Market Mavericks in Emerging Economies: Redefining Sales Velocity and Profit Surge in Today’s Dynamic Business Environment

Abstract

This research aims to explore market mavericks by redefining sales velocity and profit surge in today’s dynamic business environment in emerging economies. The study focuses on the interplay between Sales Excellence (SE), Sales Capability (SC), Market Alignment (MA), Strategic Responsiveness (SR), and Dynamic Sales Management (DSM). Data from 180 companies (2021–2023), provided by financial leaders, were analyzed using SPSS (23.0) and AMOS (23.0) software. The analysis employed exploratory factor analysis (EFA), reliability analysis, and confirmatory factor analysis (CFA). The results highlight the critical role of these factors in shaping market mavericks and their significant impact on sales and profits in emerging economies. Specifically, SE enhances sales and profits when supported by effective strategies, SC drives organizational change by aligning service quality with SE, and MA drives sales velocity and profit surges through accurate forecasting. SR positively influences sales results by aligning sales with corporate strategy, while DSM is critical for motivating salespeople and shows strong links to SC and SR for successful adaptation in a dynamic business environment. The study reveals the interdependence of these factors and emphasizes the need for seamless integration and coordination to drive effective organizational change. These findings have significant implications for corporations seeking to improve their sales strategies and achieve sustainable growth in a rapidly evolving marketplace in emerging economies. This research explores market mavericks, redefines sales velocity and profit surge, and provides valuable insights into the critical factors shaping market mavericks and their impact on sales and profits. It offers guidance for organizations seeking sustainable growth.

1. Introduction

In today’s dynamic and rapidly evolving business environment, achieving optimal sales velocity and profit surge is a significant challenge for companies. This paper examines the relationship between five crucial factors: Sales Excellence (SE), Sales Capability (SC), Market Alignment (MA), Strategic Responsiveness (SR), and Dynamic Sales Management (DSM). The focus is on how these factors impact market mavericks and their effectiveness in redefining sales velocity and profit surge, particularly in emerging economies. The primary research questions are: how do SE, SC, MA, SR, and DSM affect sales velocity and profit surge in emerging markets, and what is the statistical significance of these factors in shaping sales strategies for market mavericks? This research is motivated by the need for businesses in emerging markets to adapt their sales strategies to the rapidly changing environment. By analyzing these key factors, the study aims to provide actionable insights that can help companies enhance their sales performance and achieve sustained success.

The significance of this study lies in its potential to provide valuable insights for companies operating in emerging markets. For instance, Edwards et al. (2023) highlight how SE, including sales territory management and market analysis, is crucial for sales performance. Singh et al. (2021) and Echchakoui (2016) discuss how customer-oriented salespeople and customer loyalty can impact sales velocity and profit surge. In terms of SC, Koponen et al. (2019) and Park et al. (2023) emphasize the importance of communication, expertise, and creative selling. Xie et al. (2023) point out that detailed information about customer identity and pricing affects profitability, underscoring the role of MA.

Chen et al. (2015) argue that SR, including salespeople’s accountability and alignment with corporate strategy, is essential for promoting new products and redefining sales velocity. This view is supported by Reichstein-Scholz et al. (2021), who stress SR’s importance in a globalized sales environment. Additionally, Datta et al. (2024) and Figueiredo et al. (2023) highlight the need for ongoing improvements in DSM despite its integral role in sales management.

Regarding the maverick market for all factors (SE, SC, MA, SR, and DSM), according to Isherwood and Tassabehji (2016), it is emphasized that innovative companies can use their talents to solve operational problems by making a maverick company successful in the market, and Rothkopf and Pibernik (2016) suggest maverick strategies for companies. In addition, Ho et al. (2014) and Huarng and Yu (2020) emphasize that sales velocity and customer satisfaction have a significant impact on profit growth. According to Yang et al. (2020), surge pricing has long fueled the debate about its advantages and disadvantages in today’s dynamic business environment. Lu et al. (2023) highlight that sales excellence (SE) encompasses aspects such as sales territory management, market analysis, customer satisfaction, sales growth, effective handling of challenging situations, continuous sales improvement, new sales opportunities, and sales support.

The contributions of this study are both theoretical and practical. Theoretically, the study advances the understanding of how SE, SC, MA, SR, and DSM impact sales performance, particularly in emerging markets. It integrates these factors into a cohesive framework, expanding existing theories on sales excellence and market responsiveness. Practically, the research provides actionable insights for business leaders and sales managers, offering recommendations for refining sales strategies, optimizing practices, and adapting to market changes. By addressing these factors, companies can improve their sales management and achieve sustainable financial and managerial success.

The paper is structured as follows: first, a detailed literature review will be presented, followed by a comprehensive methodology section outlining data collection and analysis techniques. The empirical results will then be discussed, culminating in a conclusion that summarizes key insights, implications, and recommendations for future research.

2. Literature Review and Hypotheses Development

This literature review explores how market mavericks redefine sales velocity and profit surges in today’s dynamic business environment. It combines existing research with this study’s variables to offer a comprehensive summary, contributing to hypothesis construction and verification, and provides recommendations on how businesses can adapt to change, redefine sales velocity, and propel towards profit surges in this dynamic milieu. Therefore, according to Cui et al. (2022) on the relationship of factors (SE, SC, MA, SR, and DSM) in market mavericks (Zheng and Pan 2022), through redefining sales velocity and profit surges, consumer reactions to tariffs are highly emphasized, as well as price phenomena, managing the balance between continuity and change (Vilkamo and Keil 2003) in today’s dynamic business environment. With regard to Sales Excellence (SE) and its variables (sales territory management, market analysis, customer satisfaction, effective selling, increasing sales, improving sales skills, identifying new sales opportunities, competitive sales environment, etc.), according to Evangelista and Regis (2019), it is emphasized that the Gaussian model is promising for forecasting company sales. Sharma and Sagar (2023) highlight the key challenges of selling new products in the FMCG sector, which include product innovation, product differentiation, customer perception, and market turbulence. They suggest that focusing organizational efforts on these areas will enable retailers to better address these challenges. Morgan et al. (2024) find that customer involvement has a positive impact on new service development (NSD) performance and that the use of customer relationship management technology also improves NSD performance. In addition, Fergurson et al. (2024) point out that the two-factor measure of customer ownership reveals important aspects of the salesperson customer relationship and provides a method to empirically address customer migration. According to Mullen and Berrill (2015), it is emphasized that international sales are increasing, and the percentage of companies oriented to the local region is decreasing. Kozielski et al. (2017) noted that companies spend millions on training their salespeople. In today’s dynamic business environment, Annunen et al. (2021) stress the importance of improving market analysis, customer satisfaction, and effective sales to achieve higher sales velocity and profit surge.

Regarding Sales Capability (SC) and its variables (clear sales strategy, quality services, sales knowledge of products/services), Sun et al. (2024) emphasize the importance of using sales strategies that consider both company characteristics and customer decisions influenced by costs. Additionally, it is highlighted that the sales strategy should incorporate the opportunities and outcomes that arise from customer service completion by salespeople (Tavakoli et al. 2016) to define sales velocity and profit increase in today’s dynamic business environment. Furthermore, Trentin et al. (2013) emphasize the use of a sales configurator to mitigate the risk of offering an excessive variety of products and customization, which can result in a loss of sales. Wang et al. (2024) emphasize the necessity for companies to publish salary information.

Regarding Market Alignment (MA) and its variables (fulfillment of customer expectations, accurate and reliable sales forecasts, competitive prices in the market), Wacker and Lummus (2002) emphasize that companies can improve their sales forecasts by addressing limitations and gaining a deeper understanding of the managerial side of forecasting from customer expectations. In the context of market mavericks redefining sales velocity and profit surge in today’s dynamic business environment, Xi and Zhang (2023) highlight the importance of companies making informed choices in pricing strategy. Coreynen et al. (2024) emphasize that as companies gradually develop and progress towards the maturity of digital service innovation (DSI), they deal with an increasing degree of complexity, driving their learning needs; therefore, in each cycle of the company, they must unlock opportunities and new challenges of DSI.

Regarding Strategic Responsiveness (SR) and its variables (responsibility of salespeople for customer requests, selling according to business strategy, real selling strategy), Bharadwaj and Shipley (2020) emphasize the significance of effective communication between salespeople and customers for redefining sales velocity and achieving profit surge in today’s dynamic business environment. Edwards et al. (2023) highlight the positive relationship between the creation of an entrepreneurial strategy, corporate readiness, entrepreneurial selling actions (such as creative selling, innovation in selling, and responsiveness to customer requests and questions), and sales performance aligned with the business strategy. Vagtborg (2024) presents a process perspective on the sustainability transition relevant to firms seeking a shift in focus from simple compliance to strategic responsiveness based on adaptability and renewal.

Regarding Dynamic Sales Management (DSM) and its variables (regular research and analysis of sales data, clear sales program, motivation and commitment of salespeople, adaptation of salespeople to changes in market conditions), Corsaro (2022) emphasizes that sales are undergoing an invisible process of change known as sales transformation. Langley and Rieple (2024) point out that the management’s management perceptions should be revised in the dynamic managerial environment of businesses in emerging economies. Moreover, Hou et al. (2024) reveal that consumer honesty significantly affects dynamic decisions. Pereira et al. (2023) highlight the importance of the company determining which demand segments should be improved in the sales program. In today’s dynamic business environment, Zheng et al. (2023) consider sales transformation as an effective management approach to enhance sales performance and increase sales force engagement amidst changing market conditions. The work engagement and motivation of salespeople, as noted by Medhurst and Albrecht (2016), significantly impact sales velocity and profit surge. Lulaj (2021) highlights that large enterprises are competitive over small enterprises in emerging markets. Lulaj and Dragusha (2022) discuss the challenges in business tax payments in developing countries, emphasizing the need for improvements, particularly in light of the COVID-19 pandemic. Furthermore, Dragusha et al. (2023) assert that trade liberalization positively influences economic growth, exports, and imports, driving sales velocity and profit surge in today’s dynamic business environment. The integrated discussions highlight the importance of formulating hypotheses and sub-hypotheses to validate and extend the findings of this study. The collective knowledge gained from various studies by different authors on the factors (SE, SC, MA, SR, and DSM) and their variables strengthens the foundation for hypothesis building. Based on this research, the main hypothesis (H) and its sub-hypotheses (H1–H10) have been developed for the five factors to explore their relationship and evaluate their effect on market mavericks in terms of redefining sales velocity and achieving profit surge in today’s dynamic business environment. These hypotheses aim to provide a new perspective for understanding sales velocity and contribute to the empowerment of businesses through improved sales and increased profits, as elaborated below:

Hypothesis (H). 

There is a statistically significant and positive relationship between the factors of SE (Sales Excellence), SC (Sales Capability), MA (Market Alignment), SR (Strategic Responsiveness), and DSM (Dynamic Sales Management) in the context of market mavericks through redefining sales velocity and profit surge in today’s dynamic business environment.

Figure 1 presents the conceptual model that explores how Sales Excellence (SE), Sales Capability (SC), Market Alignment (MA), Strategic Responsiveness (SR), and Dynamic Sales Management (DSM) are interrelated in the context of market mavericks and climate change. The main hypothesis (H) suggests a statistically significant and positive relationship among these factors. Specifically, the sub-hypotheses (H1–10) propose that SE has a positive effect on SC, MA, SR, and DSM; SC has a positive effect on MA, SR, and DSM; MA has a positive effect on SR and DSM; and SR has a positive effect on DSM. In this context, SE refers to efforts to achieve sales excellence, SC focuses on an organization’s ability to adapt its capabilities to market demands, MA involves aligning sales strategies with market expectations, SR highlights the ability to respond to changes in the business environment, and DSM covers dynamic sales management strategies. This analysis is expected to enhance the understanding of market and sales behavior, aiding in the development of effective strategies and improving business performance in a rapidly evolving environment and climate change.

3. Materials and Methods

3.1. The Purpose of the Paper

The research focuses on market mavericks in emerging economies through redefining sales velocity and profit surge in today’s dynamic business environment between the factors of SE (Sales Excellence), SC (Sales Capability), MA (Market Alignment), SR (Strategic Responsiveness), and DSM (Dynamic Sales Management). Therefore, the aim is to identify and illustrate the correlations between these factors, providing insights and recommendations for effective practices that influence sales and profit surges in today’s dynamic business environment.

3.2. Data Analysis

To assess the significance of the model and validate the hypotheses, we selected SPSS and AMOS software for their robust capabilities in handling complex statistical analyses, which are crucial for our study’s objectives. These tools were chosen because they provide comprehensive methods for exploring and confirming the underlying structures within the data, ensuring accurate and reliable results. The research process unfolded in four steps: first step (s₁), we applied exploratory factor analysis (EFA) to uncover the underlying structure of the data, a method widely recognized for its effectiveness in revealing the dimensionality of complex constructs (Spearman 1927). EFA was necessary to ensure that the constructs in our model were well defined and appropriately measured. Next, in the second step (s₂), we conducted a reliability analysis to confirm the consistency and stability of our measurement instruments, following Floyd and Widaman’s (1995) guidelines on the importance of factor analysis in the evaluation of multi-factor questionnaires. This step was essential to ensure that the data collected were reliable and could be used with confidence in further analyses. In the third step (s₃), confirmatory factor analysis (CFA) was used to validate the model specification using standardized regression coefficients (β) and correlations (r) to predict indicators from latent factors, along with multiple regression analysis, as recommended by Cohen et al. (2003). CFA was critical to confirm that the data fit the proposed model structure. Finally, in the fourth step (s₄), we used covariance, correlation analysis, and model fit indices to test the main hypothesis (H) and its sub-hypotheses (H1–H10), ensuring a comprehensive understanding of the relationships between variables and the reliability of the model’s predictions. This detailed explanation is visually represented in Figure 2.

3.3. Data Collection

The data were collected from 180 companies (manufacturing, service, and commercial) in Kosovo during 2021–2023 using a survey completed by employees in various positions, including workers, financial managers, accountants, directors/owners, internal auditors, and investors/stockholders. The sample was selected to provide a representative overview of different sectors and roles within the business environment in Kosovo, a developing economy. The companies and respondents were selected to explore market challenges and opportunities, particularly in redefining sales velocity and profit growth in today’s dynamic business environment. Participants were informed that their data would be used for research purposes only and would remain confidential to ensure their voluntary and honest participation. The questionnaire was structured using a Likert scale (1—strongly disagree to 5—strongly agree) to accurately capture respondents’ attitudes and perceptions.

Table 1. Clarification and analysis of research variables.

Abbv.	Description	Source
Sales Excellence—SE
SE1	The company has a well-defined sales territory management system	Kaur et al. (2024) Mercer (2024) Lulaj et al. (2024a) Kääriä and Shamsuzzoha (2024) Lulaj et al. (2023) Peesker et al. (2024)
SE2	Sales targets are based on thorough market analysis
SE3	The company conducts regular customer satisfaction surveys
SE4	Salespeople effectively handle challenging sales situations
SE5	Salespeople sell effectively and increase sales
SE6	The company continually invests in the growth and improvement of salespeople’s skills
SE7	Salespeople manage the sales pipeline effectively
SE8	Salespeople proactively identify new business opportunities
SE9	Salespeople understand the competitive environment
SE10	The company provides adequate resources to support the salespeople
Sales Capability—SC
SC1	Sales strategy is clearly defined and achievable	Conde et al. (2024) Wei et al. (2024)
SC2	Salespeople provide quality customer service
SC3	Salespeople are knowledgeable about the company’s products/services
Market Alignment—MA
MA1	Products/services meet customer expectations	Reed (2023) Nansubuga and Kowalkowski (2024) Lulaj and Iseni (2018)
MA2	Sales forecasts are accurate and reliable
MA3	Pricing strategy is competitive in the marketplace
Strategic Responsiveness—SR
SR1	Salespeople are responsive to customer inquiries and requests	Seker (2024) Zhang and Song (2024) Lulaj et al. (2024b)
SR2	Sales efforts are aligned with corporate strategy
SR3	Sales strategy is challenging but realistic
Dynamic Sales Management—DSM
DSM1	The company regularly researches and analyzes sales data	Giovannetti et al. (2024) Rice et al. (2024) Lulaj (2023)
DSM2	The company has a clear sales program
DSM3	Salespeople are motivated and engaged in their work
DSM4	Salespeople adapt to changing market conditions

Note: Abbreviations (Abbv.), Sales Excellence (SE), Sales Capability (SC), Market Alignment (MA), Strategic Responsiveness (SR), Dynamic Sales Management (DSM).

provides a description of the variables, highlighting the impact of factors such as Sales Excellence (SE), Sales Capability (SC), Market Alignment (MA), Strategic Responsiveness (SR), and Dynamic Sales Management (DSM) related to market mavericks and climate change through redefining sales velocity and profit surge in today’s dynamic business environment. Ten variables were analyzed in the SE factor, three variables in SC, three variables in MA, three variables in SR, and four variables in DSM. Each factor and its variables were thoroughly discussed in the introduction and literature review sections, taking into account the contributions of various authors to the research. In the Results and Discussion sections, the findings for each factor are analyzed and compared with the contributions of other authors.

4. Results

As discussed in the Literature Review and Materials and Methods sections, this section presents the results derived from analyzing the five factors—Sales Excellence (SE), Sales Capability (SC), Market Alignment (MA), Strategic Responsiveness (SR), and Dynamic Sales Management (DSM)—as shown below.

Table 2. Descriptive statistics.

Variables	Sub-Variables	Frequency	Percent
Company type	Manufacturing company	64	35.6
	Service company	71	39.4
	Commercial company	45	25.0
Position	Worker	23	12.8
	Manager	31	17.2
	Financial Manager	32	17.8
	Accountant	31	17.2
	Director/Owner	30	16.7
	Internal Auditor	14	7.8
	Investor/Shareholder	19	10.6

Note: Demographic data of the sample (n = 180).

presents the demographic breakdown of the 180 respondents from various companies who participated in the survey on market mavericks and climate change, focusing on redefining sales velocity and profit surge in today’s dynamic business environment. The respondents are categorized by company type and position. Therefore, the sample includes 35.6% from manufacturing companies, 39.4% from service companies, and 25.0% from commercial companies. In terms of positions, 12.8% were workers, 17.2% were managers, 17.8% were financial managers, 17.2% were accountants, 16.7% were directors/owners, 7.8% were internal auditors, and 10.6% were investors/shareholders.

Table 3. Exploratory factor analysis (EFA) and reliability analysis (Cronbach’s Alpha).

Item	Construct	Factor Loading λ	KMO and Bartlett’s Test	Variance Explained (VE) Cronbach’s Alpha	Interpretation
Sales Excellence—SE
SE1	The company has a well-defined sales territory management system	0.682	KMO = 0.880 χ2 = 559.308 df = 45 Sig. = 0.000	43.9% α = 0.851	Kaiser (1970) Kaiser (1974) Cronbach (1951) Cronbach (2004) Valid results
SE2	Sales targets are based on thorough market analysis	0.615
SE3	The company conducts regular customer satisfaction surveys	0.611
SE4	Salespeople effectively handle challenging sales situations	0.672
SE5	Salespeople sell effectively and increase sales	0.714
SE6	The company continually invests in the growth and improvement of salespeople’s skills	0.587
SE7	Salespeople manage the sales pipeline effectively	0.686
SE8	Salespeople proactively identify new business opportunities	0.675
SE9	Salespeople understand the competitive environment	0.627
SE10	The company provides adequate resources to support the salespeople	0.741
Sales Capability—SC
SC1	Sales strategy is clearly defined and achievable	0.803	KMO = 0.686 χ2 = 112.277 df = 3 Sig. = 0.000	65.3% α = 0.734	Valid results
SC2	Salespeople provide quality customer service	0.803
SC3	Salespeople are knowledgeable about the company’s products/services	0.817
Market Alignment—MA
MA1	Products/services meet customer expectations	0.762	KMO = 0.670 χ2 = 136.475 df = 3 Sig. = 0.000	67.4% α = 0.756	Valid results
MA2	Sales forecasts are accurate and reliable	0.859
MA3	Pricing strategy is competitive in the marketplace	0.837
Strategic Responsiveness—SR
SR1	Salespeople are responsive to customer inquiries and requests	0.786	KMO = 0.650 χ2 = 79.154 df = 3 Sig. = 0.000	59.9% α = 0.666	Valid results
SR2	Sales efforts are aligned with corporate strategy	0.806
SR3	Sales strategy is challenging but realistic	0.729
Dynamic Sales Management—DSM
DSM1	The company regularly researches and analyzes sales data	0.699	KMO = 0.733 χ2 = 138.300 df = 6 Sig. = 0.000	54.7% α = 0.772	Valid results
DSM2	The company has a clear sales program	0.700
DSM3	Salespeople are motivated and engaged in their work	0.816
DSM4	Salespeople adapt to changing market conditions	0.738

Source: Table prepared by the authors (2023–24). Note: KMO = Kaiser–Meyer–Olkin, χ² = chi-square, df = degrees of freedom, α = Cronbach’s Alpha, Sales Excellence (SE), Sales Capability (SC), Market Alignment (MA), Strategic Responsiveness (SR), Dynamic Sales Management (DSM), Variance Explained (VE).

presents the results of the Principal Component Analysis (PCA) for five key factors (SE, SC, MA, SR, and DSM). All factors have loadings greater than 0.50, indicating their significance. The KMO test and Bartlett’s Sphericity test support the suitability of the data for factor analysis, with KMO values ranging from 0.650 to 0.880 and a significant Bartlett’s test (Sig. = 0.000). Reliability analysis shows Cronbach’s Alpha values between 0.67 and 0.85, indicating data consistency. Each factor’s Eigenvalue indicates that it explains more than 50% of the variance.

Table 4. Confirmatory factor analysis (CFA).

Observed Variable	Latent Variable	Standardized Regression Weights	Estimate	S.E.	C.R.	p-Value	Asterisk	Confidence Level of 99.9%
SE1	SE	0.634	1.000			-		Statistically Significant
SE2		0.561	0.741	0.116	6.415	p < 0.001	***
SE3		0.555	0.909	0.143	6.357	p < 0.001	***
SE4		0.615	0.894	0.129	6.924	p < 0.001	***
SE5		0.673	0.951	0.128	7.433	p < 0.001	***
SE6		0.533	0.858	0.140	6.143	p < 0.001	***
SE7		0.638	1.079	0.151	7.128	p < 0.001	***
SE8		0.624	0.864	0.123	7.003	p < 0.001	***
SE9		0.584	1.025	0.154	6.634	p < 0.001	***
SE10		0.709	1.036	0.134	7.738	p < 0.001	***
SC1	SC	0.653	1.000			-		Statistically Significant
SC2		0.711	1.062	0.140	7.600	p < 0.001	***
SC3		0.710	1.164	0.153	7.590	p < 0.001	***
MA1	MA	0.638	1.000			-		Statistically Significant
MA2		0.768	1.842	0.251	7.333	p < 0.001	***
MA3		0.753	1.266	0.173	7.297	p < 0.001	***
SR1	SR	0.654	1.000			-		Statistically Significant
SR2		0.677	1.039	0.151	6.870	p < 0.001	***
SR3		0.574	0.800	0.131	6.104	p < 0.001	***
DSM1	DSM	0.585	1.000			-		Statistically Significant
DSM2		0.562	0.953	0.164	5.795	p < 0.001	***
DSM3		0.739	1.241	0.180	6.888	p < 0.001	***
DSM4		0.643	1.106	0.174	6.362	p < 0.001	***

Note: Standard Error (S.E.), Critical Ratio (C.R.), *** p < 0.001 indicates statistical significance. The confidence interval is set at 99.9% (CI), Sales Excellence (SE), Sales Capability (SC), Market Alignment (MA), Strategic Responsiveness (SR), Dynamic Sales Management (DSM), confirmatory factor analysis (CFA). Source: table prepared by the authors (2023–2024).

shows the CFA results for the factors SE, SC, MA, SR, and DSM. All variables show significant effects on their latent variables, with standardized regression weights above 0.5 and p-values below 0.001. Specifically, for SE, SE10 (0.71) and SE5 (0.67) are critical, highlighting the importance of providing resources and effective sales techniques. Then, for SC, SC2 (0.71) and SC3 (0.70) are crucial for ensuring high service quality and thorough product knowledge. MA is best represented by MA2 (0.77), which is essential for accurate sales forecasting. In SR, SR2 (0.68) and SR1 (0.65) are significant, focusing on aligning sales activities with corporate strategy and addressing customer needs. Finally, in DSM, DSM3 (0.74) underscores the importance of salesperson motivation and commitment. The results, supported by a 99.9% confidence interval, underscore the value of strategic resource allocation, effective sales strategies, quality service, accurate forecasting, and motivated salespeople in achieving market success and adapting to dynamic business environments.

Table 5. Covariances and correlations.

Path Variables	Covariances				Correlation	Interpretation
	Estimate	S.E.	C.R.	p-Value	Estimate
SE <--> SC	0.047 **	0.018	2.591	0.010	0.263	Cov(SE, SC, MA, SR, DSM) Cor(SE, SC, MA, SR, DSM) Positive and significant relationship
SE <--> MA	0.057 **	0.019	3.000	0.003	0.310
SE <--> SR	0.067 ***	0.021	3.210	0.001	0.356
SE <--> DSM	0.057 **	0.018	3.145	0.002	0.341
SC <--> MA	0.092 ***	0.021	4.297	***	0.556
SC <--> SR	0.139 ***	0.027	5.244	***	0.828
SC <--> DSM	0.122 ***	0.024	5.023	***	0.824
MA <--> SR	0.094 ***	0.023	4.118	***	0.539
MA <--> DSM	0.081 ***	0.020	4.044	***	0.532
SR <--> DSM	0.112 ***	0.024	4.671	***	0.720

Note: *** p < 0.001, ** p < 0.01, Standard Error (S.E.), Critical Ratio (C.R.), covariance (Cov), correlation (Cor), C. I = 95%. Sales Excellence (SE), Sales Capability (SC), Market Alignment (MA), Strategic Responsiveness (SR), Dynamic Sales Management (DSM), confirmatory factor analysis (CFA). Source: table prepared by the authors (2023–2024).

shows the relationships between the SE, SC, MA, SR, and DSM factors. It includes covariance, correlation, and significance values for each pair of factors. The results indicate significant positive relationships: SE and SC (Cov: 0.047, Cor: 0.263, p < 0.010), SE and MA (Cov: 0.057, Cor: 0.310, p < 0.003), SE and SR (Cov: 0.067, Cor: 0.356, p < 0.001), SE and DSM (Cov: 0.057, Cor: 0.341, p < 0.002), SC and MA (Cov: 0.092, Cor: 0.556, p < 0.000), SC and SR (Cov: 0.139, Cor: 0.828, p < 0.000), SC and DSM (Cov: 0.122, Cor: 0.824, p < 0.000), MA and SR (Cov: 0.094, Cor: 0.539, p < 0.000), MA and DSM (Cov: 0.081, Cor: 0.532, p < 0.000), and SR and DSM (Cov: 0.112, Cor: 0.720, p < 0.000). The most significant relationships were observed between SC and SR (83%), SC and DSM (82%), and SR and DSM (72%). These results highlight the critical role of integrating these factors to adapt to a dynamic business environment, emphasizing the need for a strong focus on sales excellence to redefine sales velocity effectively.

Table 6. Model fit summary.

Model Fit Summary
Tests/Parameters	Default Model	Saturated Model	Independence Model	Test Clarifications and Equations	Threshold Values	Interpretation
CMIN
CMIN (χ2) α = 0.05	264.369	0.000	1610.377	(N − 1) FML, where FML is the value of the statistical criterion (fit function) minimized in ML estimation and (N − 1) Minimum Discrepancy Function divided by Degrees of Freedom (Steiger 1980) ${\mathsf{\chi }}^2-{{\mathsf{\chi }}^{\prime }}^2={\displaystyle \sum _{\mathrm{i}=1}^{\mathrm{k}}}{\displaystyle \frac{{\mathsf{\chi }}_{\mathrm{i}}^2}{{\mathrm{m}}_i}}-{\displaystyle \sum _{\mathrm{i}=1}^{\mathrm{k}}}{\displaystyle \frac{{\mathsf{\chi }}^2}{{\mathrm{m}}_{\mathrm{i}}^{\prime }}}$		-
dfM (X2/df)	203	0	253	Degrees of freedom are important for understanding model fit (Eisenhauer 2008) ≤2 = acceptable fit, Tabachnick and Fidell (2006)	n/a	n/a
${\mathsf{\chi }}_{\mathrm{M}}^2$	0.002	n/a	0.000	p-value Joreskog and Sorbom (1996)	<0.05	Significant
CMIN/DF	1.302	n/a	6.365	Chi-square divided by Degree of Freedom Kline (1998); Marsh and Hocevar (1985)	Between 1 and 3	Excellent fit
RMR, GFI
RMR	0.026	0.000	0.118	Root Mean Square Residual ≤0.05 = acceptable fit Diamantopoulos and Siguaw (2000)	The smaller the RMR value, the better	Perfect fit
GFI	0.890	1.000	0.382	Goodness of Fit Index A value ≥ 0.9 indicates a reasonable fit (Hu and Bentler 1998) A value of ≥0.95 is considered an excellent fit (Kline 2005) $\mathrm{G}\mathrm{F}\mathrm{I}=1-{\displaystyle \frac{{\mathrm{C}}_{\mathrm{r}\mathrm{e}\mathrm{s}}}{{\mathrm{C}}_{\mathrm{t}\mathrm{o}\mathrm{t}}}}$ where Cres and Ctot are the residual and total variability in the sample covariance matrix (Jöreskog 2004)	≤1 >0.80	Good fit
AGFI	0.850	n/a	0.326	Adjusted Goodness of Fit Index	>0.80	Good fit
PGFI	0.654	n/a	0.350	Parsimony Goodness of Fit Index Mulaik et al. (1989)	n/a	n/a
Baseline Comparisons
NFI	0.836	1.000	0.000	Normed Fit Index, also referred to as Delta 1 (Bollen 1989) A value of 1 shows a perfect fit, while models valued < 0.9 can usually be improved substantially (Bentler and Bonett 1980)	>0.80	Good fit
RFI	0.795	n/a	0.000	Relative Fit Index	>0.70	Good fit
IFI	0.956	1.000	0.000	Incremental Fit Index	>0.90	Perfect fit
TLI	0.944	n/a	0.000	Tucker–Lewis coefficient	0 to 1 >0.90	Perfect fit
CFI	0.955	1.000	0.000	Comparative Fit Index (Hu and Bentler 1998) A CFI value of ≥0.95 is considered an excellent fit for the model (West et al. 2012) (McDonald and Marsh 1990) $\mathrm{C}\mathrm{F}\mathrm{I}=1={\displaystyle \frac{{\mathsf{\chi }}_{\mathrm{M}}^2-{df}_{\mathrm{M}}}{{\mathsf{\chi }}_{\mathrm{B}}^2-{df}_{\mathrm{B}}}}$	>0.95	Excellent fit
Parsimony-Adjusted Measures
PRATIO	0.802	0.000	1.000	Parsimony Ratio	0 to 1 >0.50	Good fit
PNFI	0.671	0.000	0.000	Parsimony Normed Fixed Index expressing the result of parsimony adjustment (James et al. 1982) to the Normed Fixed Index (NFI)
PCFI	0.766	0.000	0.000	Parsimony Comparative Fix Index
NCP
NCP	61.369	0.000	1357.377	Non-Centrality Parameter	17.3–106.1 CI 90%	Good fit
LO 90	23.401	0.000	1234.343	Lower boundary
HI 90	107.451	0.000	1487.871	Upper boundary
FMIN
FMIN	1.477	0.000	8.997	Index of Model Fit	0.08–0.53 CI 90%	Good fit
F0	0.343	0.000	7.583	Confidence Interval
LO 90	0.131	0.000	6.896	Lower boundary
HI 90	0.600	0.000	8.312	Upper boundary
RMSEA
RMSEA (90% CI)	0.041	n/a	0.173	Root Mean Square Error of Approximation values ≤ 0.05 are considered excellent (MacCallum et al. 1996) (Steiger 1990) $\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}\mathrm{A}=\sqrt{{\displaystyle \frac{{\mathsf{\chi }}_{\mathrm{M}}^2-{df}_{\mathrm{M}}}{{df}_{\mathrm{M}}\left(\mathrm{N}-1\right)}}}$ (Mulaik 2009)	<0.06	Excellent fit
LO 90	0.025	n/a	0.165	Lower boundary	CI 90%
HI 90	0.054	n/a	0.181	Upper boundary	CI 90%
PClose	0.857	n/a	0.000	Close Fit Hypothesis Browne and Cudeck (1992)	>0.05

Note: P Close > 0.05, CF I > 0.95. Sales Excellence (SE), Sales Capability (SC), Market Alignment (MA), Strategic Responsiveness (SR), and Dynamic Sales Management (DSM). Source: table prepared by the authors (2023–2024).

presents the results of the FIT model, which aims to identify and evaluate the possible relationships between the variables and factors involved (SE, SC, MA, SR, and DSM) related to market mavericks and climate change through redefining sales velocity and profit surge in today’s dynamic business environment. The model has a chi-squared value (CMIN/χ²) of 264.369 and (χ2/df, 203) and a p-value of 0.002 at the 5% (0.05) level, indicating an excellent fit and a statistically significant model effect. Various model performance indices such as RMR (0.026), GFI (0.890), AGFI (0.850), PGFI (0.654), NFI (0.836), RFI (0.795), IFI (0.956), TLI (0.944), PRATIO (0.802), PNFI (0.671), and PCFI (0.766) collectively indicate a high level of model fit. The RMSEA index of 0.041 also supports a good fit to the data. These results indicate that the model has a good fit and corresponds well to the structure of the available data, highlighting the possibility of the presence of significant relationships and interactions between factors when testing alternative hypotheses.

Figure 3 presents the relationship related to market mavericks and climate change through redefining sales velocity and profit surge in today’s dynamic business environment based on the factors (SE, SC, MA, SR, and DSM) and their variables (SE1-10, SC1-3, MA1-3, SR1-3, and DSM1-4). According to the path diagram, the following findings are highlighted: SE (Sales Excellence) <--> SC (Sales Capability): the correlation of 0.263 indicates a positive and moderate relationship between high sales performance and salesperson abilities, suggesting that an improvement in SE has a positive impact on SC. SE <--> MA (Market Alignment): the correlation of 0.310 indicates a positive relationship between high sales performance and the company’s market adaptability, suggesting that changes in SE influence the company’s market adaptability. SE <--> SR (Strategic Responsiveness): the correlation of 0.356 indicates a positive relationship between high sales performance and the company’s strategic ability to respond to market developments, suggesting that changes in SE influence strategic responsiveness. SE <--> DSM (Dynamic Sales Management): the correlation of 0.341 indicates a positive relationship between high sales performance and dynamic sales management, suggesting that changes in SE influence dynamic sales management. SC (Sales Capability) <--> MA (Market Alignment): the correlation of 0.556 indicates a strong relationship between salespeople’s abilities and market adaptability, suggesting that changes in salespeople’s skills significantly affect the company’s suitability in the market. SC <--> SR (Strategic Responsiveness): the correlation 0.828 shows a very strong relationship between salespeople’s capabilities and the company’s strategic ability to respond to market developments, suggesting that salespeople’s capabilities have a substantial impact on the company’s strategic capability the company. SC <--> DSM (Dynamic Sales Management): the correlation 0.824 shows a very strong relationship between salesperson’s skills and dynamic sales management, suggesting that salespeople’s skills have a substantial impact on dynamic sales management. MA (Market Alignment) <--> SR (Strategic Responsiveness): the correlation 0.539 shows a positive relationship between market adaptability and the company’s strategic ability to respond to market developments, suggesting that market adaptability has a positive impact on the strategic ability of the company. MA <--> DSM (Dynamic Sales Management): the correlation of 0.532 indicates a positive relationship between market adaptability and dynamic sales management, suggesting that market adaptability has a positive impact on dynamic sales management. SR (Strategic Responsiveness) <--> DSM (Dynamic Sales Management): the correlation 0.720 shows a strong relationship between strategic capability and dynamic sales management, suggesting that strategic capability has a significant impact on dynamic sales management. Therefore, the results of the path diagram suggest that a focus on training and improving salespeople’s skills, effective interdepartmental collaboration, and deep market knowledge are critical for future corporate strategies to challenge the status quo in a dynamic environment and climate change.

Table 7. Verification of hypotheses.

Hypotheses	Elaboration	Tests	Rejected/ Accepted	Future Research/ Implications
Hypothesis (H)	There is a statistically significant and positive relationship between SE, SC, MA, SR, and DSM factors	Excellent Model Fit CFA EFA C.I ≈ 99.9% 0.60 ≥ α 0.05 ≥ λ p < 0.001 (***) p < 0.01 (**) RMSEA (90% CI), p = 0.041 ${\mathsf{\chi }}_{\mathrm{M}}^2$, p = 0.002 CFI = 96%	Accepted	The practical implications of the findings for businesses, stressing the importance of adapting and coordinating sales capabilities, market adaptability, strategic responsibility, and dynamic sales management.
Sub. H1	SE <--> SC		Accepted
Sub. H2	SE <--> MA		Accepted
Sub. H3	SE <--> SR		Accepted
Sub. H4	SE <--> DSM		Accepted
Sub. H5	SC <--> MA		Accepted
Sub. H6	SC <--> SR		Accepted
Sub. H7	SC <--> DSM		Accepted
Sub. H8	MA <--> SR		Accepted
Sub. H9	MA <--> DSM		Accepted
Sub. H10	SR <--> DSM		Accepted

Note: Sales Excellence (SE), Sales Capability (SC), Market Alignment (MA), Strategic Responsiveness (SR), Dynamic Sales Management (DSM), confirmatory factor analysis (CFA). Source: table prepared by the authors (2023/24).

provides strong support for the relationship between SE, SC, MA, SR, and DSM, confirming the hypotheses and demonstrating a good model fit. Each factor (SE, SC, MA, SR, and DSM) is highlighted individually for its strategic importance in shaping market mavericks. The positive and statistically significant associations between these factors emphasize their interdependence. The FIT model and path diagram analysis further confirm the significant relationships that impact sales velocity and profit surge. These findings have significant implications for companies seeking to improve their sales strategies and achieve sustainable growth in a dynamic marketplace and climate change.

5. Discussion

This research provides an in-depth analysis of the key factors—Sales Excellence (SE), Sales Capability (SC), Market Alignment (MA), Strategic Responsiveness (SR), and Dynamic Sales Management (DSM)—that drive sales performance in a dynamic business environment. The component matrix–PCA and confirmatory factor analysis (CFA) show that the values for these factors exceed 0.50, indicating their significant contribution to the model. Reliability scores from Cronbach’s Alpha range from 0.73 to 0.85, confirming the robustness and consistency of these factors.

Sales Excellence (SE) is particularly influenced by variables such as SE10 (0.71) and SE5 (0.67), focusing on resource provision and effective selling strategies. Sales Capability (SC) is driven by SC2 (0.71) and SC3 (0.70), highlighting the importance of quality service and product knowledge. Market Alignment (MA) is notably impacted by MA2 (0.77), emphasizing the significance of accurate sales forecasting. Strategic Responsiveness (SR) is shaped by SR2 (0.68) and SR1 (0.65), reflecting alignment with corporate strategy and responsiveness to customer demands. Dynamic Sales Management (DSM) is significantly driven by DSM3 (0.74), which emphasizes the motivation and commitment of salespeople.

The analysis of the correlations between these factors reveals their interconnectedness. For instance, Sales Excellence has a moderate positive correlation with Sales Capability (0.263, p < 0.010), Market Alignment (0.310, p < 0.003), Strategic Responsiveness (0.356, p < 0.001), and Dynamic Sales Management (0.341, p < 0.002). These findings suggest that improvements in Sales Excellence positively affect these other factors, aligning with Ho and Chang (2022) on the role of sales innovations, such as a clear sales strategy and quality services.

Similarly, Tong et al. (2022) emphasize the importance of efficient promotion strategies, which resonate with our results on Sales Capability and Strategic Responsiveness. The need for investment in equipping salespeople with new technological tools and motivation, as stressed by Rayburn et al. (2021), reflects our observed correlations with Sales Excellence and Dynamic Sales Management. Escobar and Alexandrov (2018) support our findings by arguing that well-designed sales territories enhance customer satisfaction and sales performance, corroborating our results on Sales Excellence, Dynamic Sales Management, Strategic Responsiveness, and Market Alignment.

Cheratian et al. (2024) suggest that funding for research and development, production diversification, and employee training can significantly increase sales velocity, aligning with our results on Sales Excellence and Dynamic Sales Management. Peesker et al. (2022) point out that salespeople’s analytical skills have a direct and moderate effect on sales performance, which supports our findings on the influence of Dynamic Sales Management, Market Alignment, Strategic Responsiveness, Sales Capability, and Sales Excellence. Lulaj (2024) underscores the critical role of sales strategies in bridging the gap between expected and actual expense values while navigating the complex interplay between finance, climate change, and sustainable finance in transitional economies. Lulaj et al. (2024c) also note that the impact of the pandemic will continue to affect businesses post-pandemic, necessitating innovative approaches to sustain and enhance sales velocity and profit in today’s dynamic business environment.

The FIT model analysis shows an excellent fit with a chi-squared value of 264.369 (p-value = 0.002) and an RMSEA index of 0.041, confirming the validity of the model. Performance indices such as GFI (0.890) and AGFI (0.850) further support the model’s fit to the data. In conclusion, the study highlights the critical importance of integrating Sales Excellence, Sales Capability, Market Alignment, Strategic Responsiveness, and Dynamic Sales Management to achieve sustainable growth and adapt to a changing business environment. Companies that effectively manage these factors are better positioned to overcome market challenges and capitalize on emerging opportunities.

6. Conclusions and Future Studies

This research explored market mavericks in emerging economies by redefining sales velocity and profit surge within today’s dynamic business environment. It focused on the interplay between key factors: Sales Excellence (SE), Sales Capability (SC), Market Alignment (MA), Strategic Responsiveness (SR), and Dynamic Sales Management (DSM). Extensive data analysis was conducted using SPSS and AMOS software, including exploratory factor analysis (EFA), reliability analysis (Cronbach’s Alpha), and confirmatory factor analysis (CFA). This analysis, performed on a sample of 180 companies in Kosovo from 2021 to 2023, validated these factors and supported the study’s hypotheses. The results underscored the crucial roles of SE, SC, MA, SR, and DSM in shaping market mavericks, with statistical reliability confirming their substantial impact on companies’ sales and profits. The component matrix highlighted these factors with values above 0.50, reinforcing their importance. The fit of the data to the models, validated by the Kaiser–Meyer–Olkin (KMO) test and Bartlett’s Sphericity test, further established the credibility of the study. CFA revealed significant weights for all factors, confirming their critical impact on redefining sales velocity and driving profit surges.

Implications: The findings of the study have significant implications for companies seeking to improve their sales strategies and achieve sustainable growth. Companies should focus on sales excellence by investing in the necessary resources and implementing effective strategies. Improving sales capability is essential, which includes delivering high-quality services and enhancing the expertise of the sales force. Ensuring Market Alignment is critical for accurate and reliable sales forecasting. Strategic Responsiveness requires aligning sales efforts with corporate strategy and ensuring that salespeople are accountable for meeting customer needs. In addition, dynamic sales management is essential to motivate and engage sales teams.

Recommendations: To achieve these goals, companies should adopt several key strategies. They should conduct regular assessments to evaluate and refine sales performance and strategies and invest in comprehensive training programs to improve the skills and knowledge of the sales force. Using advanced analytics will help make informed strategic decisions. In addition, establishing clear lines of communication within teams and across departments is essential. Finally, fostering a motivating work environment will effectively support and engage sales teams.

Future studies: Further research could explore several areas to deepen understanding and provide valuable insights. These include the following: examine specific strategies for improving sales performance and assess the impact of technological advances on sales processes; examine the influence of organizational finance culture on sales strategies and performance; examine the longitudinal impact of identified factors on sales and profitability; and analyze how external factors, such as market changes or regulatory changes, affect these key factors and their effectiveness.