1 Introduction

The emergence of several modern technologies has attracted the attention of different industries throughout the world (Ivanov et al., 2020; Queiroz et al., 2020a). Consequently, research areas covering manufacturing science, industrial engineering, operations, and so on have been affected by the influence of these cutting-edge technologies. A ground-breaking technology like artificial intelligence (AI) is helping to remodel the supply-chain networks, operational management, and production systems of the firms (Queiroz et al., 2020b; Ivanov et al., 2020; Basile et al., 2021; Thrassou et al., 2021). AI can be integrated with other existing technologies in a firm to understand its business pattern (Sahu et al., 2020; Rodríguez-espíndola et al., 2020). Operation as well as production management system has undergone a dramatic paradigm shift owing to the different applications of AI like ML (Machine Learning), DL (Deep Learning), and NLP (Natural Language Processing). With the help of these technologies, it has been possible to continue production-related internal operations of the firms remotely. The firms can be fully operated by using robots. Business creation with the help of AI will be touching $39 trillion by 2022 which was $1.2 trillion in 2018 (Richards et al., 2019; Dhamija & Bag, 2020). Apart from the impacts of AI technology, other ground-breaking technologies like IoT, blockchain, BDA, and quantum computing have affected the production systems and operation management resilience of the firms (Belhadi et al., 2021; Gupta et al., 2022; Kamble et al., 2018). With the help of IoT technology and drones, it has become easy to optimize inventory checking operations (Dolgui et al., 2020; Rimba et al., 2020). These technologies have benefitted to improve the relationships between the transport workers, suppliers, and customers. Chatbots could help to follow up the orders received by a firm automatically (Vrontis et al., 2021; Chaudhuri et al., 2021; Sakka et al., 2021; Duan & Da, 2021). Even in any apocalyptic unforeseen situation, these cutting-edge technologies are deemed to be helpful to overcome the challenges to sustain operational efficiency and flow of production, keeping the demand–supply situation unhindered (Geunes & Su, 2020; Kim et al., 2020; Baabdullah et al., 2021). Since little is known regarding how AI integrated with other curring-edge technologies could improve operational activities as well as production management systems, the present study has taken an attempt to investigate how AI integrated with new edge technologies could improve firm performance mediated through some contextual factors like production and operational system sustainability with the moderating influence of technology turbulence. Also, little is known to address the unpredictable environmental events causing resilience of operational management and production system (Dwivedi et al., 2019; Wamba et al., 2019b; Ivanov et al., 2020; Kar et al., 2021). This study strives to sort out these growing issues by identifying the determinants impacting the production system and operational sustainability by using the DCV theory (dynamic capability view) by Teece et al. (1997) as well as the absorptive capacity theory (Cohen & Levinthal, 1990). Such being the scenario, the present study attempts to answer the below RQs.

  • RQ1: How adoption of artificial intelligence and cutting-edge technologies helps production system sustainability?

  • RQ2: Whether technology turbulence could moderate the relationship between the adoption of cutting-edge technologies and production system sustainability?

The organization of the remaining parts of the paper is arranged as follows. Section 2 presents the background study with theoretical underpinning followed by hypotheses formulation and development of the theoretical model in Section 3. Next, Section 4 presents the design of the research followed by data analysis with results in Section 5. Thereafter Section 6 presents the study synthesis and discussion which included implications of this study and conclusion, limitations with future scope.

2 Background Study with the Theoretical Underpinning

2.1 Applications of Cutting-Edge Technologies

AI is considered the key technology for achieving persuasive operational transformation in the context of contemporary firm setup since AI technology is emerging in different forms (Aloini et al., 2018). AI possesses the ability associated with the philosophy of the machines to behave, think, as well as perform similar tasks to humans (Schmidt & Hazır, 2019; Gunasekaran et al., 2018). It is a fact that the concept of AI was initiated in the earlier days of 1956, but its applications have gained momentum in recent times (Dolgui et al., 2018, 2019; Chatterjee et al., 2021a). Further, expert and agent systems, genetic algorithms, and BDA are deemed to be in proximity to AI (Gupta et al., 2019; Wamba et al., 2019a; Rana et al., 2021; Sequeiros et al., 2021). Taking the help of AI technology, the operational management of a firm can be improved (Wang et al., 2018). Firms are investing a considerable amount in improving their information technology (IT) enabled applications to develop intra-firm and interfirm operational efficiency for eventual improvement of their performances (Chakravarty et al., 2013; Gupta et al., 2021). Studies documented that the application of BDA in daily operational activities of firms has provided fruitful results, especially, for large firms such as Uber, Amazon, and Walmart (Kamble & Gunasekaran, 2020; Schildt, 2017; Vahn, 2014). Moreover, quantum computing technology can produce such outputs that classical computers are not able to produce (Gupta et al., 2022). It is hoped that the optimum computing system will be able to accelerate the tasks performed by the machine learning technology (Biamonte et al., 2017; Chatterjee et al., 2021; Khorana et al., 2021; Preskill, 2018). Again, for tracking indoor and outdoor assets, IoT technology is deemed to be helpful (Choi et al., 2012; Mikalef et al., 2021; Wang et al., 2010). IoT technology helps with information sharing in a firm that can influence the production and operational system of the firm (Yan et al., 2016). Another cutting-edge technology like blockchain derives benefits to the production management system and helps in the operational sustainability of a firm (George et al., 2019). This technology is deemed helpful to drive most values to the businesses since it can effectively solve problems which help in the maintenance of consistency of records, authentication of user identity, maintaining auditable information trails, and so on (Centobelli et al., 2021; Chatterjee et al., 2021b). These efficiencies are helping to impact the firms’ production efficiency and operational sustainability (Croom et al., 2018). Also, to control and for reduction of social and environmental negative impacts, the manufacturing firms are giving much emphasis the operational sustainability issues (Haapala et al., 2013; Mohanty & Prakash, 2017; Wamba et al., 2019a). Operational performance comprising cost, quality, delivery, and flexibility is impacted by the sustainability performance provided the firms can overcome the impediments caused due to technological turbulence (Wiengarten & Longoni, 2015; Santos et al., 2021). The extant literature has nurtured that several cutting-edge technologies like social media platforms, big data, AI, blockchain, cloud computing, and IoT could impact the firm performance even after the influence of technology turbulence (Centobelli et al., 2021; George et al., 2019; Wamba et al., 2019a). But how all these technologies in an integrated manner could impact the firm performance under the moderating influence of technology turbulence remained underexplored.

2.2 Contextualization of Theory

For the identification of different factors impacting production system sustainability, the present research has used the DCV theory by Teece et al. (1997) and the absorptive capacity theory by Cohen and Levinthal (1990). For fulfillment of the objectives of a firm, the firm needs to possess some specific activities which are considered valued attributes of the firm (Schreyögg & KlieschEberl, 2007). The market environment is changing globally and for addressing such changing business situation, the firm should not depend on its static (existing) abilities only but should depend on its dynamic capabilities (DC) as well. DC is explained as the firm’s “ability to integrate, build and reconfigure internal and external resources-competencies to address and possibly shape rapidly changing business environments” (Teece, 2012, page-1395). Dynamic capability comprises multiple capabilities which are seizing, sensing, and transforming or reconfiguring capabilities (Teece, 2014). All these dynamic capabilities help a firm to be able to trap suitable business opportunities to address dynamic market environments. Thus, by applying to sense, seizing, and transforming abilities, a firm should ensure maintain production system sustainability. In this perspective, it is argued that a firm must have the capacity to utilize the abilities of cutting-edge technologies like AI, IoT, Blockchain, quantum computing, big data analytics and so on which assist the firms in sensing, seizing, as well as transforming the opportunities for addressing dynamic market environment impacting production system sustainability (Kamble et al, 2021). To use these above-mentioned technologies, the firms need to recognize the opportunities that are essential to fulfill the goal and objectives of the firms. Then the firms need to assimilate and understand the knowledge so obtained from such opportunities and such accumulated knowledge is required to be applied by properly reconfiguring it. Hence, the firms must have dynamic capabilities as well as abilities to recognize, assimilate and use the available opportunities. This concept corroborates absorptive capacity theory. These abilities are construed to overcome the technological turbulence which might impede the process and procedures of the firms to utilize these modern technologies for enhancing the existing firm capabilities and improving their performance.

3 Hypotheses Formulation and Development of a Theoretical Model

The background studies with the theories could help for identifying the factors which can eventually influence the firm performance. These are explained here along with an explanation of the moderating effects of the moderator technology turbulence. Also, attempts have been made to develop a few hypotheses which help for developing a conceptual model.

3.1 AI and Cutting-Edge Technologies

AI includes machines that have the capability to work like human beings (Mishra et al., 2019). Different studies have explained the usefulness of AI (Nilsson, 2014). AI is interpreted as machines that possess intellectual capabilities like humans (Mc Gettigan, 2016; Kamble et al., 2020). Many firms are using AI technology. Airbus is known for using AI technology for examining its production problems and calculating data to arrive at a solution (Ransbotham et al., 2017). KPMG (Australia) is automating its auditing services, Bridgewater associates are engaged to automate their business operations activities taking the help of AI (Tredinnick, 2017). Firms which are specialized in finance, telecommunication, and marketing are using AI technology to become more competitive (Oana et al., 2017). The application of AI technology could be regarded as a dynamic capability of a firm that is perceived to impact the production system sustainability of the firm corroborating with DCV (Kamble et al., 2020). Accordingly, the following hypothesis is articulated.

  • H1a: Adoption of AI-based applications (AAI) positively impacts the production system sustainability (PSS) of a firm.

Big data analytics (BDA) technology and related applications have occupied the frontline of operations and production management and information system (IS management) (Wamba et al., 2019a; Kamble & Gunasekaran, 2020). BDA is explained as “a holistic process that invites the collection, analysis, use, and interpretation of data for various functional divisions with a view to gaining actionable insights, creating business value, and establishing competitive advantage” (Akter et al., 2016, p.178; Kamble et al., 2018). BDA is used in business activities and for the purposes of analysis of data (Wamba & Akter, 2019). BDA can explore the ways for extracting valuable information derived from several sources of data which help a firm for gaining competitiveness (Akter et al., 2016; Shams & Solima, 2019). BDA possesses four dimensions which are velocity, volume, accessibility, as well as variety (Morabito, 2015). Studies have demonstrated that BDA capability has a significant association with firm performance and production system sustainability (Aydiner et al., 2019; Kamble & Gunasekaran, 2020). Accordingly, the following hypothesis is developed.

  • H1b: Adoption of big data analytics (BDA) positively impacts the production system sustainability (PSS) of a firm.

The adoption of quantum computing (AQC) takes the help of quantum computing technology. This technology can produce such outputs which classical computers cannot provide effectively (Al‐Rabadi, 2009). This modern cutting-edge technology possesses algorithms that can accelerate the tasks done by machine learning (Biamonte et al., 2017; Preskill, 2018). If information is required by a firm from the huge volume of collected information (data), and if such particular data cannot be searched by ordinary technology, the technological ability of quantum computing of the firm may help to search that information quickly and accurately (Oxford Analytica, 2018). This dynamic ability of the firm is perceived to impact the sustainability of the production system even in a dynamic market environment that is in line with DCV (Kamble et al., 2020). As such, the hypothesis below is provided.

  • H1c: Adoption of quantum computing (AQC) has a positive impact on the production system sustainability (PSS) of a firm.

Adoption of IoT technology is perceived to be necessary for both indoor asset tracking and outdoor asset tracking (Choi et al., 2012; Wang et al., 2010). Studies demonstrate applications of IoT technology help a firm to optimize its floor operation, can improve production sustainability along with product logistic operation and can help the firm to effectively recognize and assimilate any external congenial opportunity which is in consonance with absorptive capacity (Kamble et al., 2020; Zhong et al., 2013). The IoT technology is comprised of electronic product code (EPC) as well as an EPC network which can provide a scalable information system for different applications like information sharing to impact the production system sustainability (Thiesse et al., 2009; Yan et al., 2016). Such prolific capability of IoT technology is perceived to have impacted the sustainability of the production system of a firm (Kamble et al., 2020). Accordingly, the following hypothesis is proposed.

  • H1d: Adoption of IoT technology (AIT) positively impacts the production system sustainability (PSS) of a firm.

Adoption of another cutting-edge technology like blockchain has a positive contribution towards firm performance (Kamble et al., 2021). Blockchain is a digital ledger that presents past transactions, distributed in several systems which are called the node, and is operated through various users (George et al., 2019). This allows different participants to introduce records supported by immutable and validated cryptographic protection (Oh & Shong, 2017). With the help of advanced cryptography, blockchain functions as a distributed open-service database (Kirkland & Tapscott, 2016). Applications using blockchain technology cannot be hacked and as such, it is a trusted platform (Orcutt, 2019). Hence, the use of blockchain technology which is considered a dynamic ability to address any dynamic market environment is perceived to impact the production system sustainability of a firm that complements DCV (Kamble et al., 2021). It is thus hypothesized as below.

  • H1e: Adoption of blockchain technology (ABT) positively impacts the production system sustainability (PSS) of a firm.

3.2 Production System Sustainability (PSS)

A sustainable production system is concerned with the creation of goods and services with the help of processes and procedures which are non-polluting and which do not waste natural resources. The processes are needed to be safe, economically viable, and healthful for employees and consumers, as well as for the community (Lozada-Contreras et al., 2021). Production systems should be sustainable so that resources and energy are used efficiently used and sustainable products are produced. Focus is given to recycling of products and usage of renewable energy which can be used for production in the firms (Havenvid et al., 2016). A sustainable production system covers four areas like social sustainability, human sustainability, economic sustainability, and environmental sustainability (Jassem et al., 2021). The firms must possess the ability to address the future needs of the consumers also after meeting the needs of the present (Geng et al., 2021; Kamble et al., 2020). If the firms fulfill these sustainability-related conditions, it is perceived that it will help to develop sustainable operations which would eventually ameliorate the firm’s performance. Accordingly, hypotheses are developed below.

  • H2a: Production system sustainability (PSS) positively impacts the operational sustainability (OPS) of a firm.

  • H2b: Production system sustainability (PSS) positively impacts firm performance (FIP).

3.3 Operational Sustainability (OPS) and Firm Performance (FIP)

Operations that can meet the present requirements while keeping the option to meet the future necessities are known as operational sustainability (Longoni et al., 2014). It can also be interpreted as the maintenance of the existing practices without endangering future resources (Kleindorfer et al., 2005). To achieve the operational sustainability, a firm needs to articulate sustainable action plans for production, take congenial actions to avoid wastage of energy, to practice sustainable operational systems which are perceived to impact market share, revenue, cash flow, profitability, and value-added productivity of the firm (Gimenez et al., 2012). Accordingly, the following hypothesis is formulated.

3.4 Bullet H3: Operational Sustainability (OPS) Positively Influences the Performance of a Firm (FIP).

3.4.1 Moderating Effects of Technology Turbulence (TT)

When a relationship between two variables is not fixed, a third variable impacting that relationship may strengthen the relationship or may weaken the relationship, or even in some cases it could reverse the direction of the relationship. This third variable impacting such a relationship is interpreted as 'moa derating variable'. Technology turbulence (TT) is defined as “the rate of change of product and process technologies used to transform inputs into outputs” (Ngamkroeckjoti & Speece, 2008, p.413). Again, it has been opined that technology turbulence “is caused by changes in, and interaction between, the various environmental factors especially because of advances in technology and the confluence of computer, telecommunication, and media industries” (Mason, 2007, p.11). Studies have demonstrated that TT can influence the relationship on of market orientation and its performance (Appiah-Adu, 1997). TT emerges when there occurs a quick change in technology and in such a case; the adoption of new technology becomes problematic due to rapid change in technology and is impeded by the users (Ngamkroeckjoti & Speece, 2008). Accordingly, the following hypotheses are formulated.

  • H4a: The moderator TT (technology turbulence) influences the relationship AAI → PSS.

  • H4b: The moderator TT (technology turbulence) influences the relationship between BDA → PSS.

  • H4c: The moderator TT (technology turbulence) influences the relationship AQC → PSS.

  • H4d: The moderator TT (technology turbulence) influences the relationship between AIT → PSS.

  • H4e: The moderator TT (technology turbulence) influences the relationship ABT → PSS.

These discussions help to develop a theoretical model which is shown in Fig. 1.

Fig. 1
figure 1

The theoretical model (using DCV theory and absorptive capacity theory)

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4 Design of Research

Hypotheses have been tested and validated with taking help of the PLS-SEM approach. This approach is simpler and can synthesize any study which is exploratory in nature (Peng & Lai, 2012; Vinzi et al., 2010). This approach permits for allowing such data which are not even normally distributed. It is not allowed when analyzing CB (covariance-based) SEM (Rigdon et al., 2017; Kock & Hadaya, 2018). This process involves a survey where the responses of the participants are quantified by a recognized scale. Here a 5-point Likert scale is used. A 5-point Likert scale has been used because this scale is simple to use and provides the respondents an opportunity to opt for a neutral stand by providing the option 'neither disagree nor agree'.

4.1 Development of the Questionnaire

Extent literature and theories help to prepare the questionnaire which has been articulated in the statement form. Questions have been pre-tested taking help of a comparatively small number of participants. The pre-test result helps to improve the formats and readability so that the participants do not feel any problem responding. After the pre-test, a pilot test has been performed to ascertain the understandability of the questions and to remove the complexities the questions. The pilot test also helped to estimate the reliability of the items and the results also help to drop some items which do not completely explain the corresponding constructs. Finally, the opinion of some experts possessing adequate knowledge and expertise in the present study has been taken to enhance the comprehensiveness of items as well as to fine-tune them. In this way, twenty-seven questions could be prepared. The summary of the questionnaire is shown in Appendix Table 7.

4.2 Data Collection

This study aims to investigate the impact of cutting-edge technologies on the betterment of the firm’s performance. Hence, data need to be obtained from respondents who have at least a preliminary concept regarding the domain of the present study. Hence, the purposive sampling technique (Apostolopoulos & Liargovas, 2016) is utilized. In this method, researchers principally depend on one’s judgment to target the respondents. A purposive and convenient sampling technique was used in the study (Garg, 2019). In this context, the co-authors based in India attended conferences and seminars on the subject matter of this study. A list of potential respondents was prepared using the inputs from the conference participants and their professional networks. In this way, details of 903 prospective respondents could be developed. All the participants received the response sheets each containing twenty-seven questions. A guideline was provided to each of the respondents highlighting how to fill it up. Each of them was assured that their identity would not be disclosed. Two months’ time (April – May 2021) was given to them for responding. Within time, 426 replies were received, the rate of the response being 47.27%. All the activities pertaining to the data collection and required follow-ups with the respondents were performed by the co-authors based in India. For conducting the non-response bias test, recommendations provided by Armstrong and Overton (1977) have been followed. For this, an independent t-test and chi-square test with the inputs of the first and last 100 respondents have been conducted. No mentionable deviation of results in these two cases was noted confirming that non-response bias could not pose a major concern in this study. After scrutiny, 14 responses were found vague. These were not considered. Analysis has been done with the inputs of the 412 respondents. The details of the demographic information are given in Table 1.

Table 1 Information about the participants
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5 Data Analysis with Results

5.1 Measurement and Test of Discriminant Validity

Convergent validity of the items has been estimated by computation of loading factor (LF) for each of the items. To assess the validity, reliability, as well as internal consistency of each of the constructs, AVE (average variance extracted), CR (composite reliability), and α (Cronbach’s alpha) have been computed. The values are within the allowable range. Table 2 presents the results.

Table 2 Properties for measurement
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Square roots of the values of average variance extracted have been assessed. All the values of the square roots are greater than the respective correlation coefficients (bifactor) satisfying the criteria of Fornell and Larcker (Fornell & Larcker, 1981). This helps to confirm the discriminant validity. Table 3 reflects the results.

Table 3 Test for discriminant validity
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5.2 Moderator Analysis (Multi-Group Analysis)

The present study has considered the effects of technology turbulence (TT) as a moderator impacting the linkages H1a to H1e. For this, MGA (multi-group analysis) was conducted under the procedure of bootstrapping with consideration of 5000 resamples. The difference of p-values towards effects concerning Strong TT and Weak TT on these five linkages is found to be all less than 0.05 confirming that the TT has significant moderating impacts (Hair et al., 2016) on the five linkages. Table 4 presents the results.

Table 4 Moderator analysis (MGA)
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5.3 Common Method Variance (CMV)

Since the data has been obtained from the survey, there is a chance for CMV. To eliminate this defect, some procedural measures have been taken. During the collection of data, all the prospective respondents were assured that their anonymity and confidentiality could be preserved. Also, at the time of preparation of the questionnaire, the recital of the questions was made simpler through the pre-test and pilot test. Even after that to ascertain the severity of CMV, a post hoc Harman’s Single Factor Test (SFT) has been conducted. The result highlighted that the first factor came out to be 20.62% of the variance which is less than the recommended value of 50% (Podsakoff et al., 2003). Since it is noted that Harman’s SFT is not a robust test for CMV, a marker correlation test has been conducted (Ketokivi & Schroeder, 2004; Lindell & Whitney, 2001). The results indicated that the differences between CMV and marker adjusted CMV were very small (≤ 0.06) (Mishra et al., 2018a, b). Hence, it is inferred that CMV could not distort the data.

5.4 Causality Test

Causality is considered an important aspect that needs to be addressed before hypotheses testing (Guide & Ketokivi, 2015). As suggested by Kock (2015), non-linear causality direction ratio (NLBCDR) values for all the linkages have been estimated. For AAI → PSS (1.002), for BDA → PSS (0.999), for AQC → PSS (1.000), for ATT → PSS (1.004), for ABT → PSS (1.003), for PSS → OPS (0.996), for PSS → FIP (0.998), and for OPS → FIP (1.005). All these estimated values are greater than 0.7 (Wamba et al., 2019a). These values highlight strong evidence that the support in favor of the revised hypothesized direction towards causality is weak. Thus, causality was not a major issue.

5.5 Hypotheses Testing

To test the hypotheses by SEM, bootstrapping procedure considering 5000 resamples has been adopted. Separation distance 7 was considered. Cross-validated redundancy has been determined for which Q2 has been estimated and its value has become 0.032 (positive) confirming the predictive relevance of the model (Mishra et al., 2018a, b). For verifying if the model is in order, standardized root means square residual (SRMR) was estimated considering it as a standard index. The values of SRMR emerged as 0.032 and 0.061 respectively for PLSc as well as for PLS. These two values are not greater than 0.08 (Hu & Bentler, 1999). As such it is inferred that the model is fit. This approach has helped for estimating the β-values and other parameters. The details are presented in Table 5.

Table 5 Computation of path coefficients and significance level (SEM)
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The model is now validated. It is provided in Fig. 2.

Fig. 2
figure 2

The model after validation (SEM)

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5.6 Mediation Analysis

Mediation effects have been analyzed for OPS within the PSS → OPS → FIP link using the approach recommended by another study (Preacher & Hayes, 2008). The bootstrapping procedure has been taken concerning the indirect effect of confidence interval (CI) to the tune of 95%. The value of the indirect effect of PSS on FIP mediating through OPS can be computed by multiplying the β-values of the relationships between PSS → OPS and OPS → FIP. The value becomes 0.42 × 0.46 = 0.19 (***p < 0.001). Also, the direct effect of PSS → OPS and OPS → FIP are found to be significant at ***p < 0.001. Hence, the results show that OPS can be construed to be the significant partial mediator between PSS and FIP. Table 6 presents the results.

Table 6 Mediation analysis and results
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5.7 Results

In this study, 13 hypotheses have been formulated. Out of these 13 hypotheses, 5 hypotheses are concerned with the effects of the moderator TT towards 5 linkages. This study highlights that the impacts of AAI, BDA, AQC, AIT and ABT on PSS (H1a to H1e) are significant as well as positive because the corresponding β-values are respectively 0.29, 0.31, 0.26, 0.28 and 0.22. The corresponding significance levels are **p < 0.01, **p < 0.01, ***p < 0.001, *p < 0.05, **p < 0.01. The impacts of PSS on OPS (H2a) and the impacts of PSS on FIP (H2b) are both significant and positive since the corresponding path coefficients have values like 0.42 and 0.37 respectively. The corresponding significance levels are both ***p < 0.001. FIP is impacted by OPS (H3) significantly and positively because the β-value is 0.46 with a significance level ***p < 0.001. The moderator TT has significant impacts on the five linkages and the impacts are also positive because the corresponding β-values are 0.14 (*p < 0.05), 0.16 (*p < 0.05), 0.19 (**p < 0.01), 0.24 (**p < 0.01), as well as 0.34 (**p < 0.01). In terms of R2 values which are known as the coefficient of determination, the results show that exogenous variables AAI, BDA, AQC, AIT, and ABT can explain PSS amounting to 46% (R2 = 0.46) and PSS can interpret OPS to the extent of 52% (R2 = 0.52) and PSS, as well as OPS, can simultaneously interpret FIP by 79% (R2 = 0.79). This is the predictive power of the proposed theoretical model.

6 Study Synthesis and Discussion

This study has made an attempt to demonstrate how the emergence of different cutting-edge technologies could influence the business pattern and eventually business growth of the firms. The present study has dealt with how AI, IoT, blockchain, big data analytics, and quantum computing could impact the production system sustainability which could eventually influence the performance of the firm mediating through operational performance sustainability. The present study has shown that the adoption of these cutting-edge technologies could impact production system sustainability (H1a to H1e) which has been supplemented by another study (Ivanov et al., 2020) that investigated how applications of industry 4.0 could impact the operational management of the firms. The present study has investigated how production system sustainability could impact operational sustainability (H2a) as well as firm performance (H2b). This idea is supported by other literature (Wamba et al., 2020). It investigated that BDA and other technologies could impact the performance of the firms which is influenced by the moderator ED. This present research has highlighted that operational sustainability could impact the performance of the firm (H3). This idea corroborates another study (Aydiner et al., 2019). The moderator TT has significant effects on all the linkages covered by H1a to H1e. This has been confirmed through multi-group analysis. Now the impacts of TT on the five relationships (H1a to H1e) are discussed through graphical representation presented by five graphs simultaneously marked by Fig. 3.

Fig. 3
figure 3

Impacts of the moderator TT on five linkages

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Here, through Fig. 3, the impacts of Strong TT, as well as Weak TT, have been shown on the relationship between H1a to H1e. For all the five graphs, the continuous and dotted lines represent the impacts of the Strong as well as Weak TT towards these five relations, respectively. It is observed that with an increase of AAI (for H1a), BDA (for H1b), AQC (for H1c), AIT (for H1d), and ABT (for H1e), the rate of increase of PSS for all the relationships is greater for the impacts of the Strong TT in comparison to impacts of the Weak TT since the dotted lines have gradients less than the gradients of the continuous lines. It is pertinent to mention here that the gradient of a straight line is the trigonometrical tangent of the angle that the straight line makes with the positive direction of the horizontal axis.

6.1 Contributions to the Theory

There are multiple theoretical contributions to this study. No studies could investigate how the advantages of cutting-edge technologies like AI, BDA, IoT, AQC, and ABT could impact the production system sustainability of a firm impacting the firm performance under the moderating influence of technology turbulence (TT). This research has successfully investigated all these issues and has been able to provide a suitable theoretical model possessing respectable predictive power. The instant research has utilized two theories absorptive capacity theory and DCV. The dynamic capability view theory conceptualized that a firm must possess DCs which are seizing, sensing, and transforming abilities (Teece, 2014) to trap external and internal opportunities to mobilize them and eventually use them to address the dynamic market environments. The present research has shown that the concept of DCV has been extended by arguing that the capabilities of these cutting-edge technologies are dynamic capabilities that could impact the firm performance eventually helping the firms to address any high-velocity market situation. The present research has extended the concept of absorptive capacity theory by arguing that for utilizing cutting-edge technologies, the firms need to recognize their importance. The firms need to learn how to use these cutting-edge technologies to harness the best results through the process of assimilation. Eventually, the firms need to apply those technologies in a congenial situation so that such use could help the firms to exhibit the best performance. The present study is principally concerned with the adoption of AI and cutting-edge technologies for production system sustainability. Thus, for the interpretation, the study could be dependent on the theoretical lens concerned with a standard adoption theory. But that has not been done. Instead, better-suited contextual factors have been chosen and as a result, the proposed theoretical model could achieve high explanative power. In a study by Rodríguez-espíndola et al. (2020), it is observed that the study has investigated how applications of AI, blockchain, and 3D printing could improve the humanitarian supply chain by ensuring better efficiency. This concept has been expanded in the present research to project how the different disruptive technologies could influence the production patterns and operations of the firms to help improve their performance. This has enriched extant literature. Besides, consideration of the impacts of the moderator technology turbulence (TT) on the relationships between product system sustainability (PSS) with its five predictors has enriched the conceptual model.

6.2 Implications for Practice

This research study has shown several important practical aspects. The study demonstrates that big data analytics is helpful to improve the production system. In such a situation, the managers need to adopt a data-driven view when approaching improving the production system. The managers need to arrange to make the employees aware of the advantages of adopting the data-driven culture and the utility of big data analytics capability. The present study has demonstrated that the adoption of AI technology would improve the production system sustainability of a firm. In this context, the managers are needed first to motivate the employees for making them realize the prolific opportunities the AI technology can provide. The employees should be made to realize that usage of AI in the daily routine works will reduce the human load and the pressure of works will be reduced by the automated power of AI. This will enhance the motivation of the employees toward these cutting-edge technologies like blockchain, IoT, quantum computing, and so on. To accomplish this, the managers of the firms need to arrange periodical workshops to keep the employees of the firms appraised about the utility and success of those cutting-edge technologies if those technologies are properly adopted and effectively used in the firms. For this, the managers of the firms are required to arrange for imparting appropriate training to the employees. The motivation of the employees with proper training will help a firm to extract its best potential from these cutting-edge technologies. It is a fact that in this era of rapid advancement of technology, the employees will have inconvenience using the technologies to their full potential. But regular periodical training and readiness for the employees are expected to mitigate the menace of technology turbulence. Before attempting to adopt these cutting-edge technologies, the top management of the firms should improve the dynamic capabilities of the firms. Before investing in adopting such diverse types of disruptive technologies, the top executives of the firms are needed to carefully evaluate the expertise of the employees to sense the dynamic changes in the market environment which may help them to shape the opportunities and to mitigate the risks. They are to assess if the firms’ infrastructure could seize such opportunities and if the existing staff can reconfigure such trapped opportunities to gain a competitive advantage.

6.3 Conclusion, Limitations, and Future Scope of the Study

This research has reached a finding on such data which are cross-sectional. It creates defects of causality in the relationships between the variables. This gives rise to the defects of endogeneity. To eliminate the defects, future researchers need to undertake a longitudinal study. The present study has utilized the dynamic capability view theory. But DCV is associated with context-insensitivity defects (Ling-Yee, 2007). DCV fails to detect such conditions in which firms’ capability (Dubey et al., 2019) will be valuable. In this perspective, future studies should explore such conditions in which operational sustainability could provide maximum inputs to improve firm performance. Depending on the input of 412 participants of production and manufacturing firms based out of India, the present research has reached a finding. Hence, such results suffer from the external validity issue. Future researchers need to arrange to obtain input from respondents dispersed across the world. Then the results will have generalizability. The model possesses predictive power to the extent of 79%. In such a situation, future researchers need to consider the inclusion of relevant boundary conditions along with other constructs for verifying if such consideration could enhance the strength of the proposed theoretical model. The present study has demonstrated that applications of AI, BDA, quantum computing, IoT, and blockchain technologies could improve the firm’s performance. But there are other cutting-edge technologies such as augmented intelligence, warehouse robotics, and so on. Future researchers may consider the effects of applications of these technologies in the firms to examine how the firm performance could be impacted. Besides, the present study did not analyze the firm cultural issues as a moderator. It is suggested that future researchers should explore the cultural issue to investigate how such consideration could influence the performance of the firms.