Analyzing Interrelationships and Prioritizing Performance Indicators in Global Product Development: Application in the Chinese Renewable Energy Sector

Abstract

Today’s global business landscape and intense market competition have heightened the significance of global product development (GPD) practices, making it necessary for organizations to evaluate GPD projects for sustained success. Existing research has primarily focused on independent and tangible performance metrics, neglecting their interdependencies and intangible nature in real-world scenarios. To address this gap, this study seeks to enhance the understanding of GPD performance by establishing a more sophisticated assessment approach. A hybrid decision-making trial and evaluation laboratory (DEMATEL) and analytical network process (ANP) method, known as DANP, was employed to determine the importance of evaluation metrics and their relationships. This research stands out by integrating financial, quality, time, environmental, and capability dimensions, along with their respective indicators, and presenting their interrelationships and priority weights for evaluating GPD projects. The proposed framework was validated through an in-depth case investigation conducted with a Chinese sustainable energy solutions company, involving extensive discussions with decision-makers. The results indicate that technological, innovation, and environmental indicators are the most critical metrics. The influential network relation map (INRM) derived from these findings offers practical recommendations to enhance GPD project performance, catering to policymakers and researchers in both managerial and theoretical contexts.

1. Introduction

Under the tension of globalization and the growing demand for new and sustainable energy resources, GPD companies continue to expand worldwide by distributing their research and development (R&D) activities, product development (PD) teams, and projects overseas [1]. Despite its benefits, GPD practice causes additional pressure on businesses to generate new, competitive, and profitable products. For instance, collaborating with various stakeholders, including internal teams, external suppliers, and global partners under temporal and geographical dispersion, cultural background diversity, and complex technical knowledge makes GPD projects complex, pricey, and risky [2,3,4]. GPD companies must achieve high performance and sustainable efficiency to cope with the inhibitors and meet the high requirements of customers and competitors to have a position in the global market [1]. Therefore, developing an effective evaluation system has become a fundamental necessity [5].

The current study is driven by observing the concerns experienced by managers for monitoring and enhancing performance while operating globally: (i) the dynamicity of the global market, the fast changes in customer requirements, and the severe competition; (ii) the necessity of dealing with shifted collaboration, from managing collocated teams in conventional PD to managing cross-border and culturally dispersed engineering teams; and (iii) the complexity of knowledge and technology transfer among virtual teams that can generate several threats inherent in the product development process. In this regard, various interconnected features and uncontrollable factors make GPD performance assessment more challenging. The interdependencies and interactions among these factors make project performance evaluation complex and multidimensional.

Earlier studies have substantial shortcomings as all diverse metrics are given the same significance level when assessing GPD projects [5,6,7,8]; also, researchers have ignored the interrelationship of performance dimensions and indicators. Disregarding the preference among the different levels and neglecting the nature of interactions among the various performance assessment elements prevent managers from making accurate assessments, making appropriate decisions, and devoting attention to the proper criteria [9]. On the contrary, a clearer vision concerning the interrelationships and the preference between GPD indicators would greatly benefit managers and enable them to develop effective strategies to enhance GPD performance and focus on the causal indicators in the evaluation system to obtain better outcomes [10]. Moreover, understanding different measures and their applicability would support decision making in GPD practice, enabling identifying and avoiding potential risks [5].

This study evaluates GPD performance by identifying and analyzing key performance indicators (KPIs) and their interrelationships. The research recognizes the importance of KPIs as primary sources of information for project evaluation and emphasizes the need to prioritize them. Since the KPIs serve as benchmarks for tracking and monitoring projects’ performance [6], establishing relevant KPIs enables measuring critical aspects of diverse GPD projects that may vary depending on the nature of the product, industry, and project goals [7]. Regularly tracking these KPIs allows for early identification of potential issues or deviations from project objectives, enabling timely corrective actions [5]. Additionally, KPIs play a critical role in decision support during GPD projects by providing objective and quantifiable measures for assessing the progress, performance, and success of GPD projects [5]. Moreover, understanding the relationships between KPIs reduces the number of indicators that need to be monitored [11] by focusing on improving indicators for the cause group to enhance the set of impact KPIs and overall performance, providing valuable guidance for decision making and strategic executive actions [12,13].

Additionally, this enables analysts to validate the accuracy of the indicators and create cause-and-effect diagrams that depict the behavior of performance indicators over time. Developing such a structure would lead to the dynamically better management of a performance measurement system. Consequently, organizations can closely monitor and control the achievement of their intended goals, recognizing that specific goals cannot be accomplished without considering the interaction of other goals.

Previous studies have widely used the DANP approach in different areas of alternative selection, such as new product development (NPD) [14], six sigma projects [15], green manufacturing [16], supplier selection [17], and intermodal railroads [12]; it was also widely used for performance evaluation to prioritize KPIs in various fields such as evaluating business sustainability and banking institutions [18,19], hot spring hotels [20], and manufacturer–supplier collaboration [21]. However, no research used the approach to prioritize KPIs in the GPD field. This research gap highlights the need to investigate the most critical KPIs and their interrelationships in GPD using the DANP approach. Hence, measuring GPD performance by establishing an effective evaluation model considering the preference and the interrelationship among KPIs is necessary. Accordingly, in this research, we take advantage of the DANP technique’s power to address this gap.

Two aspects of this study’s novelty are highlighted: First, the framework has the ability to identify the GPD performance measurement through different dimensions, including financial performance, quality effectiveness, time efficiency, environmental performance, and capability enhancement and their indicators. The proposed framework then uses an inference network relation map to examine how these measurements relate. The interdependence-based priority weight is also presented.

The following questions are investigated in this study:

• What are the essential metrics for advancing GPD performance assessment? What is the interrelationship between these performance indicators?
• How does an indicator influence or be influenced by other indicators in the evaluation network?
• What is the importance of each indicator in the evaluation network?
• What measures would be appropriate for developing a practical approach to assessing and improving GPD performance?

This study aims to answer the aforementioned research questions through the following research goals:

• Selecting the appropriate KPIs for assessing GPD projects.
• Assessing the cause-and-effect influences of each indicator and measuring the strengths of those interdependence relationships and interactions.
• Calculating the priority weight of the indicators and recommending appropriate suggestions to improve GPD performance.

Consequently, this research offers three key contributions: (1) It adopts a more sophisticated perspective than earlier methods to evaluate GPD projects’ success—performance from different perspectives associated with the appropriate KPIs. (2) It extends existing studies by identifying the causal relationships among performance indicators, which are then utilized to generate a network relationship map via the hybrid MCDM approach (DEMATEL-ANP) to further understand the KPIs’ evolution. (3) It addresses the influencing and the influenced KPIs in the performance evaluation system, where using the DANP technique permits the classification of the central indicators and delivery of a feasible orientation for prioritizing a strategic path on the practice level to improve the GPD performance.

The intended objectives are achieved due to DANP, a hybrid MCDM method that integrates the DEMATEL and ANP. The network hierarchy model offered by DANP examines each indicator’s connections and priority weights. For this aim, the DEMATEL technique is applied to determine the mutual connections and to address dependencies among indicators, where their significance and weights are counted using ANP [22,23]. The proposed approach can better reveal the dependent relationships among indicators to reflect real-world situations [22,23].

The remaining sections are arranged as follows: After the relevant literature is reviewed in Section 2, the investigation’s methodology is discussed in Section 3. The findings of the empirical investigation are given in Section 4. Section 5 includes the discussion and implications for academics and managers. The work is concluded in Section 6.

2. Literature Review

GPD is considered a primary intensive strategy for firm growth that involves various stakeholders, including customers, suppliers, designers, and engineers, collaborating to create and introduce innovative products to the global market [2]. Companies pursue GPD through various practices, such as offshoring, partnering, and outsourcing, to leverage the likely benefits of collaborative PD, in addition to cost reduction, access to new markets, acquisition of competencies, and technology innovation overseas [24,25]. The effectiveness and efficiency of development procedures are frequently used to determine the performance of projects involving product development, which can be measured with a balanced set of quantified metrics changing from one firm to another according to firm strategies and objectives [7]. Compared to the manufacturing process, processes in PD are mostly not tangible but cognitive, focusing on intellectual, innovative ways of developing solutions [26].

The existing literature has provided various measures for assessing the performance of PD projects. Ref. [26] reported that KPIs are highly dependent on the firm targets and summarized KPIs into three different groups, effectiveness (e.g., economic efficiency, innovative product idea), efficiency (e.g., high quality, short processing time), and capabilities (e.g., skilled employees and organization). Ref. [27] provided a review of studies concerning collaborative product development (CPD) performance and its applications from three perspectives (dynamics, partnership formation, and infrastructure). Ref. [28] highlighted the tendency to record performance from various dimensions in PD projects. Ref. [29] developed a balanced scorecard model that considers financial, customer, internal, and growth aspects. Ref. [7] analyzed the main challenges in R&D performance analysis.

Some PD performance evaluation frameworks rely on project outcomes or even on the factors that influence these outcomes. For instance, Ref. [29] categorized KPIs as lagging KPIs to measure the outcomes of past activities and as leading KPIs to measure the factors influencing these outcomes, stating that contrary to lagging KPIs, leading indicators are developed to design a comprehensive model of the potential future state. Development time, quality performance, and product cost are crucial evaluation criteria for collaborative development [30].

Even though GPD practice is a substantial topic, few contributions to evaluating GPD project performance were proposed. For instance, Ref. [31] presented an empirical study of Danish firms conducted through surveys and interviews; a total of 17 KPIs were proposed, with the inclusion of “communication capability” as a new KPI denoting the external collaborator’s ability to communicate. Afterward, Ref. [6] proposed a framework to support selecting leading and lagging indicators based on the motivations and difficulties of the GPD project while addressing the success of the global new PD. Ref. [8] used only three benchmarks to evaluate the GPD performance: timing, opportunities, and financial outcomes. A more recent study [5] investigated the development and implementation of KPIs to support the avoidance of project risks by developing preventive measures; a framework consisting of cost and time development, product quality, and other KPIs related to GPD was developed and validated through three stages: key concepts, critical influence factors, and KPI development and documentation, where the aim of each stage was understanding, commitment, and KPI development.

A compilation of multiple studies investigating various aspects of GPD assessment was summarized. These studies encompass conventional measurements such as process efficiency, innovation, time to market, customer satisfaction, and cost management. Additionally, some studies have investigated the concept of “windows of opportunities”, which examines the extent to which GPD programs open new markets, products, and technological areas [8]. Another aspect explored is the ability to adapt production volume and product offers to meet changing market demands, referred to as “flexibility” [2]. Further, the studies outline different GPD practices (e.g., offshoring, outsourcing, global NPD, collaborative PD), related practices (e.g., multinational cooperation), and PD practice evaluation that is necessary to evaluate GPD performance and provide a comprehensive overview of the research landscape, as shown in

Table 1. Summary of GPD performance evaluation in the literature.

Study Title	Dimensions	No. of KPIs	Source
Performance of Global New Product Development Programs: A Resource-Based View	Financial, windows of opportunity	6	[32]
Global engineering networks: drivers, evolution, performance, and key pattern	Effectiveness, efficiency	9	[33]
Success in Global New Product Development: Impact of Strategy and the Behavioral Environment of the Firm	Windows of opportunity, time to market, financial outcomes	8	[8]
You Learn from What You Measure: Financial and Non-financial Performance Measures in Multinational Companies	Financial, customer, people, internal processes	35	[34]
Using a hybrid MCDM methodology to identify Critical factors in new product development	Financial, non-financial	14	[14]
The differences between successful and unsuccessful new manufacturing products in international markets	Export performance	4	[35]
Firm-Level IT Outsourcing Decision Making: A Balanced Scorecard-Based Analytic Network Process Model	Customer, financial, internal operations, learning and growth	17	[9]
Non-financial performance measures and the BSC of multinational companies with multi-cultural environment an empirical investigation	Financial, non-financial	19	[36]
Process-related key performance indicators for measuring sustainability performance of eco-design implementation into product development	Economic, social, environmental	22	[37]
Global product development projects: measuring performance and monitoring the risks	Development cost, time, product quality, others	12	[5]
Comparing offshoring and backshoring: The role of manufacturing site location factors and their impact on post-relocation performance	Cost, quality, delivery, flexibility	9	[2]
Product architecture, global operations networks, and operational performance: an exploratory study	Quality, delivery, flexibility, Cost	7	[38]
A comprehensive KPI network for the performance measurement and management in global production networks	Quality, efficiency, time, flexibility	11	[10]

.

3. Research Methodology

In this section, we present the research methodology used in this study, designed to yield valid and reliable results and fulfill the study’s objectives. That includes providing an overview of the study framework and detailing the methodology for analysis.

3.1. Proposed Framework for Evaluating GPD Performance

The framework used in this study to analyze and prioritize performance indicators in the GPD context is depicted in Figure 1. This comprehensive framework consists of two distinct phases. The initial phase is dedicated to selecting the appropriate metrics from the relevant literature and evaluation team opinion to establish a finalized list of dimensions and indicators for GPD project assessment. The second phase involves the analysis and prioritization of the selected KPIs. A combination of methodologies was employed to examine the interrelationships among the selected KPIs and determine their relative importance.

This study employed the DEMATEL method to construct the network relation map, illustrating the interconnections among key performance indicators (KPIs) in the GPD evaluation system to capture the complex relationships within the system. Subsequently, the DANP technique integrated the DEMATEL and ANP methods to originate the final priority weights for each of the KPIs, considering their interdependencies and relative importance. This comprehensive approach enabled a more accurate prioritization of the KPIs. The analysis results offer managerial implications and recommendations, providing actionable insights for decision-makers and practitioners in the GPD field.

3.2. Using the DANP Approach for GPD

The DANP technique was developed by combining the graph-theory-based decision-making trial and evaluation laboratory (DEMATEL) and analytic network process (ANP) method meant for solving complicated issues, enhancing comprehension of multi-criteria decision problems, and establishing realistic solutions by restructuring problems using a hierarchical structure within a multi-factor interlinked system [39].

3.2.1. Building the Influential Network Relation Map (INRM) via DEMATEL

As a practical structural modeling tool, the DEMATEL approach gathers experts’ knowledge and establishes a structure model that helps the decision-maker distinguish the indicators with higher impact [40]. In comparison, the DEMATEL method has advantages over other methods through interactive relationships among specific items that identify complicated systems (e.g., risk criteria, KPIs, success/failure factors) in several areas without the need for large datasets or pre-hypothesis verification compared to traditional and statistical approaches (e.g., SEM) [18]. Further, compared to causal analysis tools such as interpretive structural modeling (ISM) that consider only causal relationships amongst items, DEMATEL permits us to investigate the cause-and-effect interactions and elucidate the strength of the meaningful relationships. Hence, the DEMATEL approach helps determine the critical items in the network (e.g., KPIs) by analyzing both causes/effects and their influence strength. Using the DEMATEL, we built a network relation map with the following steps:

Step 1: Direct-influence matrix computation.

Based on the experts’ feedback, we constructed the direct-influence matrix $A$. In this step, $H$ interviewees were requested to rate the impact of $n$ indicators/criteria on a scale of 0–4, where 0 = no influence, 1 = low influence, 2 = medium influence, 3 = high influence, and 4 = very high influence. According to responses, the $n\times n$ direct-influence matrix $A$ for all the respondents’ feedback is obtained by calculating the average of each non-negative matrix $K^h={\left[K_{ij}^h\right]}_{n\times n}$, where $h=1,2,...,H$ and $K^1,...,K^h,...,K^H,$ depicting the matrix of ratings given by $H$ experts based on their expertise. The direct-influence matrix $A$ represents the average matrix, as shown in Equation (1).

$$A={\left[{\mathrm{a}}_{ij}\right]}_{n\times n}=\left[\begin{array}{ccccc}{a}_{11}& \cdots & a_{1j}& \cdots & a_{1n}\\ ⋮& & ⋮& & ⋮\\ a_{i1}& \cdots & \boxed{a_{ij}}& \cdots & a_{in}\\ ⋮& & ⋮& & ⋮\\ a_{n1}& \cdots & a_{nj}& \cdots & a_{nn}\end{array}\right]\to a_{ij}=\frac{1}{H}{\displaystyle {\sum }_{h=1}^H{K}_{ij}^h}$$

(1)

where the average $H$ expert score $a_{ij}$ refers to the extent of influence of indicator/criterion $i$ on indicator/criterion $j$, and the diagonal of matrix $A$ is “0”.

Step 2: Total-influence matrix $T$ calculation.

By normalizing the direct-influence matrix $A$, we can calculate the initial influence matrix $X$ following Equations (2) and (3).

$$s=\min \left\{\frac{1}{\underset{i}{\max }{\displaystyle {\sum }_{j=1}^n{a}_{ij}}},\frac{1}{\underset{j}{\max }{\displaystyle {\sum }_{i=1}^n{a}_{ij}}}\right\}$$

(2)

$$X=s\times A$$

(3)

After that, Equation (4) was used to calculate the total-influence matrix $T$, where the $n\times n$ identity matrix is symbolized by $\text{'}I\text{'}$, as follows:

$$\begin{array}{ll}T& ={{\displaystyle \left[t_{ij}\right]}}_{n\times n},i,j=1,2,...,n\hfill \\ T& =X+X^2+...+X^q\hfill \\ & =X(I+X+X^2+...+X^{q-1})\hfill \\ & =X(I+X+X^2+...+X^{q-1})(I-X){(I-X)}^{-1}\hfill \\ & =X{(I-X)}^{-1},\underset{q\to \infty }{\lim }{X}^q={[0]}_{n\times n}\hfill \end{array}$$

(4)

Step 3: Develop the cause-effect influence among the criteria.

In this step, we compute the matrix $T$ rows’ and columns’ sums using Equations (5) and (6) to set up the cause/effect links between the criteria, denoted by $r$ and $c$, respectively.

$$r={\left(r_i\right)}_{n\times 1}={\left[{\displaystyle \sum _{j=1}^n{{\displaystyle t}}_{ij}}\right]}_{n\times 1}={\left(r_1,...,r_i,...,r_n\right)}^{\prime }$$

(5)

$$c={\left(c_j\right)}_{n\times 1}={\left(c_j\right)}^{\prime }{}_{1\times n}={\left[{\displaystyle \sum _{i=1}^n{{\displaystyle t}}_{ij}}\right]}^{\prime }{}_{1\times n}={\left(c_1,...,c_i,...,c_n\right)}^{\prime }$$

(6)

The sum $r_i$ of the i-th row elements indicates the degree to which the criterion $i$ affects other criteria, and the sum $c_j$ of the j-th column elements indicates the degree to which the criterion $j$ is affected by others.

Furthermore, we calculate the sum $r_i+c_i$ for each criterion to evaluate the total effects on the i-th criterion, illustrating the total intensity of the effects provided and received. The net effect value $(r_i-c_i)$ represents the i-th criterion’s influence on the system. If the $(r_i-c_i)$ value in a network is positive, the i-th criterion falls under the cause category (influencing the other criteria). On the other hand, if the $(r_i-c_i)$ value is negative, the i-th criterion falls under the effect category (affected by other criteria) [41].

3.2.2. Measuring the DANP Weights by Integrating the DEMATEL and ANP

The ANP is one of the most sophisticated MCDM approaches. In contrast to the analytic hierarchy process (AHP) technique which considers that the elements are disconnected, the ANP approach supports modeling interdependencies and feedback amongst items in the network [41]. However, the traditional ANP method has many limitations, such as the complexity of the ANP questionnaire and the association of equal weight to each criterion which does not give accurate feedback for real-life situations and issues [23]. Compared to the traditional ANP, the DANP technique adopts a multiple-influence matrix rather than the standard pairwise comparison matrix used in ANP to assess the significance of each criterion [22]. So, in this study, we applied DANP to highlight the key influencing indicators for measuring GPD performance using DEMATEL’s output as DANP’s input. The final weights can be determined by taking the following steps:

Step4: Formation of the unweighted supermatrix $W$.

The total-influence matrix $T$, including the $m\times m$ dimension-based matrix $T_D$ and the $n\times n$ indicator-based matrix $T_C$, was determined using DEMATEL and experts’ opinions. Matrix $T$ was then used as input into DANP to calculate the normalized total-influence matrix $T_D^{\alpha }$ and $T_C^{\alpha }$ through the normalization of $T_D$ and $T_C$ that can be derived by Equations (7)–(11).

$$\begin{array}{ll}{T}_D^{\alpha }& =\left[\begin{array}{ccccc}{{\displaystyle t}}_D^{11}/d_1& \cdots & {{\displaystyle t}}_D^{1j}/d_1& \cdots & {{\displaystyle t}}_D^{1m}/d_1\\ ⋮& & ⋮& & ⋮\\ {{\displaystyle t}}_D^{i1}/d_i& \cdots & {{\displaystyle t}}_D^{ij}/d_i& \cdots & {{\displaystyle t}}_D^{im}/d_i\\ ⋮& & ⋮& & ⋮\\ {{\displaystyle t}}_D^{m1}/d_m& \cdots & {{\displaystyle t}}_D^{mj}/d_m& \cdots & {{\displaystyle t}}_D^{mm}/d_m\end{array}\right]\hfill \\ & =\left[\begin{array}{ccccc}{T}_D{}^{\alpha 11}& \cdots & T_D{}^{\alpha 1j}& \cdots & T_D{}^{\alpha 1m}\\ ⋮& & ⋮& & ⋮\\ T_D{}^{\alpha i1}& \cdots & T_D{}^{\alpha ij}& \cdots & T_D{}^{\alpha im}\\ ⋮& & ⋮& & ⋮\\ T_D{}^{\alpha m1}& \cdots & T_D{}^{\alpha mj}& \cdots & T_D{}^{\alpha mm}\end{array}\right]\hfill \end{array}$$

(7)

where,

$$d_i={\displaystyle \sum _{j=1}^m{t}_D^{ij}},i=1,2,...,m$$

(8)

$$T_C^{\alpha }=\begin{array}{c}\begin{array}{c}{D}_1\\ \\ \\ D_2\\ \\ \\ ⋮\\ \\ \\ D_n\end{array}\end{array}\begin{array}{c}{c}_{11}\\ c_{12}\\ ⋮\\ c_{1m_1}\\ c_{21}\\ c_{22}\\ ⋮\\ c_{2m_2}\\ \\ ⋮\\ c_{n1}\\ c_{n2}\\ ⋮\\ c_{n{m}_n}\end{array}\stackrel{\underset{\begin{array}{cccccccccccccc}{C}_{11}& C_{12}& ...& C_{21}& ...& & C_{2m_2}& & & C1n& & C_{n{m}_n}& & \end{array}}{{\displaystyle \begin{array}{cccccc}{D}_1& & D_2& \cdots & D_n& \end{array}}}}{\left[\begin{array}{ccccc}{T}_C^{\alpha 11}& \cdots & T_C^{\alpha 1j}& \cdots & T_C^{\alpha 1n}\\ ⋮& & ⋮& & ⋮\\ T_C^{\alpha i1}& \cdots & T_C^{\alpha ij}& \cdots & T_C^{\alpha in}\\ ⋮& & ⋮& & ⋮\\ T_C^{\alpha n1}& \cdots & T_C^{\alpha nj}& \cdots & T_C^{\alpha nn}\end{array}\right]}$$

(9)

As indicated in Equation (9), $T_C^{\alpha ij}$ is the $m_i\times m_j$ normalized sub-matrix of $T_C^{\alpha }$. In order to demonstrate the normalization process, we use the example of the sub-matrix $T_C^{\alpha 13}$, as shown in Equation (10).

$$T_C^{\alpha 13}=\begin{array}{c}{C}_{11}\\ \\ ⋮\\ C_{1i}\\ \\ ⋮\\ \\ C_{1m_1}\end{array}\stackrel{\begin{array}{cccccccccc}{C}_{31}& & \cdots & & C_{3j}& & \cdots & & C_{3m_3}& \end{array}}{\left[\stackrel{}{{\displaystyle \begin{array}{ccccc}{t}_{11}^{13}/t_1^{13}& \cdots & t_{1j}^{13}/t_1^{13}& \cdots & t_{1m_3}^{13}/t_1^{13}\\ ⋮& & ⋮& & ⋮\\ t_{i1}^{13}/t_i^{13}& \cdots & t_{ij}^{13}/t_i^{13}& \cdots & t_{i{m}_3}^{13}/t_i^{13}\\ ⋮& & ⋮& & ⋮\\ t_{m_{1}1}^{13}/t_{m_1}^{13}& \cdots & t_{m_{1}j}^{13}/t_{m_1}^{13}& \cdots & t_{m_1{m}_3}^{13}/t_{m_1}^{13}\end{array}}}\right]}$$

(10)

where,

$$t_i^{13}={\displaystyle {\sum }_{j=1}^{m3}{t}_C^{13}},i=1,2,...,m_1$$

(11)

Step 5: Construct the weighted supermatrix $W^{\alpha }$.

We first derive the unweighted supermatrix $W$ using Equation (12).

$$W=(T_C^{\alpha }{)}^{\prime }=\left[\begin{array}{ccccc}{w}^{11}& ...& w^{i1}& ...& w^{n1}\\ ⋮& & ⋮& & ⋮\\ w^{1j}& ...& w^{ij}& ...& w^{nj}\\ ⋮& & ⋮& & ⋮\\ w^{1n}& ...& w^{in}& ...& w^{nn}\end{array}\right]$$

(12)

Then, Equation (13) is used to derive the weighted matrix $W^{\alpha }$ by adopting $T_D^{\alpha }$ and the unweighted matrix $W$.

$$W^{\alpha }=T_D^{\alpha }\times W=\left[\begin{array}{ccccc}{t}_D^{\alpha 11}\times {{\displaystyle w}}^{11}& \cdots & t_D^{\alpha i1}\times {{\displaystyle w}}^{i1}& \cdots & t_D^{\alpha n1}\times {{\displaystyle w}}^{n1}\\ ⋮& & ⋮& & ⋮\\ t_D^{\alpha 1j}\times {{\displaystyle w}}^{1j}& \cdots & t_D^{\alpha ij}\times {{\displaystyle w}}^{ij}& \cdots & t_D^{\alpha nj}\times {{\displaystyle w}}^{nj}\\ ⋮& & ⋮& & \\ t_D^{\alpha 1n}\times {{\displaystyle w}}^{1n}& \cdots & t_D^{\alpha in}\times {{\displaystyle w}}^{in}& \cdots & t_D^{\alpha nn}\times {{\displaystyle w}}^{nn}\end{array}\right]$$

(13)

Step 6: Limit the weighted supermatrix.

The limited supermatrix provides the weight of each criterion obtained by raising the power of the weighted supermatrix $W^{\alpha }$ until it converges and converts into a stable matrix $W^{\ast }$, as shown in Equation (14).

$$W^{\ast }=\underset{g\to \infty }{\lim }{(W^{\alpha })}^g$$

(14)

4. Application and Results of the Framework

4.1. Phase I: Identifying KPIs of GPD Projects

4.1.1. Evaluation of the Potential KPIs

A performance evaluation team was formed to gain insights into critical areas for assessing the success of GPD projects. The team comprised twelve experts with over ten years of experience. This diverse team consisted of five project managers qualified in leading and delivering complex overseas projects and two R&D senior engineers who provided evaluation insights into innovation prospects and technological feasibility from five GPD companies specializing in sustainable and green product development. In addition, three professors contributed theoretical foundations and research-based perspectives for the assessment. To address sustainability concerns, two green supply chain management specialists offered valuable insights into sustainable practices and environmental considerations. The collaboration between industry experts and academics brought practical expertise and research-oriented insights, enhancing the credibility and relevance of the chosen KPIs. Accordingly, a set of thirty-five potential KPIs relevant to GPD projects was collected from the literature, and the most frequently mentioned KPIs from the studies are listed in Table 1. Questionnaire 1 was adopted from the research of Chen et al. [20] and adjusted to include these indicators. The KPIs of GPD performance were identified by asking the evaluation team experts to complete the questionnaire by determining the essential indicators. The responses were evaluated using a scale ranging from 0 to 10, where higher scores indicate greater importance. This scale was divided into five distinct ranges to classify the responses accordingly. The scale was categorized as follows: (0–1.99) very unimportant, (2–3.99) unimportant, (4–5.99) fair, (6–7.99) important, and (8–10) very important, as illustrated in Figure 2.

4.1.2. KPIs’ Selection and Identification

Expert opinions were solicited and synthesized. Considering the assessment of the product development performance, collaborative processes, and virtual teams’ performance, nineteen indicators for GPD project evaluation were selected (with a mean of 7.5 or higher) [20]. The final selected list of KPIs could be grouped under five dimensions, including financial, quality, time, environmental, and capability [5,26,42,43], as shown in

Table 2. GPD project performance indicators.

Dimension	KPIs	Description	Sources
Financial Performance (FP)	Cost of PD (FP1)	The cost incurred in the entire product development process, from conception to launch, including research, design, testing, and marketing.	[1,5,31,32,44]
	Labor cost efficiency (FP2)	The daily performance of each team or individual compared to the benchmarking and GPD site location (changes from one country to another).	[1,2,44,45,46]
	% of sales exported/foreign sales (FP3)	The proportion of a company’s total sales generated from international markets and through exports due to GPD projects.	[31,35,46,47]
	Return on investment (FP4)	The ratio of profit or return generated to the investment made in offshored PD projects indicates the investment’s efficiency.	[5,9,31,36]
Quality Effectiveness (QE)	On-time delivery (QE1)	The percentage of products or services delivered on time, indicating speed to market and reliability of delivery.	[2,36,45]
	Quality of product/output (QE2)	The degree to which the developed products and work outputs meet the global market quality standards, reliability, and robustness.	[2,9,14,34]
	Customer satisfaction (QE3)	The degree to which customers are satisfied with the quality and usability of the globally developed product and the provided services.	[5,9,36]
	% of innovative product ideas (QE4)	The proportion of novelty of the product and the creativity of ideas generated by the virtual teams during the GPD projects.	[7,11,26]
	Strategy-compliant GPD portfolio (QE5)	The degree of congruence of the project with the lined visualization measures how much the GPD project portfolio aligns with the organization’s strategic objectives and goals.	[25,26]
Time Efficiency (TE)	Time to market (TE1)	The time it takes to bring a product to market from when it is first conceptualized to when it is finally launched.	[7,8,34,45]
	Speed for new product development (TE2)	The duration required to develop and introduce “new products” or innovations during GPD projects.	[7,8,31]
	GPD project lead time (TE3)	The amount of time it takes to execute a GPD project from project initiation to completion successfully.	[5,6,32]
Environmental Performance (EP)	Carbon footprint (EP1)	The amount of greenhouse gas emissions, particularly carbon dioxide (CO2), produced throughout the lifecycle of the development projects.	[48]
	Energy consumption (EP2)	The energy resources consumed during various activities involved in developing, producing, and distributing a product on a global project scale.	[48,49,50]
	Environmental regulation compliance (EP3)	The degree to which the GPD project aligns with local and international environmental protocols and standards (e.g., adherence to emissions limits and waste disposal regulations).	[37,49,50]
Capability Enhancement (CE)	New technology acquisition ratio (CE1)	The proportion of novel technological tools and approaches gained from overseas projects and applied to recently developed products.	[8,27,32,46,47]
	Co-learning/global knowledge integration (CE2)	The extent of acquired information and knowledge due to collaboration within GPD projects.	[26,45]
	Employee training and development (CE3)	The number of training and development programs provided to employees involved in GPD projects to nurture their skills and knowledge.	[7,36,45]
	External supplier development (CE4)	The amount of optimal and trustful cooperation established with external parties, which contributes to enhancing delivery and export.	[26,45,51]

.

4.2. Phase II: Analysis of Key Performance Indicators

In this section, we start by introducing the case company to address the aim of this research. Later on, the procedure for gathering data and main findings is provided based on the information collected from experts.

4.2.1. The Case of Chinese Renewable Energy and Sustainable Technology Development

Due to rapid economic growth and development, energy demand has risen dramatically. China’s role in renewable energy development significantly impacts the worldwide economy and environment [51]. China’s investment in renewable energy has the potential to lower greenhouse gas releases and moderate the impact of climate change [51]. Therefore, a multinational leader in sustainable technology and renewable energy development company has been selected to analyze interrelationships and prioritize performance indicators in GPD projects.

The company was carefully chosen based on the following essential criteria to achieve the research purpose: (1) increased investment in establishing offshore R&D facilities and international collaborative product development projects; and (2) high involvement of environmentally friendly practices and incorporating sustainable materials and processes into its products overseas. The company’s name is not stated for confidentiality reasons. Its business covers research and development of energy-saving technologies and sustainable products in Europe, India, and China, including the design, manufacturing, installation, and services of wind power generation equipment and its components. More than 10,000 employees work worldwide with customers to bring sustainable energy solutions. The company has installed more than 113 gigawatts worldwide in over 60 countries and regions. The case company wished to systematically evaluate its performance to continue being effective in its global research, development, and manufacturing. In this regard, a hybrid MCDM model is proposed to assess performance and to clarify the understanding of performance indicators considering each criterion’s significance and weight.

4.2.2. Respondent Selection, Questionnaire Development, and Data Collection

In this phase, results from the first phase were utilized to design a second questionnaire. This questionnaire compared the importance of different measures by obtaining respondents’ opinions through personal interviews and completed surveys. The survey focused on the performance evaluation of GPD projects. It was presented to a total of ten experts with at least ten years of work experience in the case company having bachelor and above degrees, including seven senior managers with considerable experience in carrying out GPD projects, two environmental engineers with substantial expertise and knowledge in the renewable energy arena, and one expert in green R&D collaboration occupying an R&D department head position. All experts agreed to contribute their responses, and then each expert was given an explanation of the evaluation metrics under consideration (given in Table 2). Each interview was structured and systematic to ensure that all the questions were addressed, lasting for about 60–90 min. Based on their experience, experts were asked to provide pairwise assessments by associating each indicator with the degree of influence on the other indicators, following a given 5-point Likert scale, where 0 = completely no influence, 1= low influence, 2 = medium influence, 3 = high influence, and 4 = very High Influence, and the surveys were collected at the end of the interview. Additionally, the ten questionnaire’s consistency gaps were calculated [39].

The average gap ratio in consensus % =$\frac{1}{(n(n-1))}{\displaystyle {\sum }_{(i=1)}^n{\displaystyle {\sum }_{(j=1)}^n\left(\frac{|d_{ij}{}^h-d_{ij}{}^{h-1}|}{d_{ij}{}^h}\right)}}\times 100\%=3.97\%$, where $n$ = 19 and denotes the number of indicators, and $h$= 10 denotes the total number of experts; the average 3.97% (less than 5%) means that the experts’ practical knowledge and significant confidence reach 96.03% (more than 95%).

4.2.3. Determining Interrelationships between KPIs

This section aims to determine the interrelations of the performance dimension and its indicators. The DEMATEL method is applied to the above case to build the impact-relation map with the strength of each criterion’s influence by performing the following steps:

The expert feedback was solicited and synthesized as a pairwise comparison matrix of cause-and-effect relationships; Equation (1) was then used to determine the 19 × 19 direct-influence matrix $A$ by averaging the ten experts’ questionnaire responses illustrated in

Table 3. Direct-influence matrix $A$.

	FP1	FP2	FP3	FP4	QE1	QE2	QE3	QE4	QE5	TE1	TE2	TE3	EP1	EP2	EP3	CE1	CE2	CE3	CE4
FP1	0.000	3.000	2.800	1.600	1.400	1.600	2.400	3.000	1.400	2.000	2.600	3.400	2.800	2.400	2.000	3.400	2.200	1.600	1.800
FP2	1.800	0.000	3.000	2.600	1.600	1.800	2.000	2.200	3.200	3.000	2.000	3.600	3.400	3.200	3.400	2.400	2.800	2.600	1.400
FP3	2.000	2.800	0.000	3.200	1.800	1.600	2.400	2.600	2.200	3.400	0.000	3.800	3.200	3.000	1.600	2.000	1.600	1.000	1.800
FP4	2.600	1.000	1.600	0.000	0.800	1.000	2.400	1.200	2.400	2.800	1.000	2.200	2.000	2.400	0.800	1.200	1.400	1.000	2.200
QE1	1.600	1.000	1.400	2.000	0.000	1.800	3.000	1.600	2.400	2.600	3.200	1.400	1.800	1.000	0.800	0.600	0.000	2.200	2.400
QE2	1.600	2.100	1.600	1.600	1.800	0.000	2.400	1.600	2.000	3.000	2.400	1.800	1.800	2.800	1.000	1.400	1.600	2.400	1.600
QE3	1.200	2.000	2.400	1.400	1.600	2.600	0.000	2.400	2.200	2.400	1.600	2.000	1.800	2.800	2.800	1.600	2.200	1.000	1.400
QE4	1.800	2.200	1.600	2.000	1.600	2.200	3.400	0.000	2.400	3.200	2.400	3.200	1.800	2.800	2.600	1.600	2.400	1.600	2.200
QE5	2.000	2.600	2.200	2.000	2.600	2.400	3.400	2.800	0.000	3.200	1.600	3.400	3.200	3.000	1.600	1.400	2.400	2.800	2.600
TE1	2.400	2.200	1.600	1.000	2.400	2.200	3.200	3.000	2.800	0.000	1.000	2.000	2.000	3.600	1.200	1.000	1.600	2.400	2.800
TE2	2.000	1.800	1.600	3.200	2.800	1.800	3.400	2.800	2.400	2.800	0.000	3.000	2.200	2.600	2.200	2.400	1.400	1.000	2.000
TE3	2.600	2.000	1.800	2.000	1.800	1.200	2.200	2.800	3.200	2.800	2.600	0.000	2.600	3.600	1.400	1.400	1.000	1.400	2.800
EP1	2.300	1.300	1.600	3.400	1.600	2.400	2.200	1.000	2.000	2.000	1.000	1.000	0.000	3.600	1.600	1.600	2.600	2.600	2.000
EP2	1.800	2.400	2.600	3.000	2.600	2.400	3.000	2.600	3.200	3.000	2.400	2.200	2.000	0.000	2.000	2.800	2.400	2.600	2.000
EP3	1.400	2.200	2.400	2.400	2.000	2.400	2.400	3.400	3.200	3.200	2.800	3.400	3.600	3.400	0.000	2.800	2.800	3.200	3.000
CE1	3.200	3.600	3.400	2.400	2.000	2.200	3.000	3.600	2.400	3.000	3.200	3.600	3.600	2.400	2.600	0.000	2.600	2.800	2.800
CE2	2.400	3.000	2.400	2.600	1.600	2.300	2.600	2.000	2.600	2.800	2.600	3.600	3.200	3.400	2.400	2.200	0.000	2.200	1.600
CE3	1.800	2.400	1.800	2.600	3.200	2.800	2.600	2.800	3.200	2.800	3.400	3.400	2.600	2.400	1.800	1.600	1.600	0.000	2.400
CE4	1.600	2.600	1.600	1.800	2.000	2.400	2.600	2.400	2.600	2.200	2.600	1.600	1.000	2.400	2.600	2.600	1.600	1.000	0.000

.

After normalizing the initial influence matrix, $A$, we obtained the normalized influence matrix $X$ from Equations (2) and (3). Next, we used Equation (4) to calculate the indicator-based and the dimension-based total influence matrices, shown in

Table 4. Total-influence matrix $T$.

	FP1	FP2	FP3	FP4	QE1	QE2	QE3	QE4	QE5	TE1	TE2	TE3	EP1	EP2	EP3	CE1	CE2	CE3	CE4
FP1	0.1316	0.1993	0.1867	0.1769	0.1538	0.1640	0.2182	0.2125	0.1917	0.2166	0.1836	0.2342	0.2104	0.2280	0.1629	0.1847	0.1661	0.1578	0.1737
FP2	0.1784	0.1592	0.2035	0.2098	0.1714	0.1820	0.2295	0.2145	0.2410	0.2530	0.1864	0.2551	0.2378	0.2617	0.1986	0.1789	0.1895	0.1901	0.1822
FP3	0.1615	0.1868	0.1279	0.1957	0.1533	0.1566	0.2086	0.1956	0.1972	0.2311	0.1289	0.2297	0.2078	0.2291	0.1473	0.1518	0.1484	0.1409	0.1666
FP4	0.1419	0.1233	0.1269	0.1044	0.1075	0.1160	0.1696	0.1362	0.1624	0.1794	0.1159	0.1631	0.1503	0.1764	0.1051	0.1110	0.1163	0.1108	0.1414
QE1	0.1236	0.1221	0.1222	0.1416	0.0935	0.1309	0.1815	0.1434	0.1628	0.1767	0.1557	0.1488	0.1459	0.1512	0.1051	0.0995	0.0904	0.1314	0.1455
QE2	0.1376	0.1574	0.1409	0.1504	0.1400	0.1117	0.1886	0.1599	0.1736	0.2026	0.1563	0.1746	0.1636	0.2023	0.1220	0.1269	0.1326	0.1498	0.1456
QE3	0.1342	0.1603	0.1594	0.1514	0.1393	0.1629	0.1497	0.1787	0.1821	0.1982	0.1458	0.1840	0.1693	0.2085	0.1577	0.1345	0.1478	0.1298	0.1464
QE4	0.1611	0.1814	0.1618	0.1792	0.1553	0.1724	0.2325	0.1547	0.2062	0.2337	0.1772	0.2256	0.1882	0.2312	0.1699	0.1496	0.1663	0.1552	0.1780
QE5	0.1757	0.2002	0.1833	0.1920	0.1838	0.1876	0.2471	0.2177	0.1763	0.2486	0.1747	0.2427	0.2255	0.2499	0.1625	0.1557	0.1764	0.1872	0.1966
TE1	0.1624	0.1724	0.1531	0.1522	0.1616	0.1645	0.2180	0.1983	0.2021	0.1641	0.1446	0.1930	0.1805	0.2321	0.1374	0.1312	0.1442	0.1613	0.1789
TE2	0.1647	0.1731	0.1612	0.1998	0.1754	0.1643	0.2323	0.2044	0.2051	0.2260	0.1328	0.2208	0.1942	0.2260	0.1617	0.1625	0.1477	0.1438	0.1744
TE3	0.1694	0.1713	0.1591	0.1734	0.1534	0.1487	0.2044	0.1982	0.2122	0.2183	0.1740	0.1597	0.1944	0.2361	0.1430	0.1411	0.1362	0.1457	0.1823
EP1	0.1539	0.1476	0.1451	0.1864	0.1390	0.1592	0.1893	0.1528	0.1778	0.1900	0.1355	0.1653	0.1350	0.2210	0.1355	0.1346	0.1542	0.1572	0.1563
EP2	0.1733	0.1978	0.1914	0.2103	0.1844	0.1878	0.2414	0.2156	0.2344	0.2465	0.1891	0.2241	0.2063	0.2157	0.1699	0.1807	0.1770	0.1843	0.1871
EP3	0.1822	0.2116	0.2036	0.2182	0.1899	0.2045	0.2520	0.2484	0.2548	0.2714	0.2130	0.2650	0.2529	0.2798	0.1483	0.1959	0.1996	0.2107	0.2219
CE1	0.2205	0.2439	0.2289	0.2259	0.1955	0.2073	0.2708	0.2604	0.2490	0.2771	0.2264	0.2786	0.2620	0.2723	0.2032	0.1526	0.2030	0.2094	0.2250
CE2	0.1863	0.2106	0.1906	0.2066	0.1688	0.1879	0.2365	0.2077	0.2271	0.2457	0.1946	0.2512	0.2305	0.2613	0.1792	0.1734	0.1361	0.1799	0.1822
CE3	0.1721	0.1956	0.1754	0.2023	0.1945	0.1937	0.2335	0.2175	0.2335	0.2426	0.2063	0.2425	0.2146	0.2383	0.1650	0.1586	0.1612	0.1358	0.1934
CE4	0.1470	0.1773	0.1516	0.1643	0.1521	0.1651	0.2050	0.1862	0.1961	0.2023	0.1704	0.1848	0.1619	0.2084	0.1604	0.1578	0.1426	0.1349	0.1264

and

Table 5. Dimension-based total-influence matrix.

	FP	QE	TE	EP	CE
FP	0.7401	1.0322	0.9023	0.9604	0.7541
QE	1.1020	1.1644	1.2113	1.2185	1.0811
TE	1.1933	1.3681	0.9824	1.1761	1.0336
EP	1.0036	1.2386	0.9545	0.8844	0.8644
CE	1.0396	1.2690	1.0909	1.1556	0.8086

, respectively.

The cause/effect relationships of the dimensions and indicators were obtained from the total-influence matrix using Equations (5) and (6), including the impact given $r_i$, impact received $c_i$, and the prominence values and their rank based on the influences given and received $(r_i+c_i)$, along with the net effect $(r_i-c_i)$. The dimensions/indicators with greater positive $(r_i-c_i)$ values have a higher impact on the other dimensions/indicators (cause category), and those with lower negative $(r_i-c_i)$ values are significantly influenced by other dimensions/indicators to a higher degree (effect category). The column labeled “Nature” specifies whether the dimension/indicator is classified as an effect or a cause, as displayed in

Table 6. Influences among dimensions and indicators.

Dimension/Indicator	${\mathbf{r}}_{\mathbf{i}}$	${\mathbf{c}}_{\mathbf{i}}$	$({\mathbf{r}}_{\mathbf{i}}+{\mathbf{c}}_{\mathbf{i}})$	Rank	$({\mathbf{r}}_{\mathbf{i}}-{\mathbf{c}}_{\mathbf{i}})$	Nature
FP	4.3891	5.0787	9.4679	5	−0.6896	Effect
FP1	3.5528	3.0772	6.6300	14	0.4756	Cause
FP2	3.9226	3.3912	7.3138	5	0.5314	Cause
FP3	3.3650	3.1729	6.5379	15	0.1921	Cause
FP4	2.5578	3.4407	5.9985	18	−0.8829 2	Effect
QE	5.7773	6.0723	11.8496	1	−0.2950	Effect
QE1	2.5717	3.0127	5.5844	19	−0.4410	Effect
QE2	2.9363	3.1670	6.1033	17	−0.2307	Effect
QE3	3.0401	4.1086	7.1487	9	−1.0685 2	Effect
QE4	3.7025	3.4797	7.1822	7	0.2228	Cause
QE5	3.7836	3.8853	7.6689	2	−0.1017	Effect
TE	5.7535	5.1414	10.8948	2	0.6121	Cause
TE1	3.2519	4.2239	7.4758	3	−0.9720 2	Effect
TE2	3.4703	3.2111	6.6814	13	0.2592	Cause
TE3	3.3209	4.0428	7.3637	4	−0.7219	Effect
EP	4.9455	5.3949	10.3403	3	−0.4494	Effect
EP1	3.0355	3.7312	6.7667	12	−0.6957	Effect
EP2	3.7970	4.3092	8.1062	1	−0.5122	Effect
EP3	4.2235	2.9346	7.1581	8	1.2889 1	Cause
CE	5.3637	4.5418	9.9055	4	0.8218	Cause
CE1	4.4119	2.8811	7.2930	6	1.5308 1	Cause
CE2	3.8561	2.9358	6.7919	11	0.9203 1	Cause
CE3	3.7763	3.0158	6.7921	10	0.7605	Cause
CE4	3.1945	3.3038	6.4983	16	−0.1093	Effect

¹ “main cause-indicator” (highest R−C score): delivering the greatest impact to others. ² “main effect-indicator” (lowest R−C score): obtaining the greatest impact from others.

.

The results in Table 6 indicate that the central dimensions with the highest prominence values are quality effectiveness (11.8496), time efficiency (10.8948), and environmental performance (10.3403). The results show that the significance of dimensions can be prioritized as QE > TE > EP > CE > FP. According to the net effect $(r_i-c_i)$ values, capability enhancement (CE) has the most significant influence among the five dimensions and is the primary dimension influencing the other dimensions, followed by time efficiency (TE). Financial performance (FP), quality effectiveness (QE), and environmental performance (EP) were affected by other dimensions.

The $(r_i+c_i)$ score can be used to rank the top ten indicators as follows: EP2 > QE5 > TE1 > TE3 > FP2 > CE1 > QE4 > EP3 > QE3 > CE3. This ranking indicates that energy consumption (EP2), strategy-compliant GPD portfolio (QE5), time to market (TE1), GPD project lead time (TE3), and labor cost efficiency (FP2) are crucial indicators with the highest prominences values. According to the net effect $(r_i-c_i)$ among the nineteen indicators, nine indicators with positive values are categorized as cause indicators, where CE1, CE2, and EP3 are identified as the main cause-indicators that provide other indications with the strongest influence. Ten other indicators are categorized as effect indicators, including FP4, QE3, and TE1 as the main effect-indicators being most influenced by other indicators.

Based on various dispatched and received influence interactions $(r_i+c_i)$ and $(r_i-c_i)$ derived from the dimension and indicator total-influence matrices T_D and T_C, respectively, we calculated the sum and the difference of impact given $r_i$ and received $c_i$ using Equations (5)–(12); the INRM is represented in Figure 3 and comprises six panels. The horizontal axis of each panel reflects the importance of the total influences among criteria (R + C), while the vertical axis shows the extent to which a dimension/indicator affects (R − C > 0) or is affected by other criteria (R − C < 0). The central panel provides a comprehensive view of the interrelationships among the five dimensions, highlighting their interconnected nature. On the other hand, the remaining five panels showcase the interactions among the indicators within each dimension, illustrating how these indicators mutually influence and are influenced by one another. This representation offers a holistic understanding of the intricate dynamics and dependencies within the INRM framework.

4.2.4. Determining the DANP Weights

Integrating DEMATEL and ANP, DANP priority weight values and rankings were derived by raising the power of the weighted supermatrix using Equations (7)–(14). Furthermore, we calculated the local weight, which refers to the relative weight among criteria. The final DANP weights are shown in

Table 7. Local and global DANP weights of indicators and dimensions.

Dimension/Indicator	Local Weight	Global Weight	Rank
Financial Performance	0.1928		4
Cost of PD	0.2448	0.0451	17
Labor cost efficiency	0.2676	0.0450	18
% of sales exported/foreign sales	0.2516	0.0461	16
Return on investment	0.2713	0.0508	10
Quality Effectiveness	0.2313		1
On-time delivery	0.1989	0.0463	15
Quality of product/output	0.2093	0.0474	14
Customer satisfaction	0.2715	0.0599	5
% of innovative product ideas	0.2438	0.0650	2
Strategy-compliant GPD portfolio	0.2572	0.0488	12
Time Efficiency	0.1967		3
Time to market	0.3274	0.0527	8
Speed for new product development	0.2486	0.0522	9
GPD project lead time	0.3111	0.0442	19
Environmental Performance	0.2054		2
Carbon footprint	0.2751	0.0487	13
Energy consumption	0.3213	0.0567	7
Environmental regulation compliance	0.2186	0.0633	3
Capability Enhancement	0.1738		5
New technology acquisition ratio	0.2532	0.0662	1
Co-learning/global knowledge integration	0.2583	0.0620	4
Employee training and development	0.2652	0.0494	11
External supplier development	0.2911	0.0570	6

.

The data in Table 7 were obtained based on global weight, demonstrating that the quality effectiveness dimension is ranked first with the highest weight (0.2313). Environmental performance (0.2054) was next, followed by time efficiency (0.1967). By dividing the global weight by the dimension weight, the local priority weight for each indicator is calculated. The priority weight calculation determines that the top KPIs are new technology acquisition ratio (0.0662), % of innovative product ideas (0.0650), and environmental regulation compliance (0.0633). These indicators have been prioritized due to their potential impact on organizational performance, competitiveness, and alignment with strategic goals.

4.3. Indicator Classification According to High and Low Cause-and-Effect Performance

The outcomes show that integrating the DEMATEL method into ANP can assist policymakers in understanding the causal relationships among the indicators and their weights. This information allows decision-makers to prioritize indicators based on their level of influence and weight.

In

Table 8. Classification of GPD performance indicators.

#	Category	Weight	Indicators/Priority	Implications
1	Cause	High	QE4, TE2, EP3, CE1, CE2 Priority CE1 > QE4 > EP3 > CE2 > TE2	Indicators in this group are critical. By allocating resources and attention to these indicators, enhancement in these areas can significantly optimize their high causal impact and improve the overall GPD project performance.
2		Low	FP1, FP2, FP3, CE3 Priority CE3 > FP3 > FP1 > FP2	The amelioration of this category by considering aligning these indicators with strategic objectives and exploring ways to enhance their influence is beneficial. Although they may not have the highest priority, addressing these indicators can still have a positive impact.
3	Effect	High	FP4, QE3, TE1, EP2, CE4 Priority QE3 > CE4 > EP2 > TE1 > FP4	This group includes the strongly influenced indicators reflecting the outcomes of the GPD projects. Decision-makers should closely monitor these by enhancing their leading “cause” indicators and measuring them regularly to assess project performance accurately.
4		Low	QE1, QE2, QE5, TE3, EP1 Priority QE5 > EP1 > QE2 > QE1 > TE3	This group of indicators may not be the primary focus, yet it is necessary to consider monitoring their impact and seeking opportunities for improvement.

, group 1 represents the cause, high-weight indicators, and it indicates that new technology acquisition ratio (CE1), % of innovative product ideas (QE4), and environmental regulation compliance (EP3) are the most influential indicators in driving the performance of the GPD projects. Accordingly, decision-makers should assign high consideration and resources to leverage advanced technologies, fostering innovation, and ensuring compliance with ecological rubrics to enhance the overall performance of GPD projects.

Likewise, from group 3 in Table 8, the high-weight effect indicators include customer satisfaction (QE3), external supplier development (CE4), and energy consumption (EP2). Hence, it is crucial to note the underlying causes and factors influencing these indicators to achieve significant growth in GPD projects.

Table 8 sets a strategic priority for enhancing GPD projects’ success by concurrently improving the cause aspects with the effect aspects. Decision-makers can drive positive outcomes and enhance project success.

5. Discussion

The case-based investigation results are presented at aggregated and individual levels, showcasing the central dimensions and their indicators.

5.1. Analysis of General Cause-and-Effect Relationships

Figure 3 presents the influence network relationship map (INRM) generated based on the comprehensive analysis of indicators/dimensions shown in Table 6. The INRM visually illustrates the total influences identified within the network, highlighting the relationships and interdependencies among the indicators/dimensions. Some indicators, referred to as dispatchers (cause category), have positive values of $r_i-c_i$, indicating that they significantly affect the other indicators. On the other hand, there are indicators referred to as receivers (effect category), which have negative values of $r_i-c_i$, indicating that they are strongly affected by the other indicators.

The $r_i+c_i$ value represents the degree of interdependence between each indicator and the others. Indicators exhibiting higher values demonstrate stronger associations, whereas indicators with lower values exhibit weaker relationships with other indicators. When an indicator has a significantly positive value of $r_i-c_i$, it means that it has a more significant influence on other indicators compared to how much it is influenced by them, implying that making improvements ought to be a top priority. The findings obtained through DEMATEL analysis can offer valuable insights for GPD managers to enhance their projects’ performance by focusing on the indicators with the most significant influence on the other indicators [50].

Accordingly, it can be observed from the dimension panels’ vertical axis (R−C) of the influential relationship network maps in Figure 3 that time efficiency (TE) and capability enhancement (CE) fall into the cause category with positive $r_i-c_i$ values. In contrast, the other three dimensions, namely financial performance (FP), quality effectiveness (QE), and environmental performance (EP), fall under the effect category with negative $r_i-c_i$ values. The significance of these relationships reveals that FP, QE, and EP are highly influenced dimensions, whereas the TE and CE dimensions fundamentally influence other dimensions. This finding indicates that time efficiency and capability enhancement play critical roles in shaping the performance of GPD projects across various dimensions. The above finding indicates that the performance of GPD projects can be enhanced by optimizing time and enhancing capabilities that can have far-reaching effects on the success and outcomes of GPD initiatives [1,26].

According to prominence values $r_i+c_i$ from Table 6 illustrated on the horizontal axis (R + C) of Figure 3, quality effectiveness (QE) scored the highest, followed by time efficiency (TE) and environmental performance (EP). Their high prominence indicates these dimensions’ great significance. Therefore, QE should be considered a crucial dimension for improving GPD projects’ performance [52]. TE and EP are the second and third most significant dimensions.

Similarly, $(r_i-c_i)$ values showed that nine indicators have been classified into the cause category, and ten have fallen into the effect category. The cause group ranking is CE1 > EP3 > CE2 > CE3 > FP2 > FP1 > TE2 > FP3. This result shows that new technology acquisition ratio (CE1) is the most causal indicator with the highest effect on other indicators and a significant role in promoting GPD projects. This finding suggests that incorporating new technologies highly affects the effectiveness and success of GPD initiatives. Organizations can enhance their performance and stay competitive in the global market by adopting and integrating innovative technologies into their sustainable product development processes [53,54]. The previous study also supports this assertion, suggesting that considering technological benefits is key to achieving positive outcomes [55].

Similarly, the values of $r_i+c_i$ for energy consumption (EP2), strategy-compliant GPD portfolio (QE5), time to market (TE1), GPD project lead time (TE3), and labor cost efficiency (FP2) are the most crucial indicators with significant degrees of 8.1062, 7.6689, 7.4758, 7.3637, and 7.3138, respectively. The high degrees of importance assigned to these indicators indicate their critical role in evaluating and improving the performance of GPD projects. Organizations should focus on optimizing energy usage, aligning projects with strategic objectives, reducing time to market, streamlining project lead time, and maximizing labor cost efficiency to enhance overall GPD performance. The network map of dimensions and indicators that represents influence relationships is shown in Figure 3. More specific indicators of cause-and-effect analysis are presented in the following subsections.

5.2. Cause-and-Effect Analysis for Dimension-Based Indicators

• Financial performance (FP)

The financial performance panel in Figure 3 highlights the cause-and-effect groups of the FP perspective, with three indicators falling into the cause group and one indicator falling into the effect group. The cause group indicators are labor cost efficiency (FP2), cost of PD (FP1), and % of sales exported/foreign sales (EP3). The one effect group indicator is return on investment (EP4).

These results suggest that improving labor cost efficiency, reducing the cost of product development, and increasing the percentage of sales exported can contribute to a higher return on investment. These findings emphasize the importance of effectively managing cost (primarily labor cost), which is viewed as a critical driver for offshoring [2], controlling PD expenses, and expanding sales to foreign markets [35] to enable enhancing financial performance and profitability. Organizations should optimize these cause group indicators to maximize their return on investment in GPD initiatives.

In the prominence analysis of the above indicators, labor cost efficiency (FP2) is the most critical indicator from the financial perspective.

• Quality Effectiveness (QE)

In the QE perspective, % of innovative product ideas (QE4) is a causal indicator, while on-time delivery (QE1), quality of product/output (QE2), customer satisfaction (QE3), and strategy-compliant GPD portfolio (QE5) are categorized as effect indicators (as shown in Table 6). This classification indicates that the proportion of innovative product ideas has a causal influence on the other four indicators in the effect group. The generation of innovative product ideas can drive efficient project execution, enhance product quality, increase customer satisfaction, and contribute to the development of a strategy-compliant GPD portfolio [7,56].

Organizations can generate novel ideas and concepts for product development by prioritizing and fostering a culture of innovation. In turn, this can lead to improving on-time delivery, higher product value, increased customer satisfaction, and developing a GPD portfolio that aligns with the organization’s strategic objectives. These findings are supported by previous studies that confirm the significance of nurturing and promoting a culture of innovation within GPD projects [57,58]. By encouraging and supporting the generation of innovative product ideas, organizations can enhance their overall efficiency, quality, customer satisfaction, and strategic alignment in the PD process.

According to prominence values of the QE indicators, strategy-compliant GPD portfolio (QE5), % of innovative product ideas (QE4), and customer satisfaction (QE3) have the highest values: 7.6689, 7.1822, and 7.1487, respectively. The high prominence values assigned to these indicators indicate their significant impact on the overall quality and effectiveness of GPD projects. These results suggest that improving these indicators is crucial for enhancing the GPD projects’ implementation, which would lead to better project outcomes, increased market success, and improved performance.

• Time Efficiency (TE)

In the dimension of time efficiency, the speed for new product development (TE2) is identified as the leading cause indicator, while time to market (TE1) and GPD project lead time (TE3) are categorized as effect indicators. This causal analysis shows that TE2 influences both TE1 and TE3. The causal analysis suggests that by improving the speed of new product development, organizations can positively impact both time to market and GPD project lead time. By streamlining and accelerating the product development process, organizations can bring products to market faster and complete GPD projects more efficiently [2,52].

The prominence analysis of the above indicators shows that the time to market is the most critical indicator of time efficiency.

• Environmental performance (EP)

Concerning the environmental performance dimension (refer to Table 6 and Figure 3), the analysis of the interrelationships reveals that environmental regulation compliance (EP3) is identified as the cause indicator, while carbon footprint (EP1) and energy consumption (EP2) are categorized as effect indicators. This result implies that organizations can positively impact their GPD projects’ carbon emissions and energy consumption by ensuring compliance with environmental guidelines. Further, these findings indicate that organizations that prioritize and comply with environmental regulations can reduce their carbon footprint and achieve better returns on energy usage [50,59]. Compliance with regulations ensures that organizations follow sustainable practices and minimize their environmental impact. Reducing carbon footprint and improving energy efficiency align with environmental goals and have potential economic benefits. Organizations that effectively manage their energy consumption and comply with regulations can reduce costs, enhance operational efficiency, and demonstrate their commitment to sustainability [42,50].

Based on the prominence analysis of EP indicators, energy consumption has the highest significance value of 8.1062.

• Capability Enhancement (CE)

Regarding the capability enhancement dimension, the analysis of the interrelationships reveals that the indicators of new technology acquisition ratio (CE1), co-learning/global knowledge integration (CE2), and employee training and development (CE3) are categorized as cause indicators. In contrast, external supplier development (CE4) falls into the effect category.

The analysis highlights the importance of enhancing capabilities in new technology acquisition, co-learning/global knowledge integration, and employee training and development. On the other hand, by focusing on these cause indicators, organizations can foster productive supplier relationships and enhance overall capability and performance in their global product development endeavors. These capabilities enable organizations to collaborate more effectively with their suppliers, share knowledge and expertise, and jointly improve performance and outcomes [60,61].

From the prominence analysis, CE1 and CE3, with values of 7.2930 and 6.7919, respectively, are the most significant indicators for improving capabilities.

5.3. Prioritization of Indicators Based on DANP

GPD performance evaluation is challenging due to the involvement of various distributed teams, external suppliers, and global partners. The analysis of KPIs cannot be adequately explained by a single model. Therefore, the DANP method was used to address the DEMATEL approach’s shortcomings in calculating inter-dimensional interactions and priority weight.

The priority weights for each of the dimensions and their associated indicators are displayed in Table 7. Each indicator’s local priority weight is computed by dividing the global weight by the dimension weight. Based on the calculation of the priority weight, new technology acquisition ratio (CE1), % of innovative product ideas (QE4), environmental regulation compliance (EP3), co-learning/global knowledge integration (CE2), and customer satisfaction (QE3) are the five top-ranked KPIs for GPD project evaluation for the case of renewable energy development. This study can be used as a standard by policymakers to make appropriate policy adjustments to boost GPD performance due to the priority weight of the different dimensions and indicators.

Further information for establishing strategies for enhancement is provided in Table 8. It presents the required and typical actions related to the indicators and concludes that QE4, TE2, EP3, CE1, and CE2 are the most vital causal indicators to address to enhance GPD projects’ performance.

5.4. Research Implications and Future Research Directions

Managers dealing with distributed projects face challenges in sustainable management due to their multidimensional nature and limited collaboration opportunities. Prioritizing their focus on highly influential key performance indicators (KPIs) should be addressed primarily, while less influential ones require less attention. Policymakers can utilize the proposed DANP evaluation framework to assess GPD promotion effectively and develop practical improvement plans for connected indicators. By integrating DEMATEL into ANP, causal relationships and the importance of influences among the indicators are determined (Figure 3, Table 6, Table 7 and Table 8), enabling the development of a strategic framework based on expert feedback. The findings provide valuable managerial insights, and the converged supermatrix facilitates insightful improvements. This study’s managerial contributions are as follows:

• Encourage organizations to invest more in capacity strengthening. Based on research findings, managers are encouraged to prioritize capacity enhancement aspects for evaluating GPD projects due to their significant causal impact in increasing knowledge capacity and identifying areas for improvement to achieve sustainable performance. Active pursuit and adoption of new technologies and leverage of joint learning and training programs significantly drive innovation, improve product quality, increase efficiency, and reduce costs, and these advancements can lead to better market positioning, increased customer satisfaction, and higher overall performance in GPD projects.
• Enable investment in environmental management. Managers must focus on environmental aspects, which are not only aligned with environmental objectives but also have potential economic benefits in GPD projects. Besides reducing carbon emissions and improving energy efficiency, proper management and environmental compliance with regulations can reduce costs, enhance operational efficiency, and demonstrate a commitment to sustainability. Accordingly, GPD managers can achieve environmental goals and reap the associated economic benefits by prioritizing compliance and implementing sustainable practices essential to creating a green image in the market for the company.
• Focus on the effect category of KPIs. The cause category KPIs will drive the effect category KPIs. As a result, managers ought to concentrate on cause group KPIs, including time efficiency and capability enhancement, as foundational elements in driving performance improvements across financial, quality, and environmental dimensions. By enhancing these cause indicators, organizations can create a solid foundation for achieving superior outcomes and overall success in their GPD projects.

This paper raises several aspects that warrant additional investigation. Firstly, while the paper focuses on five dimensions, exploring additional perspectives for future research is suggested. Dimensions like operational and social aspects deserve to be considered to provide a more comprehensive evaluation of GPD projects, allowing for a holistic view of GPD performance. Secondly, this study used a sample from experts working in one company with specific goals and characteristics for developing renewable energy products and technologies, which can influence the understanding of GPD practice. However, to enhance the robustness and applicability of this study’s findings for GPD practice, it is crucial to conduct replications in various fields, compare results across different contexts, and include a broader sample of participants from multiple organizations. Such efforts would contribute to a more comprehensive understanding of GPD and its implications across businesses and environments. Lastly, it is essential to recognize that various firms possess distinct objectives and are influenced by different factors that impact their performance of GPD, such as adopting a distinct practice of GPD (e.g., offshoring, outsourcing, or a combination of both), the nature of products involved (e.g., software, hardware), the size and age of the firms, and the number of countries in which GPD is distributed, which warrant further exploration. The findings of this research can serve as valuable seeds for advancing the practice of GPD, paving the way for future developments in this field.

6. Conclusions

Global product development practices are essential to persisting in the fiercely competitive market. This study emphasized the importance of evaluating the performance of GPD projects to enhance overseas operations. The paper introduces a novel approach to address a pressing GPD practice challenge by comprehensively evaluating GPD projects’ performance. We suggest a framework that enables managers to improve GPD performance measurement more effectively by utilizing a hybrid MCDM model to build a comprehensive performance evaluation system and measure the relationship between the items, including five dimensions and nineteen indicators selected according to a literature examination and experts’ opinions. A thorough case investigation was conducted with a leading Chinese sustainable energy solutions company to test and validate the proposed framework. This investigation involved in-depth discussions with the company’s decision-makers.

Thereon, we built the influential network relation map (INRM) based on the DEMATEL analysis. The INRM enabled the interpretation and helped to clarify the causal interrelationships among the financial, quality, time, environmental, and capability dimensions and their indicators. The DANP analysis determined the total and local influence weight of each dimension and indicator. The findings of this study point out “quality effectiveness” and “environmental performance” as essential dimensions while evaluating GPD projects, which denotes that these aspects play a vital role in the assessment of GPD projects and profoundly impact the overall performance of GPD projects. Accordingly, a high level of quality effectiveness has the potential to positively influence the success and competitiveness of GPD projects, leading to improved customer satisfaction and market acceptance [10] where prioritizing environmental performance is crucial in today’s business landscape, as customers, regulators, and stakeholders increasingly value sustainable and environmentally friendly practices [42,43].

Furthermore, this study identifies indicators that carry significant weight in evaluating GPD performance. These indicators, such as new technology acquisition ratio, % of innovative product ideas, environmental regulation compliance, co-learning/global knowledge integration, and customer satisfaction, greatly influence the performance valuation policy, allowing companies to effectively measure their GPD projects’ progress. Prioritizing these indicators optimizes GPD strategies, facilitates adaptation to market demands, and fosters sustainable growth in GPD.

By designating the importance and interconnections among all indicators, this paper provides valuable insights for evaluating GPD performance from multiple perspectives, aiding informed decision making. Utilizing the DANP technique allows for a deeper examination of the interactive cause-and-effect relationships between key performance indicators. By leveraging this knowledge, managers gain orientation for prioritizing strategic paths at the operational level, enabling them to chart a course that aligns with their firm’s goals and enhances GPD success.