected by better customer–organization interaction, impacted by the proper use of SCRM (Greenberg, 2010). SCRM adds functions, social features and processes to traditional CRM activities, which organizations need to exploit to enhance interactions with customers (Woodcock et al., 2011). SCRM is considered a business strategy of profoundly engaging customers through social media to build trust and enhance brand loyalty (Greenberg, 2010). Thus, an organization’s three dynamic abilities (sensing, seizing and reconfiguring) are perceived to impact its sustainability, and they are facilitated by the influence of the organization’s SCRM ability.

Development of Hypotheses and Conceptual Model

From the inputs of the literature review and from the theories, the authors have identified which dynamic capabilities of an organization are perceived to impact organizational sustainability. The authors also consider the moderating effects of organizations’ SCRM abilities on the impact of those capabilities. In this section, all these determinants are explained and then some hypotheses are formulated to develop a theoretical model.

Sensing Capability

From DCV theory (Teece et al., 1997), an organization’s sensing capability (SEN) can be considered a vital dynamic ability of an organization. With this capability, an organization can develop, co-develop and even estimate technological opportunities to address customers’ everchanging needs (Wu, 2010). The ability also helps the organization to develop and articulate marketing strategies in order to realize customers’ preferences as they change (Dwivedi & Rana, 2021; Lu & Ramamurthy, 2011; Piccolo et al., 2021). With SEN, an organization can also adopt modern innovative technology that can be applied in expanding its business domestically as well as internationally (Nguyen et al., 2020). Another benefit of this ability is that organizations can effectively sense the necessary external information to develop sustainability (Eckstein et al., 2015). Accordingly, it is hypothesized as follows.

H₁: Sensing capability (SEN) of an organization positively impacts organization sustainability (SUS).

Seizing Capability

Seizing capability (SEI) of an organization helps it to appropriately make use of the sensed opportunities, which therefore makes it possible for the organization to select the appropriate business model to develop the products and services, to properly allocate essential resources, and to commercialize such products or services on the markets (Cepeda & Vera, 2007). SEI is comprised of essential dimensions which are concerned with the vertical integration of innovation strategies to develop market-related products. Eisenhardt and Martin (2000) also found that with SEI, organizations can take appropriate decisions to transform into sustainable organizations. Organizations with SEI can take advantage of opportunities at the right time to address volatile dynamic markets (Teece et al., 1997) and produce innovative results with knowledge gained with their sensing ability (Haapanen et al., 2020). Mobilizing the resources to meet customers’ needs in a dynamic market can be achieved by the organization’s seizing ability, which helps it to become sustainable (Wamba et al., 2019). SEI helps an organization to learn which opportunities are the best to take, and as such, it is also called the ‘learning ability’ (Wamba et al., 2019; Wilhelm et al., 2015). Accordingly, it is hypothesized as follows.

H₂: Seizing capability (SEI) of an organization positively impacts organization sustainability.

Reconfiguring Capability and Sustainable Organization

According to Teece (2007), reconfiguring capability (REC) is the ability to integrate, create, renovate and develop the existing resources for innovating products and services. Organizations need to reconfigure their resources to ensure they remain competitive and sustainable when responding to environmental changes (Chatterjee et al., 2021). Organizations need to be able to coordinate, manage and appropriately control several diversified organizational activities to develop their technological innovativeness, which enhances their marketing ability (Becker & Dietz, 2004; Kar & Mustafa, 2019). REC helps an organization to build an effective knowledge management system to develop problem-solving skills that transform relevant information among stakeholders (Gold et al., 2001). Also, an organization with REC ability can utilize the knowledge that comes from external sources to achieve sustainability (Becker & Dietz, 2004). Ajgaonkar et al. (2021) opined that an organization that is sustainable can meet its present needs without compromising its ability to address its future needs. Finally, sustainability is associated with an organization’s economic development and this is perceived to be achieved through its REC ability. Accordingly, it is hypothesized as follows.

H₃: Reconfiguring capability (REC) of an organization positively impacts organization sustainability.

Moderating Effects of Social Customer Relationship Management Ability

Studies demonstrate that technological advancement and the impacts of social media on the behaviour of the customers have brought change to traditional CRM (Malthouse et al., 2013; Trainor, 2012). Using social media, customers can drive the conversation, which influences organizations to learn how to prioritize the experience and develop relationships with the customers (Dessart et al., 2015; Lipiäinen, 2015; Sreenivasulu, 2019). Customers using social media seek to build attitudinal loyalty, and they are not interested in only enjoying transactional benefits, but they also want to have effective personalized services at any place and at any time (Chaudhuri, & Vrontis, 2020; Mayock, 2014; Sigala, 2015).

Organizations can use social media platforms to gather, analyse and exploit market information for better understanding their customers (Dutot, 2013; Küpper et al., 2015). They can also use SCRM to personalize the customer experience (Acker et al., 2011; Trainor, 2012). Therefore, SCRM ability (SCA) is perceived to help improve the sustainability of an organization. Accordingly, it is hypothesized as follows.

H_4a: SCRM ability (SCA) moderates the relationships between sensing capability (SEN) and organization sustainability.

H_4b: SCRM ability moderates the relationships between seizing capability (SEI) and organization sustain- ability.

H_4c: SCRM ability moderates the relationships between reconfiguring capability (REC) and organization sustainability.

With all these inputs, a conceptual model is proposed, which is shown in Figure 1.

Figure 1.

The Conceptual Model (Adapted from DCV theory, Teece et al., 1997)

Research Methodology

To validate the model, the authors adopted a survey process and prepared a questionnaire (a set of questions). Responses were obtained from usable respondents who completed the questionnaire and they were quantified on a standard scale, which in this study was a five-point Likert scale. The points of the scale ranged from Strongly Disagree (SD), marked as 1, to Strongly Agree (SA), marked as 5. The 5-point Likert scale was preferred because it is simple to use, and respondents can take a neutral stance by selecting the “neither disagree nor agree” option. The survey process contained two steps. In the first step, the measurement instruments were prepared, and in the second step, responses were collected from the usable respondents.

Research Instruments: Measures and Scales

To test the hypotheses, a survey was conducted. This process is suitable for studies which aim to test hypotheses, to describe a population, to develop the measurement scale and eventually to develop a theoretical model (Lee & Shim, 2007). After primarily preparing the instruments, they were pretested, then piloted, and eventually refined by the inputs from experts who have adequate knowledge in the field of this study. With the inputs from the theories and the extant literature, some questions in the form of statements were prepared. For improving the readability of the questions, a rectification process has been adopted, as recommended by Carpenter (2018). The questions were pretested to enhance their readability and to correct some formats. After the pre-test stage, a pilot test was conducted to assess the expected response rate and the understandability of the instruments. The pilot test was conducted with a small sample, which was different from the sample of the main survey. The outcomes of the pilot test helped to improve the instruments to enhance their understandability and readability. Some instruments were dropped, as they were deemed to not interpret the corresponding construct appropriately. After the pilot test, experts who have adequate knowledge and expertise in the domain of this study provided their opinions about the comprehensiveness of the instruments, which helped to further improve the questionnaire. All these processes were followed to make the instruments simpler so that prospective respondents would not have any constraints to understand the questions and so the replies would be of good quality. In this way, 25 instruments were prepared.

Data Collection Procedure and Sample Characteristics

The aim of this study is to investigate how business sustainability of organizations is impacted by their dynamic capabilities under the moderating influence of SCRM. Hence, data were collected from respondents who have at least some preliminary knowledge of the subject matter of this study. Therefore, purposive sampling process was adopted (Apostolopoulos & Liargovas, 2016). This subjective, or judgemental, sampling is considered a non-probabilistic sampling method, where the researchers depend on their own judgement. Since most of the authors are based out of India, it was felt appropriate to target respondents there, which is considered a convenient sampling process (Garg, 2019). With that process, as well as purposive sampling, the researchers collected the inputs from the respondents. To collect the data, the researchers first obtained a list of organizations from the Bombay Stock Exchange (India). From the list, 40 organizations were then selected at random. Large as well as small and medium enterprises have been selected. After contacting the executives of the 40 organizations, it could be ascertained that only 27 organizations focus on improving their dynamic capabilities. The executives of these 27 organizations were then contacted and requested to allow their employees to take part in the survey. The executives were informed that the aim of this study was purely academic, and the anonymity and confidentiality of the respondents would be strictly preserved. With prolonged persuasion, ultimately the executives of 23 organizations agreed to allow their employees to participate in the survey. The researchers then contacted the top managers of those 23 organizations to obtain the details of 659 employees who were managers of different ranks. Each of the 659 prospective respondents was provided with a response sheet that contained the 25 instruments with five options for each instrument. Each respondent was instructed to put one tick mark in one of the five options. Guidelines were also given to each respondent describing how to fill in the response sheet. The 659 respondents were requested to reply within the two months (March and April 2021). Within the scheduled time, 314 respondents replied, which is a response rate of 47.6%. Upon scrutinizing the response sheets, the authors found that 12 replies were vague, and they were not considered. Therefore, analysis was done on 302 usable responses against 25 instruments. The demographic statistics of these 302 respondents are provided in Table 1.

Table 1.

Demographic Statistics (N = 302)

Particulars	Category	Number	%
Gender	Male	205	67.9
	Female	97	32.1
Rank	Employees	242	80.1
	Managers	60	19.9
Organization size	Small and medium enterprise	31	10.3
	Large enterprise	271	89.7

Data Analysis and Results

The data have been analysed and hypotheses have been tested by using the partial least squares structural equation modelling technique because it can successfully analyse an exploratory study like this (Peng & Lai, 2012). This technique differs from the covariance-based structural modelling technique (Kock & Hadaya, 2018) in that it does not impose any sample restriction (Hair et al., 2018) and can analyse any data which are not normally distributed.

Measurement Properties and Discriminant Validity Test

To examine the convergent validity of each item, the loading factor (LF) of each item was measured. To ascertain validity, reliability and internal consistency of each construct, average variance extracted (AVE), composite reliability (CR) and Cronbach’s alpha (α) were estimated. The estimated values are within the allowable range. The results are shown in Table 2.

It can be observed that the square roots of all the AVEs are more than the respective bi-factor correlation coefficients. This satisfies Fornell and Larcker’s criteria (Fornell & Larcker, 1981). The results confirm discriminant validity, and they are shown in Table 3.

Table 2.

Measurement Properties

Constructs/ Items	LF	AVE	CR	α	t-values
SEN		0.87	0.91	0.94
SEN1	0.94				22.11
SEN2	0.89				26.16
SEN3	0.97				24.01
SEN4	0.95				28.72
SEN5	0.95				29.17
SEN6	0.90				26.12
SEI		0.81	0.85	0.89
SEI1	0.86				28.11
SEI2	0.82				29.17
SEI3	0.92				24.25
SEI4	0.89				21.11
SEI5	0.85				27.02
SEI6	0.96				31.11
REC		0.84	0.87	0.92
REC1	0.89				24.17
REC2	0.96				34.19
REC3	0.91				22.31
REC4	0.92				24.38
REC5	0.87				27.29
REC6	0.90				39.13
SUS		0.85	0.89	0.93
SUS1	0.95				31.12
SUS2	0.85				37.17
SUS3	0.90				26.19
SUS4	0.96				24.31
SUS5	0.88				31.27
SUS6	0.97				25.32
SUS7	0.92				26.11

Table 3.

Discriminant Validity Test (Fornell & Larcker Criteria)

Construct	SEN	SEI	REC	SUS	AVE
SEN	0.93				0.87
SEI	0.17	0.90			0.81
REC	0.29	0.19	0.91		0.84
SUS	0.26	0.21	0.31	0.92	0.85

Moderator Analysis (Multigroup Analysis)

To examine the moderating effects of SCRM ability (SCA) on the linkages H₁, H₂ and H₃, multigroup analysis (MGA) with the bootstrapping procedure, considering 5,000 resamples, was adopted. The effects of SCA on the three linkages were categorized as High SCA and Low SCA. The p-value differences in the linkages are all found to be less than .05 (Hair et al., 2016), which confirms that the effects of the moderator SCA on the three linkages H₁, H₂ and H₃ are all significant. The results are shown in Table 4.

Table 4.

Moderator Analysis (Multi-group Analysis)

Linkages	Hypotheses	Path Coefficients	Remarks
(SEN→SUS) × SCA	H _4a	0.04	Significant
(SEI→SUS) × SCA	H _4b	0.01	Significant
(REC→SUS) × SCA	H _4c	0.03	Significant

Effect Size f² Test

To examine if the latent exogenous variables affect the corresponding latent endogenous variables, the effect size f² test was conducted. The values of f² were estimated for each linkage. The f² values are weak when they are between 0.020 and 0.150, they are medium when they are between 0.150 and 0.350, and they are large when they are greater than 0.350 (Cohen, 1988). The results are shown in Table 5.

Table 5.

Effect Size f²

Construct	SUS
SEN	0.393 (L)
SEI	0.226 (M)
REC	0.401 (L)

Note: L = Large, M = Medium.

Hypotheses Testing

To test the hypotheses, the bootstrapping procedure was followed with consideration of 5,000 resamples. Considering a separation distance 7, cross-validated redundancy has been assessed by estimating Q² values for the endogenous variables, which came out to be 0.062 (positive). Hence, it can be concluded that the model has predictive relevance. By using this process, it has been possible to assess the path coefficients of the different linkages along with other parameters. The results are shown in Table 6.

Table 6.

Structural Equation Modelling (SEM)

Linkages	Hypotheses	R²/Path Coefficients	P-values	Remarks
Effects on SUS		R² = 0.71
By SEN	H ₁	0.21	p < .001 (***)	Supported
By SEI	H ₂	0.19	p < .05(*)	Supported
By REC	H₃	0.17	p < .01(**)	Supported
(SEN→SUS) × SCA	H _4a	0.12	p < .05(*)	Supported
(SEI→SUS) × SCA	H _4b	0.16	p < .01(**)	Supported
(REC→SUS) × SCA	H _4c	0.14	p < .001(***)	Supported

Note: p < 0.05 (*); p < 0.01 (**), and p < 0.001 (***).

With all these inputs, the validated model is shown in Figure 2.

Figure 2.

Validated Model (SEM)

Results

The present study has formulated six hypotheses, out of which three are concerned with the effects of the moderator on the three linkages H₁, H₂ and H₃. The results demonstrate that the impacts of SEN, SEI and REC on SUS (H₁, H₂ and H₃) are all significant and positive, since the concerned path coefficients are 0.21, 0.19 and 0.17, respectively, with levels of significance as p < .001(***), p < .05(*) and p < .01(**). The effects of the moderator SCA on the three linkages H₁, H₂ and H₃ are found to be significant and positive, since the concerned path coefficients are 0.12, 0.16 and 0.14, with respective levels of significance as p < .05(*), p < .01(**) and p < .001(***). According to the coefficient of determination, SEN, SEI and REC could explain SUS to the extent of 71% (R² = 0.71), which is the overall predictive power of the proposed model.

Discussion

The present study has discussed the contemporary means, forms, as well as the issue of organizational sustainability within the strategy and marketing framework, especially balancing business goals along with social good in the spirit of compromise for mutual benefits. The present study has discussed that sustainability, impacted by dynamic abilities, is not only essential for an organization’s survival but it also offers scope to the leadership of organizations to nurse the environment and enhance the profitability of the organizations. The present study has focused on the point that sustainability is not merely something to obey but it is also something to exploit (Festa et al., 2009). Based on this conjecture, the present study has been able to establish a nexus between dynamic capabilities and sustainability, and this study has also projected how the effects of SCRM could tighten the bond between organizations’ dynamic capabilities and sustainability. Again, organizations’ dynamic abilities are construed as their processes and practices that enable them to achieve superior performances compared to their counterparts. The present study has documented how dynamic capabilities, comprising sensing, seizing and reconfiguring abilities, could impact organizations’ sustainability. This concept has received support from another study by Wilden et al. (2013), which documented the impact of these three abilities on the performance of organizations. The present study has observed the impact of the moderator SCA on the linkages covering H₁, H₂ and H₃, which received support from a study by Sigala (2018).

Now the effects of the moderator SCA, categorized as either High SCA or Low SCA, on the three linkages, H₁, H₂ and H₃, will be discussed graphically through and shown in Figures 3, 4 and 5, respectively.

Figure 3 shows the effects of SCA on H₁, where the continuous and dotted lines represent the effects of High SCA and Low SCA, respectively. As SEN increases, SUS increases more from the effects of Strong SCA on H₁ compared to the effects of Low SCA on H₁, since the gradient of the continuous line is more than the gradient of the dotted line.

Figure 3.

Effects of SCA on H₁

Figure 4 presents the effects of the moderator SCA on the linkage H₂. Here, we can see from the gradient of the continuous line, representing the effects of High SCA on H₂, that, as SEI increases, SUS increases at a greater rate than compared to the effects of Low SCA on H₂, represented by the dotted line.

Figure 4.

Effects of SCA on H₂

Figure 5, which again uses a continuous line and a dotted line for High SCA and Low SC, respectively, shows the moderating effects of SCA on the linkage H₃. With the increase of REC, the rate of increase of SUS is more for the effects of High SCA compared to the effects of Low SCA on H₃ since the gradient of the continuous line appears to be steeper than the gradient of the dotted line.

Figure 5.

Effects of SCA on H₃

Theoretical Contributions

The present research study provides several theoretical contributions to the extant literature. This study has documented that the three dynamic capabilities of the organizations, which are sensing capability, seizing capacity and reconfiguring capability, could impact organizations’ sustainability and their impacts are facilitated by the moderating influence of organizations’ SCRM ability. No other studies are known to have investigated such factors and also considering the effects of the moderator, SCRM ability. In this context, these efforts are deemed to be a unique attempt to enrich the extant literature.

The present study has successfully explained that an organization’s sensing ability helps it to identify the opportunities towards product and service innovation. If the organization fails to recognize changes in the dynamic business environments, its survival will not be sustained. The study was also able to substantiate that organizations could use this ability to react and respond to the changing environment to ensure sustainability. This study also successfully explains that organizations’ seizing capabilities can include market-related product and service development strategies and could define clear strategic goals (Eisenhardt & Martin, 2000). The results also clarify that reconfiguring ability can help organizations reconstruct, integrate and renovate their existing resources for better product and service innovation (Teece, 2007).

The present research has been able to theorize the effects of SCA as a moderator on the relationships between SUS and its three predictors. By such consideration, it has been possible to provide a successful theoretical model with such a high explanative power. A study by Sigala (2018) demonstrated that the transformation of CRM to SCRM brought about a paradigm shift from auto-solution to a philosophy of customer experience management, which reflects a high level of empowerment of customers. Sigala’s idea has been extended in this present study to show how the positive relationships between dynamic abilities and organizational sustainability could be facilitated by the moderating effects of organizations’ SCRM ability. The authors claim that this adds value to the extant literature.

Implications to Practice

The present study has been able to provide several practical implications. This study has shown that the sensing capability of an organization significantly and positively impacts its sustainability (H₁). This implies that the sensing ability can help an organization develop, co-develop and identify several opportunities to adopt innovative strategies for product creation, thus ensuring long-term profits. The present study has documented that seizing ability of an organization significantly and positively impacts its sustainability (H₂), which implies that managers of the organizations must properly seize as well as assimilate the sensed opportunities. This will help the organizations to develop a suitable business model to come up with new products and services commensurate with the needs of the customers in the changing market environment. Organiza-tions must strive to acquire appropriate information about their target markets so their businesses can flourish. This present research has documented that reconfiguring ability significantly and positively impacts organizations’ sustainability (H₃). This implies that to sustain profitable growth in the dynamic market, it is necessary for organiza-tions to successfully recombine their existing assets, as well as structures, according to what the market dictates. Thus, it is seen that to achieve sustainable growth, organizations need to enhance their sensing, seizing and reconfiguring abilities, and this could be accomplished by imparting proper training to the employees of the organizations to enrich the human capital.

The present research has shown that the SCRM abilities of organizations facilitate them to become more sustainable (H_4a, H_4b and H_4c). This implies that the managers of the organizations should keep employees aware of the effectiveness of their SCRM ability. Managers should ensure that employees interact with potential customers through social networking sites that are integrated with their organizations’ CRM system (SCRM), which will help them to know the customers better, and the customers will also have more chances to interact with the organization. When organizations understand their customers’ needs, they can then act accordingly to meet customers’ expectations.

Limitations and Future Scope

This study has several limitations. First, the data were collected from respondents in India. Hence, the results inputs suffer from the issue of external validity. It is suggested that future researchers should collect data from respondents dispersed across the globe, which could provide findings with generalizability. Second, the study has used the DCV theory (Teece et al., 1997), which suffers from the defect of context insensitivity (Ling-Yee, 2007). It is suggested that a future study should explore the optimum conditions in which dynamic capabilities could provide sustainability for organizations. Third, as this study depends on cross-sectional data, one cannot determine the causality between the relationships of the constructs. Therefore, the problem of endogeneity emerges, and to eliminate this, it is suggested that future researchers should conduct a longitudinal study. Fourth, the explanative power of the model is 71%. Future researchers may consider examining more constructs and other boundary conditions to consider if those could improve the explanative power of the model.

Footnotes

Declaration of Conflicting Interests

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

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

References

Acker

, Gröne

, Akkad

, Pötscher

, & Yazbek

(2011). Social CRM: How companies can link into the social web of consumers. Journal of Direct, Data and Digital Marketing Practice, 13(1), 3–10.

Ajgaonkar

, Neelam

N. G.

, & Wiemann

(2021). Drivers of workforce agility: A dynamic capability perspective. International Journal of Organizational Analysis. 10.1108/IJOA-11-2020-2507

Alshirah

M. H.

, Abdul Rahman

, & Mustapa

I. R.

(2020). Board of directors’ characteristics and corporate risk disclosure: The moderating role of family ownership. EuroMed Journal of Business, 15(2), 219–252.

Andrianova

, & Yeletskikh

(2012). Societal marketing: Integration of European experiences into business practices in Belarus. International Journal of Emerging Markets, 7(2), 107–131.

Apostolopoulos

, & Liargovas

(2016). Regional parameters and solar energy enterprises: Purposive sampling and group AHP approach. International Journal of Energy Sector Management, 10(1), 19–37.

Aral

(2011). Commentary-identifying social influence: A comment on opinion leadership and social contagion in new product diffusion. Marketing Science, 30(2), 217–223.

Augier

, & Teece

D. J.

(2009). Dynamic Capabilities and the Role of Managers in Business Strategy and Economic Performance. Organization Science, 20(2), 410–421.

Awasthi

, & Sangle

P. S.

(2012). Adoption of CRM technology in multichannel environment: A review (2006–2010). Business Process Management Journal, 18(3), 445–471.

Baía

, & Ferreira

J. J.

(2019). Dynamic capabilities and performance: How has the relationship been assessed? Journal of Management & Organization. 10.1017/jmo.2019.88

10.

Basile

, Chatterjee

, Chaudhuri

, & Vrontis

(2021). Digital transformation and entrepreneurship process in SMEs of India: A moderating role of adoption of AI-CRM capability and strategic planning. Journal of Strategy and Management. 10.1108/JSMA-02-2021-0049

11.

Becker

, & Dietz

(2004). Cooperation and innovation activities of firms: Evidence for the German manufacturing industry. Research Policy, 33(1), 209–223.

12.

Bharadwaj

A. S.

(2000). A resource-based perspective on information technology capability and firm performance: An empirical investigation. MIS Quarterly, 24(1), 169–196.

13.

Bhattacharya

, & Chatterjee

(2020). Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling. Education and Information Technologies, 24(1), 3443–3463.

14.

Bowen

J. T.

, & McCain

S. C.

(2015). Transitioning loyalty programs. International Journal of Contemporary Hospitality Management, 27(3), 415–430.

15.

Carpenter

(2018). Ten steps in scale development and reporting: A guide for researchers. Communication Methods and Measures, 12(1), 25–44.

16.

Cepeda

, & Vera

(2007). Dynamic capabilities and operational capabilities: A knowledge management perspective. Journal of Business Research, 60(1), 426–437.

17.

Chatterjee

(2019a). Impact of AI regulation on intention to use robots: From citizens and government perspective. International Journal of Intelligent Unmanned Systems, 8(2), 97–114.

18.

Chatterjee

(2019b). Why will people use IoT enabled devices? An empirical examination from Indian perspectives. International Journal of Technology and Human Interaction, 18(2), 33–61.

19.

Chatterjee

(2020). AI strategy of India: Policy framework, adoption challenges and actions for government. Transforming Government: People, Process and Policy, 14(5), 757–775.

20.

Chatterjee

, Chaudhuri

, & Vrontis

(2021). Knowledge sharing in international markets for product and process innovation: Moderating role of firm’s absorptive capacity. International Marketing Review. 10.1108/IMR-11-2020-0261

21.

Chaudhuri

, & Vrontis

(2020). Does data-driven culture impact innovation and performance of a firm? An empirical examination. Annals of Operational Research. 10.1007/s10479-020-03887-z

22.

Christofi

, Pereira

, Vrontis

, Tarba

, & Thrassou

(2021). Agility and flexibility in international business research: A comprehensive review and future research directions. Journal of World Business, 56(3), 1–18.

23.

Cohen

(1988). Statistical power analysis for the behavioural sciences (2nd ed.). Lawrence Erlbaum Associates.

24.

Dessart

, Veloutsou

, & Morgan Thomas

(2015). Consumer engagement in online brand communities: A social media perspective. Journal of Product & Brand Management, 24(1), 28–42.

25.

Dutot

(2013). A new strategy for customer engagement: How do French firms use social CRM? International Business Research, 6(9), 54–60.

26.

Dwivedi

Y. K.

, & Rana

N. P.

(2021). Assessing consumers’ co‐production and future participation on value co‐creation and business benefit: An F-P-C-B model perspective. Information Systems Frontiers. 10.1007/s10796-021-10104-0

27.

Eckstein

, Goellner

, Blome

, & Henke

(2015). The performance impact of supply chain agility and supply chain adaptability: The moderating effect of product complexity. International Journal of Production Research, 53(10), 3028–3046.

28.

Eisenhardt

K. M.

, & Martin

J. A.

(2000). Dynamic capabilities: What are they? Strategic Management Journal, 21(10/11), 1105–1121.

29.

Ekeledo

, & Sivakumar

(2004). International market entry mode strategies of manufacturing firms and service firms: A resource-based perspective. International Marketing Review, 21(1), 68–101.

30.

Festa

, Metallo

, & Cuomo

M. T.

(2009). Financial feasibility of sustainable events. In Raj

& Musgrave

(Eds), Event management and sustainability (pp. 249–258). CABI.

31.

Fornell

, & Larcker

D. F.

(1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

32.

Garg

(2019). Research methodology, in HR initiatives in building inclusive and accessible workplaces (pp. 61–83). Emerald Publishing.

33.

Gayathri

K. S.

, Thomas

, & Jayasudha

(2012). Security issues of media sharing in social cloud. Procedia Engineering, 38, 3806–3815.

34.

Ghosh

S. K.

, & Chaudhuri

(2019). Adoption of ubiquitous customer relationship management (uCRM) in enterprise: Leadership support and technological competence as moderators. Journal of Relationship Marketing, 19(2), 75–92.

35.

Ghosh

S. K.

, & Chaudhuri

(2020). Knowledge management in improving business process: An interpretative framework for successful implementation of AI–CRM–KM system in organizations. Business Process Management Journal, 26(6), 1261–1281.

36.

Ghosh

S. K.

, Chaudhuri

, & Chaudhuri

(2021). Adoption of AI-integrated CRM system by Indian industry: From security and privacy perspective. Information and Computer Security, 29(1), 1–24.

37.

Gold

A. H.

, Segars

A. H.

, & Malhotra

(2001). Knowledge management: An organizational capabilities perspective. Journal of Management Information Systems, 18(1), 185–214.

38.

Greenberg

(2010). The impact of CRM 2.0 on customer insight. Journal of Business & Industrial Marketing, 25(6), 410–419.

39.

Haapanen

, Hurmelinna-Laukkanen

, & Puumalainen

(2020). When strategic consensus matters: Dynamic managerial capabilities and firm internationalization as seen by TMT. Cross Cultural & Strategic Management, 27(3), 285–315.

40.

Hair

J. F.

Jr. , Hult

G. T. M.

, Ringle

, & Sarstedt

(2016). A primer on partial least squares structural equation modelling (PLS-SEM). SAGE Publications.

41.

Hair

J. F.

, Sarstedt

, Ringle

C. M.

, & Gudergan

S. P.

(2018). Advanced issues in partial least squares structural equation modelling (PLS-SEM). SAGE Publications.

42.

Harel

, Schwartz

, & Kaufmann

(2021). Organizational culture processes for promoting innovation in small businesses. EuroMed Journal of Business, 16(2), 218–240.

43.

Hatton

, Kolk

, Eikelenboom

, & Beaumont

(2017). Four approaches for staffing and structuring a product development team to identify the crucial unmet needs of B2B customers. Strategy & Leadership, 45(2), 25–32.

44.

Jeannet

, & Schreuder

(2015). From coal to biotech: The transformation of DSM with business school support. Springer.

45.

Kar

A. K.

, & Mustafa

S. Z.

(2019). Securing IoT devices in Smart Cities of India: From ethical and enterprise information system management perspective. Journal of Enterprise Information System, 15(4), 585–615.

46.

Kock

, & Hadaya

(2018). Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods. Information Systems Journal, 28(1), 227–261.

47.

Koronios

, Travlos

, Douvis

, & Papadopoulos

(2020). Sport, media and actual consumption behavior: An examination of spectator motives and constraints for sport media consumption. EuroMed Journal of Business, 15(2), 151–166.

48.

Küpper

, Wieneke

, Wittkuhn

, Lehmkuhl

, & Jung

(2015). Assessment schema for Social CRM tools: An empirical investigation [28th Bled e-Conference]. BLED 2015 Proceedings. 17. https://aisel.aisnet.org/bled2015/17

49.

Lee

C. P.

, & Shim

J. P.

(2007). An exploratory study of radio frequency identification (RFID) adoption in the healthcare industry. European Journal of Information Systems, 16(6), 712–724.

50.

Ling-Yee

(2007). Marketing resources and performance of exhibitor firms in trade shows: A contingent resource perspective. Industrial Marketing Management, 36(3), 360–370.

51.

Lipiäinen

H. S. M.

(2015). CRM in the digital age: Implementation of CRM in three contemporary B2B firms. Journal of Systems and Information Technology, 17(1), 2–19.

52.

, & Ramamurthy

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

53.

Ludwig

, & Pemberton

(2011). A managerial perspective of dynamic capabilities in emerging markets: The case of the Russian steel industry. Journal of East European Management Studies, 16(3), 215–236.

54.

, & Kim

(2021). How does salespeople’s personal interaction affect customers’ word-of-mouth in retailing? Journal of Asia Business Studies. 10.1108/JABS-03-2021-0112

55.

Madsen

H. L.

, & Ulhøi

J. P.

(2021). Sustainable visioning: Re-framing strategic vision to enable a sustainable corporate transformation. Journal of Cleaner Production, 288, 1–12.

56.

Malthouse

E. C.

, Haenlein

, Skiera

, Wege

, & Zhang

(2013). Managing customer relationships in the social media era: Introducing the social CRM house. Journal of Interactive Marketing, 27(4), 270–280.

57.

Mayock

(2014). Personalization equals loyalty for millennials. Hotel News Now, www.hotelnewsnow.com/article/13067/Personalization-equals-loyalty-formillennials

58.

Morgan

N. A.

, Feng

, & Whitler

K. A.

(2018). Marketing capabilities in international marketing. Journal of International Marketing, 26(1), 61–95.

59.

Nguyen

, & Chatterjee

(2021). Value co-creation and social media at bottom of pyramid (BOP). The Bottom Line, 34(2), 101–123.

60.

Nguyen

, Ghosh

, & Chaudhuri

(2019). Are CRM systems ready for AI integration? The Bottom Line, 32(2), 144–157.

61.

Nguyen

, Ghosh

S. K.

, Bhattacharjee

K. K.

, & Chaudhuri

(2020). Adoption of artificial intelligence integrated CRM system: An empirical study of Indian organizations. The Bottom Line, 33(4), 359–375.

62.

Peng

D. X.

, & Lai

(2012). Using partial least squares in operations management research: A practical guideline and summary of past research. Journal of Operations Management, 30(6), 467–480.

63.

Peteraf

M. A.

(1993). The cornerstones of competitive advantage: A resource-based view. Strategic Management Journal, 14(3), 179–191.

64.

Peterson

, Minton

E. A.

, Liu

R. L.

, & Bartholomew

D. E.

(2021). Sustainable marketing and consumer support for sustainable businesses. Sustainable Production and Consumption, 27, 157–168.

65.

Pezeshkan

, Fainshmidt

, Nair

, Frazier

M. L.

, & Markowski

(2016). An empirical assessment of the dynamic capabilities–performance relationship. Journal of Business Research, 69(8), 2950–2956.

66.

Piccolo

, Chaudhuri

, & Vrontis

(2021). Enterprise social network for knowledge sharing in MNCs: Examining the role of knowledge contributors and knowledge seekers for cross-country collaboration. Journal of International Management, 27(1), 100827.

67.

Prahalad

C. K.

, & Krishnan

M. S.

(2002). The dynamic synchronization of strategy and information technology. Sloan Management Review, 43(4), 24–33.

68.

Quttainah

M. A.

(2020). The role of organizational trust and JOY in Organization’s strategic behaviour. FIIB Business Review, 9(1), 42–54.

69.

Quttainah

M. A.

, & Said

D. S.

(2021). The directional nature of organizational relationships and their effect on individual performance and turnover. FIIB Business Review, 10(1), 5–15.

70.

Rana

(2019). Sustainability in business: Some research perspectives. FIIB Business Review, 8(2), 77–78.

71.

Rana

(2021). Digitalization as a vehicle to balance new vs old practices of consumers and organizations. FIIB Business Review, 10(3), 193–195.

72.

Schilke

, Hu

, & Helfat

C. E.

(2018). Quo vadis, dynamic capabilities? A content-analytic review of the current state of knowledge and recommendations for future research. The Academy of Management Annals, 12, 390–439.

73.

Sigala

(2015). From demand elasticity to market plasticity: A market approach for developing revenue management strategies in tourism. Journal of Travel and Tourism Marketing, 32(7), 812–834.

74.

Sigala

(2018). Implementing social customer relationship management: A process framework and implications in tourism and hospitality. International Journal of Contemporary Hospitality Management, 30(7), 2698–2726.

75.

Smyczek

, Festa

, Rossi

, & Monge

(2020). Economic sustainability of wine tourism services and direct sales performance: Emergent profiles from Italy. British Food Journal, 122(5), 1519–1529.

76.

Sreenivasulu

N. S.

(2019). Personal data sharing and legal issues of human rights in the era of artificial intelligence: Moderating effect of government regulation. International Journal of Electronic Government Research, 15(3), 21–36.

77.

Teece

D. J.

(2007). Explicating dynamic capabilities: The nature and micro foundations of (sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319–1350.

78.

Teece

D. J.

(2012). Dynamic capabilities: Routines versus entrepreneurial action. Journal of Management Studies, 49(8), 1395–1401.

79.

Teece

D. J.

(2014). The foundations of enterprise performance: Dynamic and ordinary capabilities in an (economic) theory of firms. Academy of Management Perspectives, 28(4), 328–352.

80.

Teece

D. J.

, Pisano

, & Shuen

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

81.

Todri

, & Adamopoulos

(2014). Social commerce: An empirical examination of the antecedents and consequences of commerce in social network platforms [Paper presentation]. 35th International Conference on Information Systems, Auckland, 1–18.

82.

Trainor

K. J.

(2012). Relating social media technologies to performance: A capabilities-based perspective. Journal of Personal Selling & Sales Management, 32(3), 317–331.

83.

Wamba

S. F.

, Gunasekaran

, Akter

, & Dubey

(2019). The performance effects of big data analytics and supply chain ambidexterity: The moderating effect of environmental dynamism. International Journal of Production Economics, 222(4), Article e107498.

84.

Wilden

, Gudergan

S. P.

, Nielsen

B. B.

, & Lings

(2013). Dynamic capabilities and performance: Strategy, structure, and environment. Long Range Planning, 46(1/2), 72–96.

85.

Wilhelm

, Schlömer

, & Maurer

(2015). How dynamic capabilities affect the effectiveness and efficiency of operating routines under high and low levels of environmental dynamism. British Journal of Management, 26(2), 327–345.

86.

Winter

S. G.

(2003). Understanding dynamic capabilities. Strategic Management Journal, 24(10), 991–995.

87.

Woodcock

, Green

, & Starkey

(2011). Social CRM as a business strategy. Journal of Database Marketing & Customer Strategy Management, 18(1), 50–64.

88.

L. Y.

(2010). Applicability of the resource-based and dynamic-capability views under environmental volatility. Journal of Business Research, 63(1), 27–31.

89.

Zahra

S. A.

(2006). Entrepreneurship and dynamic capabilities: A review, model and research agenda. Journal of Management Studies, 43(4), 917–955.

The Impact of Dynamic Capability on Business Sustainability of Organizations

Abstract

Keywords

Introduction

Literature Review

Theoretical Background and Hypotheses Development

Theoretical Background

Development of Hypotheses and Conceptual Model

Sensing Capability

Seizing Capability

Reconfiguring Capability and Sustainable Organization

Moderating Effects of Social Customer Relationship Management Ability

The Conceptual Model (Adapted from DCV theory, Teece et al., 1997)

Research Methodology

Research Instruments: Measures and Scales

Data Collection Procedure and Sample Characteristics

Data Analysis and Results

Measurement Properties and Discriminant Validity Test

Moderator Analysis (Multigroup Analysis)

Effect Size f2 Test

Hypotheses Testing

Validated Model (SEM)

Results

Discussion

Effects of SCA on H1

Effects of SCA on H2

Effects of SCA on H3

Theoretical Contributions

Implications to Practice

Limitations and Future Scope

Footnotes

Declaration of Conflicting Interests

Funding

References

Effect Size f² Test

Effects of SCA on H₁

Effects of SCA on H₂

Effects of SCA on H₃