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Article

Navigating Strategic Balance: CEO Big Data Orientation, Environmental Investment, and Technological Innovation in Chinese Manufacturing

by
Weiwei Wu
and
Xu Wang
*
School of Management, Harbin Institute of Technology, 92 Xidazhi Street, Nangang District, Harbin City 150001, China
*
Author to whom correspondence should be addressed.
Systems 2024, 12(7), 255; https://doi.org/10.3390/systems12070255
Submission received: 30 May 2024 / Revised: 30 June 2024 / Accepted: 12 July 2024 / Published: 15 July 2024
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Abstract

:
In the digital age, China’s economic development is transitioning from high speed to high quality. Through the application of digital technology, China’s manufacturing industry is moving toward more environmentally friendly and sustainable innovation, which makes it of great significance to study the effect of green investment and big data on innovation. Grounded in strategic management theory, this paper examines the interplay between CEO big data orientation, environmental investment, and their joint impact on technological innovation in manufacturing enterprises. Data are extracted from annual reports of listed Chinese manufacturing companies using computer-assisted text analysis methods and evaluated with negative binomial regression. The results indicate an inverted U-shaped relationship between CEO big data orientation and technological innovation. The results further explain that as CEO big data orientation increases, enterprises with higher levels of green investment will reach the peak of technological innovation performance earlier. According to China’s intelligent manufacturing in 2035, we have proposed some methods and suggestions for green investment and big data applications.

1. Introduction

In today’s era, continuous innovation constitutes a vital competitive advantage [1,2,3,4], requiring big data resources and applications [5,6], particularly in manufacturing enterprises facing digital transformation [7]. Chief Executive Officers (CEOs) play a pivotal role in shaping organizational strategies and priorities, including the adoption and utilization of big data technologies and resources. The emergence of big data technologies, particularly in the form of CEO big data orientation, has introduced new dynamics to organizational decision making and strategic direction [8]. From an attention-based view [9], CEO big data orientation refers to the degree to which CEOs prioritize and advocate the integration of big data technology and resources into organizational process construction and strategic decision making. In the manufacturing industry, the convergence of digitalization and innovation has significantly altered the strategic landscape, posing intricate challenges for organizational leadership. To handle these complexities, the CEO’s task is to coordinate strategic initiatives for data-driven decision making and technological advancement. In this context, understanding the relationship between CEO big data orientation and technological innovation is of significant interest to scholars and practitioners. However, currently, CEO big data orientation and attention allocation are not fully considered in the interpretation of technological innovation from a strategic perspective. Therefore, it is necessary and important to explore this topic.
The embedding of the resource-based view is the fundamental principle of aligning organizational resources and capabilities with strategic objectives to achieve sustainable competitive advantage [10,11]. From this perspective, the CEO big data orientation reflects the organization’s commitment to utilizing big data analysis to improve operational efficiency, customer insight, and market positioning, and then drive innovation. Meanwhile, the manufacturing industry is an industry with strong environmental requirements and regulatory regulations. Therefore, strategic balance between data-driven decision making and innovation pursuit requires a keen consideration of broader organizational aspirations, including sustainability requirements, as well as their applications in the manufacturing context. Strategic balance emphasizes the necessity of skillfully managing the trade-offs and synergies between competing strategic priorities [12], requiring promoting innovation while minimizing environmental pollution, and promoting innovation. Green investment is a microcosm of strategic decision making aimed at incorporating environmental sustainability into organizational operations and value-creation structures [13]. From the perspective of strategic management, green investment aligns with the overall goals of corporate social responsibility and stakeholder engagement, thereby enhancing an organization’s reputation and long-term survival [14,15]. However, the strategic challenge lies in optimizing resource allocation for sustainable development initiatives while ensuring alignment with data-driven innovation needs. It also involves achieving the optimal balance between efficiency gains and sustainable goals, while cultivating an atmosphere of innovation [16]. Therefore, it is necessary to introduce environmental investment into the study of the impact of CEO big data orientation on technological innovation in the manufacturing industry. In this paper, we will explore the combined effect of environmental investment and CEO big data orientation on technological innovation through moderating effect analysis, which, at present, few scholars have attempted to do.
Therefore, this paper attempts to delve into the complex relationship between CEO big data orientation, green investment, and manufacturing technology innovation. Specifically, this paper attempts to uncover the strategic impacts of these interrelated dynamics, revealing how strategic choices around data utilization and green investment affect innovation outcomes in manufacturing enterprises. Based on the rich theoretical foundation of a resource-based view, the research aims to provide insight for organizational leaders who strive to address the multifaceted challenges and opportunities of the digital age, while promoting environmental sustainability and promoting sustainable growth in the manufacturing industry.
This study aims to contribute to the study of the relationship between CEO big data orientation, environmental investment, and technological innovation from three key aspects. Firstly, from a strategic perspective, this paper introduces CEO big data orientation as a novel factor influencing technological innovation, thus extending the existing literature on innovation studies. Secondly, this paper delves into the non-linear, inverted U-shaped relationship between CEO big data orientation and technological innovation, diverging from previous studies that predominantly focused on linear relationships. Our research considers both the benefits and cost effects (resource constraints) of CEO big data orientation, thereby offering a more comprehensive understanding of its impact on technological innovation within enterprises. Thirdly, this paper makes a certain contribution to the literature on strategic balance by examining the joint effect of environmental factors and CEO orientation on technological innovation. Specifically, we investigate the moderating effect of environmental investment on the relationship between CEO big data orientation and technological innovation, thereby contributing to the boundary of strategic innovation theory.

2. Theory and Hypotheses

2.1. CEO Big Data Orientation and Technological Innovation

2.1.1. Benefit of CEO Big Data Orientation

In today’s rapidly changing business environment, the strategic adoption of big data analytics has emerged as a pivotal driver of organizational success. In the manufacturing sector, operational efficiency, product quality, and innovation are critical for sustained competitiveness. The CEO plays a central role in shaping the strategic direction of their organization and influences the technological trajectory to ensure the manufacturing company remains competitive and innovative. Attention is a dynamic cognitive process, capable of adapting to various stimuli from both external and internal environments [17,18,19]. It enables individuals to selectively process information within their focus while potentially suppressing irrelevant stimuli [9,20]. The CEO’s attention allocation plays a crucial role in shaping strategic priorities, guiding resource allocation, and providing information for the decision-making process. In this paper, CEO big data orientation refers to the degree to which CEOs prioritize and advocate the integration of big data technology and resources into organizational process construction and strategic decision making.
Firstly, it is necessary to recognize the strategic importance of big data analysis in driving organizational development and maintaining competitive advantage. In an era filled with big data from various sources, such as IoT sensors, machine-generated data, social media, and customer interactions, enterprises have access to rich information. Those that effectively utilize this information can gather valuable insight into market trends, customer preferences, and operational efficiency [21,22,23,24], and then support innovation [25,26]. CEOs with a firm direction toward big data understand the transformative potential of big data analytics in providing actionable insight, thereby driving strategic decision making and innovation throughout the organization.
Moreover, CEOs are guided by big data and use data-driven insights to predict customer demand and explore emerging innovative avenues, providing organizations with the flexibility and responsiveness needed to navigate volatile markets [21,23]. With the trend of digital transformation in the manufacturing industry, CEO big data orientation provides better guidance on the readiness of intelligent predictive informatics tools to manage big data in order to achieve transparency and productivity [27]. By vigilantly monitoring key performance metrics and market dynamics, CEOs can adjust their innovation strategies in real-time. This enables them to capitalize on emergent opportunities and forestall potential risks. As a result, the organization can consolidate its position as a leader in innovation in the digital era.
Additionally, CEO big data orientation is reflected in the strategic commitment to utilizing data analysis to mine opportunities and drive organizational change. By guiding investments toward enhancing data analysis capabilities, strengthening infrastructure frameworks, and cultivating talent pools, CEOs communicate the primary importance of data-driven decision making and innovation to their organizations, allowing talent to better utilize these technological tools to uncover hidden structures and gaps between existing knowledge and innovation [28]. This requires cultivating a culture that values experimentation, continuous learning, and flexible adaptation to constantly changing market dynamics. Meanwhile, CEOs are guided by big data and provide employees with the necessary tools, resources, and incentives to enter unknown technological fields, thereby cultivating an innovative culture. By creating an environment that values originality, promotes collaboration, and supports adventure, CEOs inspire innovation efforts and drive organizational progress [29,30,31,32]. In such an ecosystem, employees have the courage to explore new concepts, accept risks, and use data-driven insights to drive innovation in all aspects of organizational operations.
In conclusion, CEO big data orientation is a powerful catalyst for fostering technological innovation in manufacturing enterprises. By leveraging the potential of data analytics, cultivating a culture of innovation, and fostering organizational learning, CEOs can steer their organizations toward sustained innovation and competitive advantage in the contemporary business landscape. From a strategic perspective, this hypothesis elucidates the key role of CEO big data orientation in shaping innovation trajectories in manufacturing enterprises. However, such benefits from CEO big data orientation might wear out and hit a “plateau”. During a certain period of time, the innovation opportunities provided by existing resources within the organization are limited. As CEO big data orientation increases, the unmined value of existing resources decreases, and then mining gradually slows down. The promotion of CEO big data orientation will gradually approach the peak and will no longer significantly improve innovation. Therefore, we propose that the benefits derived from CEO big data orientation eventually stabilize, following a concave shape.

2.1.2. Resource Constraints of CEO Big Data Orientation

From a strategic perspective, the pervasive adoption of CEO big data orientation has reshaped how organizations make decisions, presenting unparalleled opportunities for insight generation and strategic planning. However, management’s attention is a limited resource [9,20,33], and the decision maker tends to focus on issues that are more valuable or legitimate [34,35,36]. Because of their limited attention, the CEO will focus on one focus, which will inevitably dilute the attention allocation of another focus. Therefore, CEO big data orientation may result in excessive attention and resources being directed toward the development and utilization of big data analytics capabilities. This can lead to a reduction in the investment of other innovative resources. Consequently, the rational allocation of attention is compromised, negatively impacting technological innovation.
This means that when the CEO focuses on big data, the attention needs of other resources will be squeezed. These resources include important resources that determine innovation, such as R&D investment [37,38,39], collaboration [40,41], professionals [42,43], employee welfare [44], and slack [45]. Successful innovation requires integrating technological and economic aspects in a way that aligns with organizational capabilities and meets market demands. This implies close cooperation and coordination among various activities in the marketing, research and development, and production functional departments [46]. The reduction of the CEO’s attention on these resources will inevitably affect their utilization in innovation. Because the utilization of other innovation resources is reduced and insufficient, the innovation performance of enterprises will be negatively affected. As CEO big data orientation increases, other resources increasingly constrain innovation.
Moreover, it is imperative to acknowledge the potential pitfalls of an overly data-centric approach to decision making from a strategic standpoint. While big data analytics can provide valuable insight and inform strategic decision-making processes, an overemphasis on data may lead to strategic tunnel vision and a lack of strategic agility within the organization [47,48,49]. CEOs with a strong orientation toward big data may prioritize data-driven metrics and key performance indicators over more qualitative measures of innovation, such as strategic foresight, market intuition, and adaptive capability. This rigid adherence to data-driven directives may constrain the organization’s strategic flexibility and limit its ability to capitalize on emerging opportunities or navigate uncertain environments. Additionally, CEO big data orientation may inadvertently foster a strategic culture of risk aversion and resistance to strategic change within the organization. Employees may perceive data-driven directives as rigid mandates that leave little room for strategic experimentation or deviation from established strategic paradigms. This risk aversion can stifle strategic innovation and impede the organization’s ability to adapt its strategic posture in response to shifting market dynamics or competitive threats.
In conclusion, although big data analysis provides important opportunities for improving decision making and efficiency in manufacturing enterprises, excessive reliance on data-driven strategies may inadvertently stifle innovation and flexibility. This may hinder the organization’s ability to maintain competitiveness in today’s dynamic environment. Meanwhile, innovation requires a combination of multiple resources. Due to their limited attention, as the CEO focuses more on big data, the allocation of other key innovation resources is more likely to be affected, thereby increasing the slope of the negative impact on innovation. We combined the theoretical arguments of benefit effect and cost effect (resource constraints) to derive an inverted U-shaped relationship between CEO big data orientation and technological innovation, as shown in Figure 1.
Hypothesis 1.
The relationship between CEO big data orientation and technological innovation is curvilinear in the shape of an inverted U.

2.2. Environmental Investment and Technological Innovation

With the advent of the era of the green economy, the manufacturing industry is an industry with strong environmental requirements, focusing on using advanced production facilities to replace backward technology [50,51]. Increasingly strict government environmental regulations is a trend and key issue that manufacturing enterprises have to face. Under increasingly strict government environmental regulations, energy conservation and emission reduction, as well as compliance with government environmental policies, are inevitable solutions for executives in the manufacturing industry [52]. At the same time, different enterprises have different regional locations, scales, and related products, which will face different levels of government environmental supervision, which makes the environmental pressures that manufacturing industry executives need to face different [15,52,53,54]. Environmental investment, encompassing strategic initiatives aimed at promoting environmental sustainability, serves as a key driver of green strategy. Organizations that invest in environmental sustainability demonstrate a commitment to reducing their environmental footprint, mitigating climate change risks, and enhancing their reputation as environmentally responsible corporate citizens. Environmental investment may take various forms, including investments in renewable energy, eco-friendly technologies, and sustainable supply chain practices, all of which contribute to an organization’s green strategy objectives [55].
From a strategic perspective, if an organization’s strategic priority is environmental sustainability, the CEO may face pressure to prioritize investments in green initiatives over investments in data-driven innovation [56,57]. Specifically, environmental investment is expected to exacerbate the negative impact of CEO big data orientation on technological innovation. The shift of resources from innovative initiatives to sustainable development projects will limit the organization’s technological innovation capabilities. Environmental investment is hypothesized to moderate the cost effect of CEO big data orientation on technological innovation by influencing the allocation of resources and strategic priorities within the organization.
Furthermore, from an attention-based perspective, the CEO’s attention is a limited resource influenced by resource constraints [9,20,33,36], and environmental investment puts companies under more severe resource constraints. Environmental investment requires allocating attention and resources to sustainability initiatives, which may shift attention and resources away from data-driven innovation activities. Allocating less attention makes the CEO’s focus on using big data to guide innovation more stressful, resulting in a relatively harsh innovation environment within the organization. Organizations that heavily invest in environmental sustainability may have fewer resources available for research and development projects or data-driven innovation initiatives, thus limiting their innovation capacity. Consequently, environmental investment may exacerbate the cost effect of CEO big data orientation on technological innovation, further constraining technological innovation efforts.
In conclusion, we believe that the more the CEO big data orientation, the less the CEO pays attention to and uses other innovative resources, and the less it does. Due to resource constraints, enterprise innovation is negatively affected. When the level of environmental investment in a company is high, it means that the company needs the CEO to invest more attention and resources in green development and sustainability, which makes the constraints on attention and resources more intense. Therefore, this paper predicts that for enterprises with a high level of environmental investment, the cost effect of CEO big data orientation on technological innovation will be subject to stronger resource constraints, as shown in Figure 1. In other words, in enterprises with higher levels of environmental investment, the relationship turning point proposed in Hypothesis 1 usually occurs at a lower level.
Hypothesis 2.
Environmental investment moderates the inverted U-shaped relationship between CEO big data orientation and technological innovation, making the negative impact of CEO big data orientation on technological innovation stronger.

3. Data, Variables, and Methodology

3.1. Data

The study sample consists of Chinese manufacturing enterprises. Advanced manufacturing is one of China’s core strategies (Made-in-China 2025), and the emergence of the concept of big data can help the manufacturing industry to become smarter and more competitive [58], which provides an appropriate setting for this paper.
In this paper, three archival data sources are used to test the conceptual model. For technological innovation, the Chinese Research Data Services Platform (CNRDS) is used to obtain patent-related data. We establish the causal relationship between CEO big data orientation and enterprise technological innovation by lagging the patent-related data by 3 years. CEO big data orientation is obtained from annual reports of Chinese listed manufacturing firms [59] and measured by using the computer-assisted content text analysis method. For environmental investment, we obtained the data of publicly listed manufacturing firms from the China Stock Market & Accounting Research Database (CSMAR). For the sampled firms, we also relied on the China Stock Market & Accounting Research Database (CSMAR) to obtain other related data and information, including cash flow, slack, long-term assets, R&D spending, team size, Hi-tech enterprise identification, liquidity, and other financial data from the database. The sample includes 3088 firms listed on the Shenzhen and Shanghai Stock Exchange markets. Incomplete samples were dropped when information was unavailable, resulting in a final sample of 2194 firms (11,746 samples).

3.2. Measures

3.2.1. Dependent Variable

The dependent variable is technological innovation, measured by invention patent applications. These data are obtained from the Chinese Research Data Services Platform (CNRDS). According to patent law in China, patents are divided into three categories: inventions, utility models, and designs. The applications of invention patents are used, which represent new technical solutions related to products or processes, or substantial improvement to existing technical solutions [60]. According to the Derwent Innovation Index, most patent applications of Chinese firms are filed within China, so only patent applications in China are included [61]. Technological innovation requires time, and patent research typically uses a 3-year time lag to test the impact of R&D investment on actual results. Therefore, we used a 3-year time lag to verify the effectiveness.

3.2.2. Independent Variables

In this paper, the content text analysis method is adopted to reflect CEO big data orientation, which is often used to measure attention and orientation. Through computer-assisted text analysis, annual reports are used to measure variables related to the attentional focus of CEOs, the organizations’ top management style, and orientation [59,62,63,64,65,66].
Attention can be measured in two ways: absolute attention and proportionate attention. Absolute attention is measured by the number of sentences referring to key words, and proportionate attention is measured by dividing the number of related sentences by the total number of sentences in the reports [67,68]. In this paper, we mainly use absolute attention to measure CEO big data orientation. According to the requirements of the China Securities Regulatory Commission (CSRC), the disclosure of annual financial reports of listed companies has been reformed since 2012. Therefore, textual data since 2012 were more comparable, and the data of annual financial reports from 2012 to 2018 are used to measure CEO big data orientation.
The specific steps of text analysis are as follows. First, we identified keywords of CEO big data orientation. Big data keywords were extracted based on definitions and related technologies in previous articles, including artificial intelligence, digitization, compute cloud, etc. [69,70,71]. Sentences containing big data keywords from annual financial reports were considered valid. Second, we performed automatic context retrieval and manual confirmation. Based on the determined keywords, a computer automatic extraction method was used to extract sentences containing keywords. Afterward, we manually confirmed the accuracy and validity of the search result sentences. Third, we summarized and counted the results. We summarized the final keyword sentence results that met the requirements to form a keyword information list, which was the configuration results of CEO big data orientation. Then, we calculated the number of sentences, which was the value of CEO big data orientation.
For environmental investment, we use environmental governance costs, related to pollution discharge, environmental protection, and greening [72,73,74], and the data source is the China Stock Market & Accounting Research Database (CSMAR).

3.2.3. Controls

Multiple variables are controlled that may affect technological innovation and CEO big data orientation. Slack is one of the important influencing factors of innovation, as companies with higher levels of slack have more cash and equivalents, staff, and even more advanced technology [75,76]. Organizational slack is measured by dividing the equity to debt ratio [77,78]. Team size of the top management team is considered to represent “parsimoniously represent a team’s structural and compositional context” [79], measured by logarithmically processing the number of executives in the team. It is believed that larger teams have more opinions and interests to promote innovation but also more conflicts and information exchange issues. This paper includes the natural logarithm of annual research and development expenditure as a proxy for R&D spending. Hi-tech enterprises is treated as a dummy value; if the firm operated in the Hi-tech industry, the value is 1, and 0 if not. At the same time, this paper also considers long-term assets (Ln), fixed asset ratio, cash flow (operating cash flow), and liquidity.

3.3. Methodology

In this paper, the data used are panel data. The use of panel data helps to control potential sources of unobserved heterogeneity [80,81]. When establishing empirical analysis models, the number of invention patent applications is usually used as a proxy for innovation [60], with the characteristic of being non-negative count data, and the negative binomial distribution model is always considered [82]. The negative binomial model is a generalized form of the Poisson model [83]. The difference lies in the addition of a discrete parameter to explain the heterogeneity of the data, making the negative binomial model more suitable [84]. When approaching infinity, it indicates that the occurrence of events is not independent and there are clustering characteristics. The Poisson distribution is a special case of the negative binomial distribution. In this paper, the negative binomial model is employed for empirical analysis, while Poisson distribution is used for robustness testing. The negative binomial regression is conducted using STATA 17.

4. Results

Table 1 shows the descriptive statistics and correlations for all variables. We also tested for multicollinearity but found that it did not pose a threat to the results that we reported. As the presence of multicollinearity would cause serious problems, we conducted a variance inflation factor (VIF) test. If the value of VIF is above 10, multicollinearity is very likely [85]. According to the results presented in Table 2, the value of VIF is below 10, implying no serious multicollinearity problem. Figure 2 shows the fitting line of CEO big data orientation and technological innovation, which is an inverted U-shaped curve.
Table 3 shows the regression results for the tests of our hypotheses. We used innovation lagging by 3 years in 2015–2021 as the dependent variable in models 1–3 and used the absolute value of CEO big data orientation as the independent variable to test our hypotheses. Model 1 includes the control variables and is used as a baseline model. Models 4–6 are similar, but the independent variable becomes the proportionate value of CEO big data orientation.
We initially tested a base model including all of the controls (Model 1). The results are reported in Table 3. In Model 2, we added CEO big data orientation and its squared term. We observed that CEO big data orientation has a positive (0.036) and significant (p < 0.001) coefficient, whereas CEO big data orientation squared has a negative (−0.001) and significant (p < 0.001) coefficient. This evidence indicated the existence of an inverted U-shaped relationship between CEO big data orientation and technological innovation and consequently supported Hypothesis 1.
In Model 3, we added the interaction of the indicator of environmental investment and CEO big data orientation and the interaction of the indicator of environmental investment and the CEO big data orientation squared term. The coefficient of CEO big data orientation × environmental investment is positive (0.002) and significant (p < 0.1), whereas the coefficient of CEO big data orientation squared × environmental investment is negative (−0.0003) and again significant (p < 0.01). Environmental investment moderates the effect of CEO big data orientation on technological innovation, and higher environmental investment causes the inverted U-shaped curve to peak at a smaller CEO big data orientation. This result supported Hypothesis 2.
Figure 3 shows the interactive relationship between CEO big data orientation and environmental investment. It shows that as environmental investment increases, the vertex of the inverted U-shaped curve of CEO big data orientation and technological innovation occurs earlier. And under the same level of CEO big data orientation, greater environmental investment will result in the maximum value of technological innovation being at a lower level. This was consistent with Hypothesis 2.
We conducted additional analyses to check the robustness of our results. As shown in Models 4–6 of Table 3, the results were similar when we used the proportionate value of CEO big data orientation as our independent variable. The relationship between CEO big data orientation and technological innovation is still significant and the influence is in the same manner, as is the relationship between CEO big data orientation squared and technological innovation. The coefficient of the interaction of CEO big data orientation and environmental investment is still significant and the influence is in the same manner, as is the interaction of CEO big data orientation squared and environmental investment. We also conducted Poisson regression for predicting technological innovation with fixed effects (Table 4). The relationship between CEO big data orientation and technological innovation remains significant, and the nature of the influence is consistent. Similarly, the relationship between the square of CEO big data orientation and technological innovation also remains significant. The coefficient for the interaction between CEO big data orientation and environmental investment is significant, with the influence persisting in the same manner. Additionally, the interaction between the square of CEO big data orientation and environmental investment also remains significant and consistent.

5. Discussion and Conclusions

This paper has used the resource-based view and attention-based view to study big data, environmental investment, and technological innovation. This paper proposes a hypothesis about the relationship between CEO big data orientation and technological innovation. This paper finds that CEO big data orientation has a significant impact on technological innovation performance. This is consistent with previous research on CEO focus and innovation [35,76]. This paper further finds that CEO big data orientation and environmental investment jointly affect technological innovation. These findings indicate that achieving high innovation performance requires the CEO to find an appropriate match between CEO big data orientation and green strategy.
This research utilizes the concept of attention to study CEO big data orientation, thereby enhancing the understanding of big data in enterprises. Inspired by previous research on big data analytics capabilities [86,87], we emphasize cognitive logic behind CEO big data orientation and enterprise technological innovation through two opposing effects: benefit effect and cost effect (resource constraints). By doing so, this paper offers a complete and comprehensive understanding of the relationship between CEO big data orientation and technological innovation. From the perspective of environmental protection in the manufacturing industry, this paper provides empirical evidence to support the significance of CEO big data orientation and its joint effect with environmental investment, which enhances the literature on information technology strategy and environmental awareness [66,88]. In addition, by combining the benefit effect and cost effect of CEO big data orientation on technological innovation, described as an inverted U-shaped effect, this paper provides a theoretical basis for enterprises to build big data platforms and capabilities.
Regarding our specific results, as predicted, there is a significant inverted U-shaped relationship between the focus of CEO big data orientation and technological innovation. Our insight was that CEO big data orientation has both a benefit effect and a cost effect (resource constraints) on technological innovation. In terms of the benefit effect, focusing on big data is more likely to help the CEO search for novel knowledge and conduct a thorough analysis. Therefore, CEOs who focus on big data are more likely to seize innovation opportunities and make effective decisions. To a large extent, this discovery resonates with arguments in the big data literature, which suggest that the related technologies of big data are typically based on basic support for volume, velocity, variety, value, and also veracity to address the challenges of the enterprise data landscape and promote innovation [89,90]. In terms of the cost effect, due to the limited attention of the CEO, when the CEO focuses more on one focus, the CEO’s attention on other focuses will be diluted because of resource constraints. In enterprises, the resources required for technological innovation activities are multifaceted, including adequate knowledge reserves, sustained R&D activities, a reasonable investment portfolio, and so on. CEO big data orientation will reduce the attention allocated to other resources, thereby affecting the rational allocation of technological innovation resources and negatively impacting technological innovation. In addition, from a strategic perspective, because of the potential pitfalls of an overly data-centric approach to decision making, overemphasizing data may lead to a lack of strategic agility, thereby limiting the organization’s opportunities for innovation. Therefore, the focus of CEO big data orientation is combined with the benefit effect and cost effect of technological innovation, forming an inverted U-shaped relationship. That is, from the perspective of the impact of CEO big data orientation on technological innovation, at the beginning, technological innovation increases as the CEO invests more attention on big data, gradually reaching a peak, and then technological innovation decreases as CEO big data orientation continues to increase.
Big data and its technology not only bring opportunities but also challenges. The challenge is not only to collect and manage a large amount of different types of data, but also to extract meaningful value from the information. Equally needed are managers and analysts with outstanding insights on how to apply big data [91]. There are many big data management challenges discussed in previous literature, such as intellectual priority [92]. Sometimes, big data, as a paradigm, can be a double-edged sword, capable of significantly advancing our field but also causing backlash if not utilized properly [93]. This article provides an explanation from the perspective of attention for the double-edged sword role of big data in enterprise performance and innovation, highlighting the importance of the CEO’s attention allocation and rational investment in big data.
This paper also demonstrates the joint effect of environmental investment and CEO big data orientation through empirical analysis. Given the importance of environmental investment in previous research on enterprise innovation, it is crucial to understand how environmental investment and CEO big data orientation are interconnected in the technological innovation process. Similar to our expectations, the emergence of environmental investment will lead to an earlier peak in the inverted U-shaped effect curve of CEO big data orientation on technological innovation This paper has noted that at high levels of environmental investment, the resource constraints on CEO big data orientation will be stronger, which will strengthen the cost impact of CEO big data orientation on technological innovation. Therefore, compared to low levels, at higher levels of environmental investment, technological innovation will peak at a smaller CEO big data orientation. At the same time, the peak of technological innovation under high levels of environmental investment will be smaller than the peak under low levels of environmental investment. In fact, previous research has also shown that the impact of big data on corporate innovation varies across different investment portfolios [94]. Environmental investment has a crowding-out effect on innovation-related expenditures of enterprises. There are adverse effects of the excessive financial burden of corporate environmental investments on the innovation capacity of Chinese firms [95].

5.1. Theoretical Implications

This research has several important theoretical implications. In this research, the derived role of the resource-based view is brought to light in explaining technological innovation, revealing how CEO big data orientation can maximize the effectiveness of positively guiding firms towards technological innovation. This research highlights the importance of CEO big data orientation as it relates to innovation, emphasizing that the success of big data within organizations relies not only on technological aspects but also on attention and strategic factors that are currently overlooked. Prior research has predominantly focused on the linear relationship between CEO characteristics and organizational outcomes, neglecting the potential curvilinear effects. In fact, enterprises may fail to achieve appreciable results when focusing on big data resources to improve themselves [96,97]. Although the result may be partly due to the nature of big data technology [71,98], based on our findings, it may be also partly due to the inverted U-shaped effect of CEO big data orientation on technological innovation.
Firstly, the impact of big data and its analytical capabilities on organizational innovation has been widely studied [86,99]. However, few studies have explored the relationship between CEO big data orientation and technological innovation from the perspectives of strategic management and attention. Although enterprises have more big data technologies and resources that can help with technological innovation, without sufficient attention to big data, it is difficult to update and integrate big data technologies and resources in a timely manner, resulting in insufficient effectiveness and practicality. From the attention-based view, in existing research explaining the relationship between executive attention and innovation, big data, as a key resource, has not yet been considered the focus of attention as an influencing factor of enterprise innovation. In addition, existing research on the relationship between CEO orientation and innovation has not fully considered big data orientation as a strategic factor. This negligence has led to an incomplete theoretical explanation of the role of enterprise big data resources in driving innovation. This paper contributes to the attention-based view literature by introducing the concept of CEO big data orientation, which provides a theoretical expansion for the impact of CEO big data orientation on technological innovation performance. This study proposes that CEO big data orientation is not merely a technical consideration but a strategic one that requires focused attention from top executives. By analyzing the impact of CEO big data orientation on technological innovation from the perspectives of strategic management and attention, this study reveals the relationship between CEO big data orientation and technological innovation. This broadens the scope of strategic perspective and provides new ideas for deepening innovation theory.
Secondly, this study contributes to the understanding of the nonlinear effects of CEO orientation on innovation by analyzing the inverted U-shaped impact of CEO big data orientation on technological innovation. Most existing studies have focused on the unidirectional positive effects of big data on corporate performance [100]. Our study examines both the benefit and cost effects of CEO big data orientation on technological innovation from the perspectives of strategic management and resource constraints, supplementing the lack of existing research on the impact mechanism of big data on technological innovation performance. This adds depth to the research on the impact mechanism of CEO orientation on innovation in enterprises, thereby strengthening the explanatory effect of big data in innovation theory. By demonstrating the presence of an inverted U-shaped effect, this research offers a more nuanced understanding of how CEO big data orientation can both facilitate and hinder technological innovation. Furthermore, by proposing a “U-shaped” relationship between CEO big data orientation and technological innovation, this research contributes to the refinement of existing theories on leadership and innovation.
Thirdly, this study highlights the moderating role of environmental investment in the inverted U-shaped impact of CEO big data orientation on technological innovation, contributing to green strategy research. By considering the effects of environmental sustainability measures on organizational innovation outcomes, this study emphasizes the need to align environmental goals with data-driven innovation strategies. This approach helps to uncover the mechanisms and boundaries of CEO big data orientation in promoting technological innovation. It also provides a more comprehensive understanding of the interplay between environmental issues, strategic balance, and innovation outcomes, revealing how organizations can balance sustainable development goals with data-driven innovation needs.
Finally, this study integrates big data orientation with green strategy to enhance strategic management theory. The interaction between the CEO’s focus on big data and their commitment to environmental sustainability significantly impacts innovation performance. This integration contributes to strategic balance research by emphasizing the need for harmony between the technical and environmental aspects of strategic management. This balanced approach is essential for fostering innovation while maintaining a commitment to sustainability within manufacturing enterprises.

5.2. Practical Implications

This study provides valuable practical and policy insights, as more and more enterprises are using big data to identify opportunities and respond by strengthening their technological innovation.
Firstly, this paper emphasizes the importance of CEO strategic attention allocation in driving technological innovation. When dealing with complex environmental pressures, CEOs of manufacturing enterprises should actively and reasonably pay attention to and utilize big data and explore technological innovation opportunities for enterprises. By recognizing the nature of attention and its role in shaping organizational priorities and decision-making processes, practitioners can adopt more strategic approaches to utilizing big data analysis. CEOs should be encouraged to allocate their attention wisely and ensure that the integration of big data technology aligns with broader organizational goals. This strategic adjustment can help organizations cope with the complexity of the digital age while maintaining a focus on innovation and competitive advantage.
Secondly, we point out that CEO big data orientation has a dual impact on enterprise technological innovation, with both benefit and cost effects. In terms of the benefit effect, CEO big data orientation can promote technological innovation performance. In terms of the cost effect, CEO big data orientation will have a negative impact on technological innovation performance. Combining the two effects, the impact of CEO big data orientation on technological innovation is an inverted U shape, with a peak. The findings indicate that while the initial increase in CEO big data orientation may promote technological innovation in manufacturing enterprises, excessive emphasis on big data may ultimately hinder innovation outcomes. This understanding can enable organizational leaders to understand the optimal investment level and focus on big data analysis to maximize innovation while avoiding potential diminishing returns. Specifically, this requires organizational leaders to constantly focus on the allocation of attention and resources in data-driven innovation. They need to invest a certain amount of attention and resources in big data, but not too much. Only when the CEO becomes more aware of this real-time situation can they strive to enhance the technological innovation strength through the use of big data, achieving twice the result with half the effort.
Thirdly, the paper highlights the crucial importance of balancing big data initiatives with environmental investment. This study reveals the potential risks of combining overly ambitious big data orientation with significant environmental investments, and manufacturing companies can use this insight to reassess their strategic priorities. Recognizing that environmental investment can amplify the potential negative impact of CEO big data orientation on innovation, it is crucial to carefully adjust investments in these two areas. This ensures effective resource allocation, making big data analysis and environmental sustainability complementary rather than conflicting. Although big data analysis and environmental sustainability are crucial for long-term success, they also pursue strategic adjustments that require caution. This research suggests that CEOs should evaluate the strength of their big data initiatives in the context of environmental investment. This may involve reducing or rethinking strategic layouts to ensure they do not conflict with innovation strategies or reduce their effectiveness. By doing so, the CEO can better manage the balance between these two strategic priorities and create a more favorable environment for technological innovation.
Finally, the paper emphasizes the importance of strategic balance in innovation management. Manufacturing enterprises should strive to develop a comprehensive strategy that aligns big data initiatives with environmental goals in a way that supports rather than hinders technological innovation. This involves a clear understanding of how these two areas interact and ensuring consistency in resource allocation, organizational priorities, and strategic initiatives. Practical measures may include cross-functional teams responsible for coordinating big data and environmental projects to reduce any negative interactions and promote synergies. For example, BYD, as a leading electric vehicle manufacturer in China, launched the BMS active, a brand new third-generation battery pack management solution with independent intellectual property rights in 2022. This plan not only utilizes big data technology to optimize battery management systems and improve battery performance and lifespan, but it also invests in environmentally friendly materials and technological research and development in battery technology, reducing the impact of batteries on the environment. The successful implementation of this plan relies on BYD’s cross-departmental collaboration, promoting collaboration between different departments to ensure the coordinated application of big data analysis and green investment in various projects.

5.3. Limitations and Future Research Directions

While our study has yielded intriguing findings, we acknowledge several limitations that could guide future research endeavors. Firstly, as noted in Section 3, our sample selection was confined to Chinese manufacturing enterprises. While manufacturing enterprises are prevalent, future investigations could benefit from extending the scope to include enterprises in the service industry or other sectors. This extension would enable researchers to assess the applicability of our findings across different industries.
Secondly, although our study contributes to our comprehension of the management revolution catalyzed by the big data era, it is imperative to acknowledge that our examination of the relationship between CEO big data orientation and green strategy represents only a fraction of the broader digital green transformation. Considering strategic management theory and strategic balance, further research could delve deeper into the intricate mechanisms underlying this transformation. Additionally, exploring how the relationship between CEO big data orientation and environmental protection manifests across various regional policies and levels of environmental technology within enterprises could provide valuable insight.
Lastly, our study’s generalizability to other countries may be constrained by our exclusive focus on Chinese organizations. To enhance the external validity of our findings, future studies should consider incorporating samples from diverse national contexts, allowing for a more comprehensive understanding of the universal implications of CEO big data orientation on innovation. By addressing these limitations, future research endeavors can enrich our understanding of the complex interplay between big data orientation, green strategy, and organizational outcomes.

Author Contributions

Conceptualization, W.W.; methodology, X.W.; software, X.W.; validation, X.W.; formal analysis, X.W.; investigation, X.W.; resources, X.W.; data curation, X.W.; writing—original draft preparation, X.W.; writing—review and editing, X.W.; visualization, X.W.; supervision, W.W.; project administration, W.W.; funding acquisition, W.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by [the National Natural Science Foundation of China] grant number [72072047]; [Natural Science Foundation of Shandong Province] grant number [ZR2023QG010]; [the Fundamental Research Funds for the Central Universities] grant number [HIT.HSS.ESD202310]; [The Research Project on Graduates’ Education and Teaching Reform of HIT] grant number [23MS011]; [Research Project on Higher Education of Heilongjiang Higher Education Association] grant number [23GJYBC011].

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Damanpour, F. Organizational innovation: A meta-analysis of effects of determinants and moderators. Acad. Manag. J. 1991, 34, 555–590. [Google Scholar] [CrossRef]
  2. Distanont, A. The role of innovation in creating a competitive advantage. Kasetsart J. Soc. Sci. 2020, 41, 15–21. [Google Scholar] [CrossRef]
  3. Farida, I.; Setiawan, D. Business strategies and competitive advantage: The role of performance and innovation. J. Open Innov. Technol. Mark. Complex. 2022, 8, 163. [Google Scholar] [CrossRef]
  4. Nonaka, I.; Toyama, R.; Konno, N. SECI, Ba and leadership: A unified model of dynamic knowledge creation. Long Range Plan. 2000, 33, 5–34. [Google Scholar] [CrossRef]
  5. Davenport, T.H.; Barth, P.; Bean, R. How Big Data Is Different. MIT Sloan Manag. Rev. 2012, 54, 43. [Google Scholar]
  6. Wamba, S.F.; Gunasekaran, A.; Akter, S.; Ren, S.J.; Dubey, R.; Childe, S.J. Big data analytics and firm performance: Effects of dynamic capabilities. J. Bus. Res. 2017, 70, 356–365. [Google Scholar] [CrossRef]
  7. Klein, V.B.; Todesco, J.L. COVID-19 crisis and SMEs responses: The role of digital transformation. Knowl. Process Manag. 2021, 28, 117–133. [Google Scholar] [CrossRef]
  8. Pinski, M.; Hofmann, T.; Benlian, A. AI Literacy for the top management: An upper echelons perspective on corporate AI orientation and implementation ability. Electron. Mark. 2024, 34, 24. [Google Scholar] [CrossRef]
  9. Ocasio, W. Towards an attention-based view of the firm. Strateg. Manag. J. 1997, 18, 187–206. [Google Scholar] [CrossRef]
  10. Fuertes, G.; Alfaro, M.; Vargas, M.; Gutierrez, S.; Ternero, R.; Sabattin, J. Conceptual framework for the strategic management: A literature review—Descriptive. J. Eng. 2020, 2020, 6253013. [Google Scholar] [CrossRef]
  11. Hitt, M.A.; Arregle, J.L.; Holmes, R.M., Jr. Strategic management theory in a post-pandemic and non-ergodic world. J. Manag. Stud. 2021, 58, 259–264. [Google Scholar] [CrossRef]
  12. Bordum, A. The strategic balance in a change management perspective. Soc. Bus. Rev. 2010, 5, 245–258. [Google Scholar] [CrossRef]
  13. Tran, T.; Do, H.; Vu, T.; Do, N. The factors affecting green investment for sustainable development. Decis. Sci. Lett. 2020, 9, 365–386. [Google Scholar] [CrossRef]
  14. Ghobadian, A.; Viney, H.; James, P.; Lui, J. The influence of environmental issues in strategic analysis andchoice: A review of environmental strategy among top UK corporations. Manag. Decis. 1995, 33, 46–58. [Google Scholar] [CrossRef]
  15. Jiang, X.F.; Zhao, C.X.; Ma, J.J.; Liu, J.Q.; Li, S.H. Is enterprise environmental protection investment responsibility or rent-seeking? Chinese evidence. Environ. Dev. Econ. 2021, 26, 169–187. [Google Scholar] [CrossRef]
  16. Giampietro, M.; Mayumi, K. Unraveling the complexity of the Jevons Paradox: The link between innovation, efficiency, and sustainability. Front. Energy Res. 2018, 6, 26. [Google Scholar] [CrossRef]
  17. Ocasio, W. Attention to attention. Organ. Sci. 2011, 22, 1286–1296. [Google Scholar] [CrossRef]
  18. Posner, M.I.; Snyder, C.R.; Davidson, B.J. Attention and the detection of signals. J. Exp. Psychol. Gen. 1980, 109, 160–174. [Google Scholar] [CrossRef]
  19. Rueda, M.R.; Moyano, S.; Rico-Picó, J. Attention: The grounds of self-regulated cognition. Wiley Interdiscip. Rev. Cogn. Sci. 2023, 14, e1582. [Google Scholar] [CrossRef]
  20. Dutton, J.E.; Ashford, S.J. Selling issues to TMT. Acad. Manag. Rev. 1993, 18, 397–428. [Google Scholar] [CrossRef]
  21. McAfee, A.; Brynjolfsson, E. Big data: The management revolution. Harv. Bus. Rev. 2012, 90, 60–68. [Google Scholar] [PubMed]
  22. Provost, F.; Fawcett, T. Data Science and its Relationship to Big Data and Data-Driven Decision Making. Big Data 2013, 1, 51–59. [Google Scholar] [CrossRef] [PubMed]
  23. Serrano-Guerrero, J.; Romero, F.P.; Olivas, J.A. Fuzzy logic applied to opinion mining: A review. Knowl.-Based Syst. 2021, 222, 107018. [Google Scholar] [CrossRef]
  24. Sohrabpour, V.; Oghazi, P.; Toorajipour, R.; Nazarpour, A. Export sales forecasting using artificial intelligence. Technol. Forecast. Soc. Chang. 2021, 163, 120480. [Google Scholar] [CrossRef]
  25. Ghasemaghaei, M.; Calic, G. Does big data enhance firm innovation competency? The mediating role of data-driven insights. J. Bus. Res. 2019, 104, 69–84. [Google Scholar] [CrossRef]
  26. Ghasemaghaei, M.; Calic, G. Assessing the impact of big data on firm innovation performance: Big data is not always better data. J. Bus. Res. 2020, 108, 147–162. [Google Scholar] [CrossRef]
  27. Lee, J.; Kao, H.A.; Yang, S. Service innovation and smart analytics for industry 4.0 and big data environment. Procedia Cirp 2014, 16, 3–8. [Google Scholar] [CrossRef]
  28. Antons, D.; Breidbach, C.F. Big data, big insights? Advancing service innovation and design with machine learning. J. Serv. Res. 2018, 21, 17–39. [Google Scholar] [CrossRef]
  29. Chowdhury, S.; Budhwar, P.; Dey, P.K.; Joel-Edgar, S.; Abadie, A. AI-employee collaboration and business performance: Integrating knowledge-based view, socio-technical systems and organisational socialisation framework. J. Bus. Res. 2022, 144, 31–49. [Google Scholar] [CrossRef]
  30. Hock-Doepgen, M.; Clauss, T.; Kraus, S.; Cheng, C.F. Knowledge management capabilities and organizational risk-taking for business model innovation in SMEs. J. Bus. Res. 2021, 130, 683–697. [Google Scholar] [CrossRef]
  31. Niebel, T.; Rasel, F.; Viete, S. BIG data–BIG gains? Understanding the link between big data analytics and innovation. Econ. Innov. New Technol. 2019, 28, 296–316. [Google Scholar] [CrossRef]
  32. Van der Voet, J.; Steijn, B. Team innovation through collaboration: How visionary leadership spurs innovation via team cohesion. Public Manag. Rev. 2021, 23, 1275–1294. [Google Scholar] [CrossRef]
  33. Brielmaier, C.; Friesl, M. The attention-based view: Review and conceptual extension towards situated attention. Int. J. Manag. Rev. 2023, 25, 99–129. [Google Scholar] [CrossRef]
  34. Bouquet, C.; Birkinshaw, J. Weight versus voice: How foreign subsidiaries gain attention from corporate headquarters. Acad. Manag. J. 2008, 51, 577–601. [Google Scholar] [CrossRef]
  35. Haas, M.R.; Criscuolo, P.; George, G. Which problems to solve? Attention allocation and online knowledge sharing in organizations. Acad. Manag. J. 2015, 58, 680–711. [Google Scholar] [CrossRef]
  36. Hoffman, A.J.; Ocasio, W. Not all events are attended equally: Toward a middle-range theory of industry attention to external events. Organ. Sci. 2001, 12, 414–434. [Google Scholar] [CrossRef]
  37. Berchicci, L. Towards an open R&D system: Internal R&D investment, external knowledge acquisition and innovative performance. Res. Policy 2013, 42, 117–127. [Google Scholar]
  38. Hall, B.H.; Lerner, J. The financing of R&D and innovation. In Handbook of the Economics of Innovation; Elsevier: Amsterdam, The Netherlands, 2010; Volume 1, pp. 609–639. [Google Scholar]
  39. Heij, C.V.; Volberda, H.W.; Van den Bosch, F.A.; Hollen, R.M. How to leverage the impact of R&D on product innovation? The moderating effect of management innovation. RD Manag. 2020, 50, 277–294. [Google Scholar]
  40. Sørensen, E.; Torfing, J. Introduction: Collaborative innovation in the public sector. Innov. J. 2012, 17, 1–14. [Google Scholar]
  41. Zahoor, N.; Al-Tabbaa, O. Inter-organizational collaboration and SMEs’ innovation: A systematic review and future research directions. Scand. J. Manag. 2020, 36, 101109. [Google Scholar] [CrossRef]
  42. Ferlie, E.; Fitzgerald, L.; Wood, M.; Hawkins, C. The nonspread of innovations: The mediating role of professionals. Acad. Manag. J. 2005, 48, 117–134. [Google Scholar] [CrossRef]
  43. Thursby, M.C.; Fuller, A.W.; Thursby, J. An integrated approach to educating professionals for careers in innovation. Acad. Manag. Learn. Educ. 2009, 8, 389–405. [Google Scholar]
  44. Wei, Y.; Nan, H.; Wei, G. The impact of employee welfare on innovation performance: Evidence from China’s manufacturing corporations. Int. J. Prod. Econ. 2020, 228, 107753. [Google Scholar] [CrossRef]
  45. Guo, F.; Zou, B.; Zhang, X.; Bo, Q.; Li, K. Financial slack and firm performance of SMMEs in China: Moderating effects of government subsidies and market-supporting institutions. Int. J. Prod. Econ. 2020, 223, 107530. [Google Scholar] [CrossRef]
  46. Kline, S.J.; Rosenberg, N. An overview of innovation. In Studies on Science and the Innovation Process: Selected Works of Nathan Rosenberg; World Scientific: Singapore, 2010; pp. 173–203. [Google Scholar]
  47. Currie, W.L. Revisiting management innovation and change programmes: Strategic vision or tunnel vision? Omega 1999, 27, 647–660. [Google Scholar] [CrossRef]
  48. Niemi-Kaija, K.; Pattinson, S. A review of strategic visioning and organizational performance: Epistemological challenges. Manag. Res. Rev. 2024, 47, 673–688. [Google Scholar] [CrossRef]
  49. Schoemaker, P.J. Scenario planning: A tool for strategic thinking. MIT Sloan Manag. Rev. 1995, 36, 25–40. [Google Scholar]
  50. Gan, Q.; Yang, L.; Liu, J.; Cheng, X.; Qin, H.; Su, J.; Xia, W. The level of regional economic development, green image, and enterprise environmental protection investment: Empirical evidence from China. Math. Probl. Eng. 2021, 2021, 5522351. [Google Scholar] [CrossRef]
  51. Marquis, C.; Zhang, J.; Zhou, Y. Regulatory uncertainty and corporate responses to environmental protection in China. Calif. Manag. Rev. 2011, 54, 39–63. [Google Scholar] [CrossRef]
  52. Costa-Campi, M.T.; García-Quevedo, J.; Martínez-Ros, E. What are the determinants of investment in environmental R&D? Energy Policy 2017, 104, 455–465. [Google Scholar]
  53. Doonan, J.; Lanoie, P.; Laplante, B. Determinants of environmental performance in the Canadian pulp and paper industry: An assessment from inside the industry. Ecol. Econ. 2005, 55, 73–84. [Google Scholar] [CrossRef]
  54. Murovec, N.; Erker, R.S.; Prodan, I. Determinants of environmental investments: Testing the structural model. J. Clean. Prod. 2012, 37, 265–277. [Google Scholar] [CrossRef]
  55. ISAR. Accounting and Financial Reporting for Environmental Costs and Liabilities. 1998. Available online: http://www.unctad.org (accessed on 14 December 2009).
  56. Ghobakhloo, M.; Iranmanesh, M. Digital transformation success under Industry 4.0: A strategic guideline for manufacturing SMEs. J. Manuf. Technol. Manag. 2021, 32, 1533–1556. [Google Scholar] [CrossRef]
  57. Joshi, M.P.; Kathuria, R.; Porth, S.J. Alignment of strategic priorities and performance: An integration of operations and strategic management perspectives. J. Oper. Manag. 2003, 21, 353–369. [Google Scholar] [CrossRef]
  58. Cui, Y.; Kara, S.; Chan, K.C. Manufacturing big data ecosystem: A systematic literature review. Robot. Comput.-Integr. Manuf. 2020, 62, 101861. [Google Scholar] [CrossRef]
  59. Ahn, Y. A socio-cognitive model of sustainability performance: Linking CEO career experience, social ties, and attention breadth. J. Bus. Ethics 2022, 175, 303–321. [Google Scholar] [CrossRef]
  60. Zhou, K.Z.; Gao, G.Y.; Zhao, H. State ownership and firm innovation in China: An integrated view of institutional and efficiency logics. Adm. Sci. Q. 2017, 62, 375–404. [Google Scholar] [CrossRef]
  61. Yang, D.; Wang, A.X.; Zhou, K.Z.; Jiang, W. Environmental strategy, institutional force, and innovation capability: A managerial cognition perspective. J. Bus. Ethics 2019, 159, 1147–1161. [Google Scholar] [CrossRef]
  62. Abrahamson, E.; Hambrick, D.C. Attentional homogeneity in industries: The effect of discretion. J. Organ. Behav. Int. J. Ind. Occup. Organ. Psychol. Behav. 1997, 18, 513–532. [Google Scholar] [CrossRef]
  63. Antons, D.; Grünwald, E.; Cichy, P.; Salge, T.O. The application of text mining methods in innovation research: Current state, evolution patterns, and development priorities. RD Manag. 2020, 50, 329–351. [Google Scholar] [CrossRef]
  64. Jancenelle, V.; Storrud-Barnes, S.F.; Buccieri, D. Market orientation and firm performance: Can there be too much of a good thing? Manag. Decis. 2022, 60, 1683–1701. [Google Scholar] [CrossRef]
  65. McKenny, A.F.; Short, J.C.; Ketchen, D.J., Jr.; Payne, G.T.; Moss, T.W. Strategic entrepreneurial orientation: Configurations, performance, and the effects of industry and time. Strateg. Entrep. J. 2018, 12, 504–521. [Google Scholar] [CrossRef]
  66. Yadav, M.S.; Prabhu, J.C.; Chandy, R.K. Managing the future: CEO attention and innovation outcomes. J. Mark. 2007, 71, 84–101. [Google Scholar] [CrossRef]
  67. D’Aveni, R.A.; MacMillan, I.C. Crisis and the content of managerial communications: A study of the focus of attention of top managers in surviving and failing firms. Adm. Sci. Q. 1990, 35, 634–657. [Google Scholar] [CrossRef]
  68. Huang, C.Y.; Liu, P.Y.; Xie, S.M. Predicting brand equity by text-analyzing annual reports. Int. J. Mark. Res. 2020, 62, 300–313. [Google Scholar] [CrossRef]
  69. Akter, S.; Wamba, S.F.; Gunasekaran, A.; Dubey, R.; Childe, S.J. How to improve firm performance using big data analytics capability and business strategy alignment? Int. J. Prod. Econ. 2016, 182, 113–131. [Google Scholar] [CrossRef]
  70. Oussous, A.; Benjelloun, F.Z.; Lahcen, A.A.; Belfkih, S. Big Data technologies: A survey. J. King Saud Univ.-Comput. Inf. Sci. 2018, 30, 431–448. [Google Scholar] [CrossRef]
  71. Storey, V.C.; Song, I.Y. Big data technologies and management: What conceptual modeling can do. Data Knowl. Eng. 2017, 108, 50–67. [Google Scholar] [CrossRef]
  72. Antheaume, N. Valuing external costs–from theory to practice: Implications for full cost environmental accounting. Eur. Account. Rev. 2004, 13, 443–464. [Google Scholar] [CrossRef]
  73. Jasch, C. The use of Environmental Management Accounting (EMA) for identifying environmental costs. J. Clean. Prod. 2003, 11, 667–676. [Google Scholar] [CrossRef]
  74. Shabbir, M.S.; Wisdom, O. The relationship between corporate social responsibility, environmental investments and financial performance: Evidence from manufacturing companies. Environ. Sci. Pollut. Res. 2020, 27, 39946–39957. [Google Scholar] [CrossRef] [PubMed]
  75. Thompson, J.D. Organizations in Action; McGraw-Hill: New York, NY, USA, 1967. [Google Scholar]
  76. Wu, H.; Hu, S. The impact of synergy effect between government subsidies and slack resources on green technology innovation. J. Clean. Prod. 2020, 274, 122682. [Google Scholar] [CrossRef]
  77. Bromiley, P. Testing a causal model of corporate risk taking and performance. Acad. Manag. J. 1991, 34, 37–59. [Google Scholar] [CrossRef]
  78. Greve, H.R. A behavioral theory of R&D expenditures and innovations: Evidence from shipbuilding. Acad. Manag. J. 2003, 46, 685–702. [Google Scholar]
  79. Amason, A.C.; Sapienza, H.J. The effects of top management team size and interaction norms on cognitive and affective conflict. J. Manag. 1997, 23, 495–516. [Google Scholar] [CrossRef]
  80. Adner, R.; Kapoor, R. Value creation in innovation ecosystems: How the structure of technological interdependence affects firm performance in new technology generations. Strateg. Manag. J. 2010, 31, 306–333. [Google Scholar] [CrossRef]
  81. Afuah, A. Mapping technological capabilities into product markets and competitive advantage: The case of cholesterol drugs. Strateg. Manag. J. 2002, 23, 171–179. [Google Scholar] [CrossRef]
  82. Chen, Z.; Guan, J. The impact of small world on innovation: An empirical study of 16 countries. J. Informetr. 2010, 4, 97–106. [Google Scholar] [CrossRef]
  83. Chen, Q. Advanced Econometrics and Stata Applications. Higher Education Press: Beijing, China, 2014. [Google Scholar]
  84. Kareem, O.I. The determinants of large-scale land investments in Africa. Land Use Policy 2018, 75, 180–190. [Google Scholar] [CrossRef]
  85. Myers, R.H.; Myers, R.H. Classical and Modern Regression with Applications; Duxbury Press: Belmont, CA, USA, 1990; Volume 2, p. 488. [Google Scholar]
  86. Ciampi, F.; Demi, S.; Magrini, A.; Marzi, G.; Papa, A. Exploring the impact of big data analytics capabilities on business model innovation: The mediating role of entrepreneurial orientation. J. Bus. Res. 2021, 123, 1–13. [Google Scholar] [CrossRef]
  87. Mikalef, P.; Krogstie, J.; Pappas, I.O.; Pavlou, P. Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities. Inf. Manag. 2020, 57, 103169. [Google Scholar] [CrossRef]
  88. Abdillah, A.; Mastuti, A.G.; Rijal, M.; Sehuwaky, N. Islamic Integrated Information Communication Technology Mathematics Learning Model for Students’ Creativity and Environmental Awareness. JTAM (J. Teor. Dan Apl. Mat.) 2022, 6, 194–211. [Google Scholar] [CrossRef]
  89. Dhar, S.; Mazumdar, S. Challenges and best practices for enterprise adoption of big data technologies. In Proceedings of the 2014 IEEE International Technology Management Conference, Chicago, IL, USA, 12–15 June 2014; pp. 1–4. [Google Scholar]
  90. He, W.; Wang, F.K.; Akula, V. Managing extracted knowledge from big social media data for business decision making. J. Knowl. Manag. 2017, 21, 275–294. [Google Scholar] [CrossRef]
  91. Sagiroglu, S.; Sinanc, D. Big data: A review. In Proceedings of the 2013 International Conference on Collaboration Technologies and Systems (CTS), San Diego, CA, USA, 20–24 May 2013; pp. 42–47. [Google Scholar]
  92. Berisha, B.; Mëziu, E.; Shabani, I. Big data analytics in Cloud computing: An overview. J. Cloud Comput. 2022, 11, 24. [Google Scholar] [CrossRef] [PubMed]
  93. Wenzel, R.; Van Quaquebeke, N. The double-edged sword of big data in organizational and management research: A review of opportunities and risks. Organ. Res. Methods 2018, 21, 548–591. [Google Scholar] [CrossRef]
  94. Ma, C.; Ren, S. Navigating the double-edged sword: How does big data affect firm innovation under different investment combinations? Asian J. Technol. Innov. 2024, 1–22. [Google Scholar] [CrossRef]
  95. Jiang, X.; Akbar, A.; Hysa, E.; Akbar, M. Environmental protection investment and enterprise innovation: Evidence from Chinese listed companies. Kybernetes 2023, 52, 708–727. [Google Scholar] [CrossRef]
  96. Abbasi, A.; Sarker, S.; Chiang, R.H. Big data research in information systems: Toward an inclusive research agenda. J. Assoc. Inf. Syst. 2016, 17, 3. [Google Scholar] [CrossRef]
  97. Mikalef, P.; Boura, M.; Lekakos, G.; Krogstie, J. Big data analytics and firm performance: Findings from a mixed-method approach. J. Bus. Res. 2019, 98, 261–276. [Google Scholar] [CrossRef]
  98. Gandomi, A.; Haider, M. Beyond the hype: Big data concepts, methods, and analytics. Int. J. Inf. Manag. 2015, 35, 137–144. [Google Scholar] [CrossRef]
  99. Mikalef, P.; Krogstie, J. Examining the interplay between big data analytics and contextual factors in driving process innovation capabilities. Eur. J. Inf. Syst. 2020, 29, 260–287. [Google Scholar] [CrossRef]
  100. Raguseo, E. Big data technologies: An empirical investigation on their adoption, benefits and risks for companies. Int. J. Inf. Manag. 2018, 38, 187–195. [Google Scholar] [CrossRef]
Systems 12 00255 g001
Figure 1. The theoretical model of technological innovation.
Figure 1. The theoretical model of technological innovation.
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Figure 2. The fitting line of CEO big data orientation and technological innovation.
Figure 2. The fitting line of CEO big data orientation and technological innovation.
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Figure 3. Interaction of CEO big data orientation and environmental investment.
Figure 3. Interaction of CEO big data orientation and environmental investment.
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Table 1. Descriptive statistics.
Table 1. Descriptive statistics.
Observations: 11,746Summary Statistics
MeanSd1234567891011
1Technological innovation42.473216.0281.00
2CEO big data orientation4.8567.3220.041.00
3Environmental investment1.5279.0560.01−0.061.00
4Long-term assets (Ln)20.7385.1050.110.080.081.00
5Liquidity0.5470.2110.060.12−0.100.571.00
6Fixed asset ratio0.2220.146−0.03−0.160.180.38−0.281.00
7Cash flow2.66853.6410.000.000.010.02−0.020.051.00
8Hi-tech enterprises0.4990.5000.080.140.000.260.180.000.001.00
9Slack0.0030.004−0.060.04−0.060.090.23−0.08−0.020.001.00
10R&D spending16.1105.4350.080.140.020.740.500.200.020.330.091.00
11Team size1.8380.5300.090.080.070.850.490.330.010.230.080.651.00
Table 2. Results of the VIF test.
Table 2. Results of the VIF test.
VariableVIF1/VIF
CEO big data orientation1.0800.925
Environmental investment1.0500.957
Long-term assets (Ln)6.0400.166
Liquidity2.7400.365
Fixed asset ratio2.1700.460
Cash flow1.0000.997
Hi-tech enterprises1.1400.876
Slack1.0600.939
R&D spending2.3700.423
Team size3.5500.282
Mean VIF2.220
Table 3. Negative binomial regression predicting technological innovation.
Table 3. Negative binomial regression predicting technological innovation.
VariablesTechnological Innovation (with Absolute Value of CEO Big Data Orientation)Technological Innovation (with Proportionate Value of CEO Big Data Orientation)
Model 1 Model 2 Model 3 Model 1 Model 2 Model 3
Independent variable
CEO big data orientation 0.036 ***0.036** 0.032***0.032***
(0.003) (0.003) (0.003) (0.003)
CEO big data orientation squared −0.001***−0.001*** −0.0004***−0.0004***
(0.000) (0.000) (0.000) (0.000)
Environmental investment −0.008***−0.009*** −0.008***−0.008***
(0.001) (0.002) (0.001) (0.002)
Interaction
CEO big data orientation × Environmental investment 0.002† 0.001
(0.001) (0.001)
CEO big data orientation squared × Environmental investment −0.0003** −0.0002**
(0.000) (0.000)
Control variables
Long-term assets (Ln)0.672***0.692***0.693***0.672***0.697***0.698***
(0.011) (0.012) (0.012) (0.011) (0.012) (0.012)
Liquidity0.849***0.901***0.900***0.849***0.887***0.886***
(0.108) (0.107) (0.107) (0.108) (0.107) (0.107)
Fixed asset ratio−1.559***−1.176***−1.173***−1.559***−1.127***−1.128***
(0.128) (0.132) (0.133) (0.128) (0.132) (0.132)
Cash flow−0.001***−0.001**−0.001**−0.001***−0.001**−0.001**
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Hi-tech enterprises0.358***0.335***0.337***0.358***0.339***0.340***
(0.027) (0.027) (0.027) (0.027) (0.027) (0.027)
Slack−18.084 −18.246***−18.241***−18.084 −18.900***−18.920***
(2.783) (2.781) (2.781) (2.783) (2.799) (2.798)
R&D spending0.031***0.026***0.026***0.031***0.026***0.026***
(0.003) (0.003) (0.003) (0.003) (0.003) (0.003)
Team size−0.037 −0.108*−0.107*−0.037 −0.126**−0.127**
(0.043) (0.044) (0.044) (0.043) (0.044) (0.044)
Constant−12.295***−12.758***−12.768***−12.295***−12.850***−12.851***
(0.251) (0.255) (0.255) (0.251) (0.255) (0.256)
Log likelihood−45,076.015 −45,000.215 −44,995.131 −45,076.015 −44,986.812 −44,983.650
Pseudo R squared0.079 0.080 0.080 0.079 0.081 0.081
Number of obs11,746 11,746 11,746 11,746 11,746 11,746
Regression p-value0.000 0.000 0.000 0.000 0.000 0.000
† p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001.
Table 4. Poisson regression predicting technological innovation.
Table 4. Poisson regression predicting technological innovation.
VariablesTechnological Innovation (with Absolute Value of CEO Big Data Orientation)
Model 1 Model 2 Model 3
Independent variable
CEO big data orientation 0.069 ***0.072 ***
(0.000) (0.000)
CEO big data orientation squared −0.001 ***−0.001 ***
(0.000) (0.000)
Environmental investment −0.019 ***−0.018 ***
(0.000) (0.000)
Interaction
CEO big data orientation × Environmental investment 0.003 ***
(0.000)
CEO big data orientation squared × Environmental investment −0.0006 ***
(0.000)
Control variables
Long-term assets (Ln)0.842 ***0.901 ***0.902 ***
(0.001) (0.001) (0.001)
Liquidity1.591 ***1.767 ***1.758 ***
(0.012) (0.012) (0.012)
Fixed asset ratio−1.382 ***−0.457 ***−0.424 ***
(0.016) (0.017) (0.017)
Cash flow0.000 ***−0.001 ***−0.001 ***
(0.000) (0.000) (0.000)
Hi-tech enterprises0.379 ***0.300 ***0.301 ***
(0.003) (0.003) (0.003)
Slack−41.844 ***−40.867 ***−40.861 ***
(0.810) (0.820) (0.820)
R&D spending−0.020 ***−0.012 ***−0.012 ***
(0.000) (0.000) (0.000)
Team size0.169 ***0.042 ***0.045 ***
(0.004) (0.004) (0.004)
Constant−16.673 ***−18.429 ***−18.451 ***
(0.027) (0.028) (0.028)
Fixed YearYes Yes Yes
Log likelihood−466,978.200 −435,708.500 −433,744.060
Pseudo R squared0.519 0.552 0.554
Number of obs11,746.000 11,746.000 11,746.000
Regression p-value0.000 0.000 0.000
*** p < 0.001.
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Wu, W.; Wang, X. Navigating Strategic Balance: CEO Big Data Orientation, Environmental Investment, and Technological Innovation in Chinese Manufacturing. Systems 2024, 12, 255. https://doi.org/10.3390/systems12070255

AMA Style

Wu W, Wang X. Navigating Strategic Balance: CEO Big Data Orientation, Environmental Investment, and Technological Innovation in Chinese Manufacturing. Systems. 2024; 12(7):255. https://doi.org/10.3390/systems12070255

Chicago/Turabian Style

Wu, Weiwei, and Xu Wang. 2024. "Navigating Strategic Balance: CEO Big Data Orientation, Environmental Investment, and Technological Innovation in Chinese Manufacturing" Systems 12, no. 7: 255. https://doi.org/10.3390/systems12070255

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