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Background:
Hypothesis

Seeking More Sustainable Merger and Acquisition Growth Strategies: A Spatial Analysis of U.S. Hospital Network Dispersion and Customer Satisfaction

by
William Ritchie
1,*,
Ali Shahzad
1,
Scott R. Gallagher
1 and
Wolfgang Hall
2
1
Department of Management, College of Business, James Madison University, Harrisonburg, VA 22807, USA
2
School of Business and Society, University of Redlands, Redlands, CA 92373, USA
*
Author to whom correspondence should be addressed.
Geomatics 2025, 5(2), 23; https://doi.org/10.3390/geomatics5020023
Submission received: 17 April 2025 / Revised: 15 May 2025 / Accepted: 26 May 2025 / Published: 5 June 2025
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Abstract

The pursuit of mergers and acquisitions (M&A) is often an acclaimed strategy for firm growth, resource sharing, and extended reach into new market segments. However, in the healthcare marketplace, there are two very different perspectives related to M&A. On the one hand, the American Hospital Association commends M&A activity as a tool to reduce healthcare costs, drive quality, and serve rural markets. On the other hand, a recent United States’ Presidential executive order suggests that M&A in the healthcare space is harmful to healthcare due to its restrictions on competition and adverse impacts on patients. These conflicting perspectives reflect differing M&A views in mainstream management research, as well. The purpose of the current study is twofold. First, we aim to explore these two seemingly paradoxical perspectives by examining the degree of hospital network geographic dispersion that results from M&A activity. Second, we contribute to the broader M&A literature by drawing attention to the importance of considering geographic influences on M&A performance. Using a spatial analysis of 147 nationwide hospital networks comprising 1713 hospitals, we propose and find support for the notion that the degree of network dispersion, as measured by actual driving distances in healthcare networks, are correlated with patient experiences. Using ordinary least squares (OLS) regression to examine relationships between patient experiences and overall hospital network geographic dispersion, we found support for the hypothesis that more spatially dispersed healthcare networks are associated with lower overall performance outcomes, as measured by customer (patient) satisfaction. The implications of these findings suggest that growth strategies that involve M&A activity should carefully consider the spatial influences on M&A entity selection. Our exploratory findings also provide a foundation for future research to bridge the gap between industry and governmental perspectives on healthcare M&A practices.

1. Introduction

Mergers and acquisitions (M&A) are a common path chosen by companies to expand and enter new markets, gain technological resources, and overall corporate development [1,2,3,4]. Given its prevalence in the United States’ economy, it is no surprise that the members of the healthcare industry are engaged in M&A activity at one of the highest rates of all industries, thus presenting the proxy for our investigation into this organizational phenomenon from a spatial perspective. According to the Institute for Mergers, Acquisitions and Alliances, M&A activity in the healthcare industry is the largest in terms of transaction value among all the announced M&A in the United States between 2000 and 2018. With this widespread M&A activity in both the mainstream business management realm as well as in healthcare sectors, there remains a need to develop and refine appropriate metrics for M&A performance.
There are numerous theoretical justifications for M&A activity that would be especially salient for hospitals, including market power, economies of scale, management expertise, and utilization rates of equipment [5]. The growing presence of large healthcare provider networks in the United States is evidenced in that hospital chains with three or more hospitals own 56.1% of all hospitals [6]. Further, 24% of the United States’ hospital market share is controlled by ten healthcare systems where the revenue growth for these conglomerates was double that of other organizations in the marketplace [7]. In recognition of the benefits of M&A activities to grow these large systems, drawing upon 125 years of experience in healthcare, the American Hospital Association (AHA) suggests that M&A reduce costs, drive quality, and enhance access to healthcare services [8]. Challenging the proposed benefits of M&A activities that accrue to healthcare entities, the United States’ White House has raised concern for the high degree of healthcare M&A activity. Specifically, on 9 July 2021, a White House Executive Order Fact Sheet stated that:
“Hospital consolidation has left many areas, especially rural communities, without good options for convenient and affordable healthcare service. Thanks to unchecked mergers, the ten largest healthcare systems now control a quarter of the market. Since 2010, 138 rural hospitals have shuttered (closed), including a high of 19 last year, in the middle of a healthcare crisis. Research shows that hospitals in consolidated markets charge far higher prices than hospitals in markets with several competitors”.
[9]
Important for the current study, the apparent conflicting perspectives of the deep industry knowledge base of the AHA and the White House present one example of the conundrum regarding the overall impact of M&A activity in the healthcare arena and in the broader marketplace. These issues also reflect dominant contrasts in M&A performance perspectives in mainstream management. For example, in the banking industry, researchers have found varying performance outcomes with M&A-enabled geographic expansion, whereby in the short-term, banks realized positive returns, yet in the long run the costs outweigh the benefits [10]. Overall, researchers have sought to narrow the scope of the multiplicity of factors that ultimately drive M&A performance. Thus, the ultimate solution to the M&A performance question is nuanced, and this study investigates the spatial components of this area of research. Specifically, while previous research has sought to address many facets of M&A performance [2,11], we hope to elucidate an important factor that might play a significant role in overall organizational performance amidst the high M&A activity in the healthcare marketplace, that of evaluating hospital network dispersion and possible relationships with performance. The analysis presented in this study not only informs a unique area of M&A outcomes in the healthcare arena but also illustrates how geographic information system (GIS)-based analytic tools can serve as an important instrument in M&A research conducted in mainstream business management [2,11,12,13]. While this study focuses on data from the United States healthcare sector, our methods and findings offer evidence for the need for researchers’ further application of GIS analytic tools to other mainstream business management sectors that are experiencing heavy M&A activities.
Like the business management marketplace, one of the benefits of hospital network growth through M&A activity is that this strategy may facilitate the opportunity for shared resources among network entities. Shared resources may include physical assets, digital information, and one-on-one best practices in medical staff training. Relatedly, appropriate M&A activity affords the opportunity to refer patients to conveniently located network-member facilities for unique treatments [14]. However, when travel distances between network entities are extensive, there may be practical logistics questions related to the feasibility of timely access of critical resources, particularly related to medical knowledge-sharing. The uniqueness of this study is that it introduces spatial tools as a means of evaluating practical network logistics, that of real-world transportation times to-and-from hospital locations in a network.
The remainder of this study is structured as follows. Emphasizing the important role that geographic dispersion plays in knowledge sharing, the background section reviews related work focused on the role of location and knowledge spillover. The theory section introduces hospital networks and describes challenges associated with hospital network dispersion, knowledge spillover, and linkages with overall hospital performance. The methodology section focuses on the relationships that are examined in this study, namely overall hospital network geographic dispersion and hospital network performance, as measured by patient satisfaction. This is followed by a section describing the results from an OLS regression that identifies linkages between hospital network dispersion and patient satisfaction. Finally, a discussion and conclusion section summarize the study’s findings and recommends future research.

2. Background

Geography plays an increasingly important role in all aspects of business, especially in the management of services and sales [15,16,17]. As such, business researchers have recommended exploration into how geographical topography and topology impedes or improves the flow of products, services, and information between organizations [18] as well as how to expand its applications across a wide range of business research disciplines [19,20].
The influence of proximity on innovative processes within and between organizations carries important implications regarding innovation in a hospital network as well. However, the proximal nature of these effects is not well documented in terms of the actual travel within networks of organizations operating under the same organizational umbrella. Understanding these relationships calls for further research into the exchange of soft information [21]. Novak and Choi [18] echoed this call for a deeper understanding of spatial linkages in networks by encouraging dialog related to innovation diffusion. A similar appeal for attention to this topic can be found in the work of [15], who advocated geographic mapping to enhance the understanding of internal processes.
Prior research has also shown that managers are aware of potential spillover effects of information and make specific decisions to take advantage of such effects [22]. Often associated with accidental innovation [23], knowledge spillover activities are closely related to the proximity of organizations. Anecdotal evidence abounds regarding the benefits of proximity on accidental innovation, particularly if one considers the physical design of organizations such as Google’s New York City campus, where chance meetings between employees are fostered through the strategic placement of food courts and rest facilities [24]. Similarly, Pixar uses architecture to capitalize on chance social meetings to foster accidental innovation [25]. In these cases, face-to-face meetings and word-of-mouth communication provides multiple benefits on a variety of fronts such as hiring skilled personnel, diffusion of information, and knowledge sharing.
Organizations can appropriate the benefits of knowledge spillovers by identifying, assimilating, or exploiting potentially useful data [26]. Such benefits include access to technical knowledge [27], improvements in organizational practice [28], and better financial performance. Importantly, proximity is a primary determinant of knowledge spillover [28]. Moreover, technical knowledge spillover has been shown to be highly localized [29]. The importance of the relationship between proximity and knowledge spillover has relevance to healthcare organizations and their M&A activities on a variety of fronts. Healthcare organizations, and hospitals specifically, rely heavily on sharing knowledge within networks to meet a variety of unique needs of patients [30]. Thus, given the multiplicity of hospital facilities in a network, the proximity of these facilities often determines the ease of patient access as well as resources and knowledge sharing between facilities.

3. Theory

Taken in aggregate, hospitals in the United States are typically geographically concentrated when viewed from the Metropolitan Statistical Area (MSA) level. One common metric used to measure industry concentration is the Herfindahl–Hirschman Index (HHI), which is calculated by squaring the individual market shares of entities within a given market or industry. Thus, an organization with 10% of the market would contribute 100 points to the HHI, whereas if two organizations shared 5% of the market, the total contribution to HHI would only be 50 points. When evaluating the healthcare marketplace regarding the concentration of hospitals in MSAs, 99% of the 389 US MSAs having a Herfindahl–Hirschman Index (HHI) greater than 2500, indicating high concentration for their hospitals [31]. This is at least partially driven by the nature of the hospital business, whereby hospitals must simultaneously negotiate both with medical specialists to house their activities at their hospital and negotiate reimbursement rates with insurance providers, both industries that also have a high level of concentration and, thus, market power [32]. Hospitals thus occupy an important middle ground, serving as venues for specialists who need a large enough market to derive economies of scale and who may be able to draw patients from further away than their local MSA.
However, when evaluated from the hospital network point of view, there is a high level of variability in hospital concentration and dispersion. While larger network sizes (i.e., as measured by the number of member hospitals in a network) typically result in cost reductions [33], these effects are diminished when in-market effects are controlled for [33] (pp. 84–85). While financial performance is a key metric for assessing concentration effects, the current study focuses on the customer experience associated with hospitals that are members of a larger hospital network. The authors of the current study posit that overall customer care, as measured by patient satisfaction, may provide another useful facet of performance to consider related to M&A outcomes. Further, since all M&A activity carries a significant spatial component regarding the proximity of hospitals as a network grows, undertaking a spatial analysis of such entities is warranted. The author(s) endeavored to examine the relationship between the hospital network geographic dispersion and overall patient satisfaction by asking the question of whether network geographic dispersion, as measured by real-time travel distances, adversely impacts the patient experience with hospitals in that network.
The geographic dispersion of organizations play an important role in organizational clustering [34], as proximally located groups of organizations facilitate the efficient acquisition of skilled labor and resources [17,35,36,37], enabling competitive advantage [38]. The true benefits of reduced organizational dispersion are apparent when the specialized skills and abilities of a workforce [39] coalesce between organizations [38], yielding spillover benefits for nearby organizations [40]. Overall, the literature suggests that innovation and research and development are enhanced when geographic distance is reduced [40,41,42,43,44,45,46,47,48].
Within research related to the geographic dispersion of organizations, the literature points to at least three specific mechanisms by which innovation is enhanced through reduced geographic dispersion. First, there is a sense of greater connectedness and relationship-building between proximate entities [49]. Second, opportunities for chance meetings in social networks are enhanced [50]. Third, information sharing may be accelerated [51] when greater access to complementary resources is sustained for longer periods [50]. Taken in aggregate, the evidence suggests manifold benefits of geographic proximity.
Healthcare’s high reliance on specialized human and physical resources introduces a variety of location issues when delivering services [52,53,54,55]. Often, hospitals share specialized resources within networks to deliver the highest quality of care possible. For example, hospital entities either require their physicians to live within specific geographic boundaries of a hospital or rely upon physicians’ ability to travel to satellite locations to achieve efficiency with specialized staff. Thus, the proximal nature of healthcare facilities matters and may ultimately have an impact on customer experiences [56]. These relationships between geography and the logistical flow of information and resources are particularly relevant in healthcare, given the often shared and networked tangible assets (e.g., hospital equipment) and intangibles (e.g., physicians and their shared expertise), and the management of both.
Finally, the overall patient experience is also a critical issue associated with the geographic dispersion of M&A activities. For example, convenient geographic access to healthcare has recently been the focus of a variety of studies [57,58] and was shown to be correlated with shorter hospital stays for the elderly [59]. When viewed in aggregate, the appropriate geolocation of hospitals potentially enables the deployment of complementary human and physical resources to gain economies of scale, reduce the number of redundant practices, and create a more cost-effective and improved healthcare delivery apparatus. Thus, in this setting, well-engineered M&A activity potentially enables the deployment of complementary human and physical resources to gain economies of scale, reduce the number of redundant practices, and create a leaner and more cost-effective healthcare delivery.
Since nearly half of the almost 6000 hospitals in the United States are a part of a hospital system, where two or more hospitals share common ownership, the growth in hospital acquisitions over the past decade contributes to this staggering number of system-owned hospitals. Importantly, the growth in hospital systems occurred in a climate where hospital social responsibility expectations are at all-time highs [60,61], and more so with the COVID-19 pandemic and the increasing audit-based society [61,62]. Thus, healthcare organizations are obligated to seek the best practices and quality process implementation [63], but this may be difficult to achieve if network M&A growth strategies do not factor in the impact of geographic dispersion between hospitals. Specifically, healthcare services may be compromised with more dispersion among hospital networks. We offer the following hypotheses related to the relationship between hospital network dispersion and overall patient satisfaction.
H1. There will be a positive relationship between greater hospital geographic dispersion and low-level hospital patient satisfaction scores.
H2. There will be a negative relationship between greater hospital geographic dispersion and high-level hospital patient satisfaction scores.

4. Methods

The primary purpose of this research design was to identify possible linkages between patient satisfaction and hospital network dispersion. The overall analysis was carried out in two consecutive phases whereby phase one involved the GIS data analysis and phase two involved a standard ordinary least squares (OLS) analysis of the data. In phase one, we constructed the hospital network dispersion variable. To undertake this analysis, we employed the ArcGIS desktop software platform (v. 10) [64] to analyze data with the spatial analysis tool, Network Analyst. This enabled the measurement of street-level transportation distances using the Esri street-level database to create our network geographic dispersion variable. This mapping software has been used extensively by cartographers and statisticians for more than four decades to analyze virtually all manners of relationships in geospatial data, ranging from flood plain analyses to company location optimization. In phase two, we gathered data for the low and high patient satisfaction dependent variables and conducted the OLS regression by regressing the satisfaction variables on the linear combination of the control variables and the independent variable of interest, hospital network dispersion, using IBM SPSS Statistics Version 24 [65].

4.1. Sample Population and Data Collection

Our study used a sample of 1713 hospitals representing 147 hospital networks (i.e., hospital conglomerate systems) distributed throughout the United States. These healthcare systems ranged in size between 2 and 183 network-member hospitals. Data in this study were derived from the Federal Government healthcare provider database and supplemented with selected demographic data from the American Hospital Association database. We examined the relationships between the network geographic dispersion of hospital systems (as measured by total street-level transportation distances between hospitals in the network) and overall patient satisfaction scores (a proxy for hospital performance, as reported to the U.S. Federal Government Hospital Consumer Assessment of Healthcare Providers and Systems [HCAHPS]). The HCAHPS data (e.g., see https://data.cms.gov (accessed on 15 January 2018 and 31 May 2025) are publicly available online and present a significant source for healthcare research data. These data are compiled by hospitals and intended to provide a metric for hospitals, the Federal Government, and the public to choose hospitals. Overall, the metrics are relied upon for reporting and quality improvement. Our sample included only hospitals that met two criteria: (1) the hospital had a Medicare patient satisfaction rating for HCAHPS, and (2) the hospital was a member of a hospital network of at least two hospitals. The sample consisted of hospitals with an average size, in terms of 233 staffed beds, and a mean total facility FTE of 1408. Eighty-five percent of hospitals in the sample were certified by the Joint Commission and seven percent were certified by Det Norske Veritas Germanischer Lloyd (DNV Healthcare).

4.2. Measures

The independent variable, hospital network dispersion, was measured using real-time street-level driving distance between entities. This is an important distinction versus other metrics such as Euclidean distances or HHI indices. For example, the Euclidean distance measurement is an origin-to-destination metric that is a straight line and disregards any land features, such as mountains and water bodies, that preclude travel by motor vehicle. The HHI indices, while capturing concentrations of organizations in terms of market share, do not account for practical geographic conditions related to travel or logistics. Thus, “highly concentrated” entities merely represent the magnitude of the market that they serve relative to other organizations in their market. Such a measure does not capture several important practical conditions regarding logistics. First, it does not account for real-time travel distances between entities. Second, HHI does not distinguish organizational ownership, which is a critical factor in healthcare sectors, whereby patients and physicians typically serve and are served within a singular network of healthcare organizations.
Given the limitations with Euclidean distances and HHI metrics, the hospital network dispersion variable in this study was determined by calculating the total driving distances across all unique driving routes between hospital entities in a single network. This study created a measure of total driving distance for each of the 147 hospital networks nationwide based upon location data from 1713 total network-member hospitals. For example, one network is presented in Figure 1. In this case, there were 6 hospitals with 15 unique route combinations between any two entities (See Figure 1). The geographic dispersion of this network was calculated by dividing the total network miles by the number of unique routes, resulting in an average driving distance of 61 miles. The natural log was calculated for each of these final metrics to stabilize the variance for use in the regression analysis. Selected descriptive statistics for this sample network are presented in Table 1 (below).
The dependent variable, patient satisfaction, was measured using two separate HCAHPS metrics collected from the Federal Government Medicare database posted by the Centers for Medicare and Medicaid Services (CMS). The first metric, HSP_Rating_0_6 (patients who gave a low rating), was calculated as the percentage of patient satisfaction scores in the range of 0–6, on a 10-point scale (10 = high satisfaction; 1 = low satisfaction). The second metric, HSP_Rating_9_10, (patients who gave a high rating), was calculated as the percentage of patient satisfaction scores in the range of 9–10, on the same 10-point scale. These HCAHPS measures were deployed in our analysis verbatim as prepared and aggregated by CMS. Specifically, CMS aggregated patient satisfaction scores for both the “0–6” and “9–10” responses and reported the percentages, as described above. Only the lowest and highest patient satisfaction categories were considered in this analysis to explore relationships with the highest level of performance variance.
This analysis included appropriate control variables to minimize multiple potential spurious effects on performance outcomes. Organizational size plays a key role in organizational performance, particularly in terms of inertial effects of available resources and slack [66,67]. Therefore, organizational size was controlled for by including the total number of staffed beds (Total Beds) in each hospital. Organizational expenses play an important role in overall organizational performance outcomes, particularly in expenses related to research and development [68]. Therefore, overall hospital expenses (Natural Log of Total Expenses) were also included as a control variable of interest. Since there are direct performance relationships between overall healthcare staffing practices and patient care outcomes [69], we controlled for the level of hospital staffing by including the full-time equivalent staffing (FTE) (Total FTE) for each hospital in the model. Overall healthcare organizational performance is also impacted by finding the appropriate balance between inpatient versus outpatient care [70]. Thus, we included the HCAHPS metric for hospital total facility inpatient days (Inpatient). Hospital certification types also play a significant role in overall hospital operations and outcomes [71]. To control for these effects, we included a dichotomous variable that indicates whether a hospital held a certification with DNV Healthcare or Joint Commission (JCOMM). Finally, we controlled for the total number of surgical operations (Surgery) to account for hospitals with an emphasis on higher level specialty services, since there are demonstrated relationships between these activities and overall patient outcomes [72]. The means, standard deviations, and Pearson correlations of all variables in the analysis of hospitals (total number = 1713) are presented in Table 2.

5. Results

We regressed the patient satisfaction measures on the linear combination of the control variables (entered in the first model) and the main effect of network dispersion (entered in the second model, p < 0.001) using the multiple ordinary least squares (OLS) regression as deployed in IBM SPSS Statistics (Version 24). The stepwise regression results indicate a statistically significant (p < 0.01) change in the R2 from the first model to the second model. Upon the examination of the condition indices, a multicollinearity diagnostic, they were all below the suggested threshold of 30 [73]. The coefficient of determination (R2) for both the first and second models of all patient satisfaction regression models were significant at the p < 0.001 level (See Table 3).
In support of Hypothesis 1, there was a positive relationship between lower patient satisfaction scores and network dispersion (B = 0.143, p < 0.01, Adjusted R-square = 0.047, p < 0.001). This finding indicates that given the data in this study, hospitals that belong to networks with greater network geographic dispersion report a higher proportion of patients that submit lower satisfaction scores. In support of Hypothesis 2, there was a negative relationship between higher patient satisfaction scores and network dispersion (B = −0.234, p < 0.05, Adjusted R-square = 0.042, p < 0.05). This finding indicates that, given the data in this study, hospitals that belong to networks with greater network geographic dispersion report a lower proportion of patients that submit higher satisfaction scores. The results of these two findings corroborate the notion that if healthcare networks reduce network geographic dispersion, increases in overall patient satisfaction may be realized. Specifically, there may be reductions in the low-range patient satisfaction scores (e.g., 0–6) and increases in the high-range (e.g., 9–10) patient satisfaction scores. These results are elaborated upon in the following sections.

6. Discussion and Conclusions

The purpose of this study was to illustrate a novel application of GIS to the business management evaluation of M&A activity. Our goal was to provide business management literature researchers with a useful GIS-based technique to evaluate the dispersion of M&A outcomes. In the mainstream business management literature, extant M&A activity has been evaluated based upon a variety of M&A antecedents and performance outcomes [3,4]. Importantly, location-related factors that have been considered have centered on global M&A activity as well as the individual firm dyads of the acquiring firm and the acquired firm [74,75,76]. However, no known studies have evaluated M&A activity from the perspective of network geographic dispersion, as measured by real-time travel distances between acquired firms in a network. Thus, this study introduces an alternative methodology to business management M&A research, that of evaluating the effects of the actual cumulative driving distances between firms in a network.
We believe, like the processes of traditional innovation acceleration, that the spread of effective service practices is likely enhanced through local labor pools and resources associated with organizations nearby. In contrast, despite the dominant logic that electronic communication technologies have all but eliminated the liabilities associated with geographic distance [77], geographic dispersion between organizations can be a barrier to high performance, particularly as seen in our sample of hospital networks nationwide. The value of examining geospatial factors in healthcare research provides a simple yet profound finding: network geographic dispersion may play a significant role in terms of resource dependencies [78]. This is likely because of the impact of location on institutional ties, knowledge transfer, trust, and information sharing between sources. Such an analysis would not be possible without the use of street-level data and associated GIS tools.
First, it is important to recognize that M&A initiatives should be evaluated in light of not only the traditional performance characteristics of the acquiring institution, such as prior acquisition performance [79], board characteristics [80], organizational capital [81], absorptive capacity [82], and culture fit [83], but also on the resultant proximal aspects of the network of organizations.
Second, this study’s findings add clarity to the seemingly paradoxical recommendations of the AHA and the White House Executive Order related to M&A activity by suggesting that the real-time cumulative travel distances in a network (i.e., network geographic dispersion) may have an impact on organizational performance. For example, it is conceivable that increased M&A activities have significant positive impacts on networks that result in lower levels of network geographic dispersion. Similarly, adverse performance outcomes may be associated with M&A activities, whereby greater network geographic dispersion patterns are present.
Third, in consideration of the broader research community related to M&A activity, this study’s findings may ex-plain equivocal M&A outcomes, whereby some conglomerates report favorable performance with increased M&A activity and others do not. These findings bolster the need for the increased use of GIS-related research in main-stream business management literature, particularly regarding physical travel distances between entities that result from M&A activities.
Overall, this analysis revealed that the cumulative travel distances in a hospital network may be influential on overall patient satisfaction, emphasizing the importance of considering the geographic dispersion of M&A activity, and potentially working to minimize this condition, particularly for knowledge-based organizations such as healthcare and research-focused entities.

7. Future Research and Limitations

A possible fruitful extension of our findings would be to explore configurations of proximity-related performance differences with M&As at the network level of analysis. As mentioned previously, when evaluating the high-satisfaction performance rating (e.g., patient ratings of “9–10”), there was a strong negative relationship between geographically dispersed systems and service quality outcomes. Further, there was a strong positive relationship between very low-level category performance metrics (e.g., satisfaction rating of “0–6”) and network dispersion. Possible future research might consider whether more concentrated networks include clustered configurations of the high-level patient satisfaction category as well as clusters of the low-level customer satisfaction configurations within the network. Such a study would potentially tease out additional geographic influences on overall network performance.
While the overall methodology for this study generally followed accepted procedures for both the network and OLS regression analyses, there are some limitations that are noteworthy. The coefficient of determination (R2) was statistically significant yet explained a nominal amount of variance in the overall model. This finding was not surprising, given the high-level analysis and large-scale model that was conducted using hospital networks and organizational attributes. However, additional analyses should be undertaken to explore other network characteristics that might impact patient outcomes.
Importantly, while the current study deployed traditional strategy-related controls (e.g., organizational size, quality metrics, FTE, and hospital types) to control for spurious effects in the final regression model, there are a variety of other issues that might impact patient satisfaction scores at the network level, as well offering many fruitful future research directions. For example, future research might consider network governance characteristics such as corporate directives related to supply chain efficiencies, the allocation of highly skilled and support personnel within the network, and the demographics of the hospital regions and local communities. These factors were beyond the scope of the current study; however, they offer significant spatial research opportunities for additional spatial analyses of merger and acquisition performance.
Although this study attempts to fill a research void in the mainstream literature regarding the call for greater attention to proximal influences on organizational performance [18], there is still much to be learned in terms of geospatial influences on M&A activity outcomes at the firm-to-firm level of analysis. In this case, GIS tools such as service area analyses, hot-spot, and other network analysis tools can further inform M&A research in the business management discipline.
In summary, one of the primary purposes of this study was to integrate the seemingly disparate research streams of strategic management (e.g., mergers and acquisitions) and spatial analyses. To date, much of the mainstream business management research is devoid of spatial considerations of organizational phenomena. The current study endeavored to bridge this chasm by exploring spatial characteristics. Thus, we hope that scholars will view the “generalizability” of the current research as resting more on its call for further cogent spatial examinations of common business management research streams rather than solely on the novel drive-time distances applied herein. That is, our hope is to not only provide a potential research model or analysis but also to spur on similar applications across a wide spectrum of business management topics in the future.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

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Geomatics 05 00023 g001
Figure 1. Sample hospital network.
Figure 1. Sample hospital network.
Geomatics 05 00023 g001
Table 1. Sample hospital network descriptive statistics.
Table 1. Sample hospital network descriptive statistics.
MinimumMaximumMeanStd. Deviation
Total Beds176625287.33170.03
Total Expenses$154,851,867.00$646,560,806.00$292,671,596.17$182,079,589.49
Total FTE70633411467.33958.02
Inpatient36,485.00149,344.0064,364.0043,426.62
Surgery402318,72710,752.175731.78
Avg. Hospital Network Dispersion6161
Ln Avg. Hospital Network Dispersion4.114.11
Table 2. Descriptive statistics.
Table 2. Descriptive statistics.
VariablesMSD123456789
1. HSP_RATING_0_67.693.261
2. Total Beds233.33234.410.061
3. Total Expenses256,029,307.20364,843,521.09−0.030.861
4. Total FTE1408.332057.90−0.030.840.941
5. Inpatient55,098.2364,523.140.050.980.890.871
6. JCOMM0.850.350.060.130.110.100.121
7. DNV 0.070.25−0.020.000.00−0.010.00−0.571
8. Surgery8520.459288.07−0.040.860.850.840.870.13−0.011
9. Hospital Network Dispersion4.871.760.08−0.16−0.20−0.20−0.190.05−0.05−0.151
Note: Correlations greater than 0.03 are significant at p < 0.05.
Table 3. Client satisfaction results. Low Satisfaction (0–6) and High Satisfaction (9–10).
Table 3. Client satisfaction results. Low Satisfaction (0–6) and High Satisfaction (9–10).
Dependent Variable: Patient Satisfication Rating 0–6 Dependent Variable: Patient Satisfaction Rating 9–10
VariablesModel 1 (Beta)Model 1 (Std. Error)Model 2 (Beta)Model 2 (Std. Error)Model 1 (Beta)Model 1 (Std. Error)Model 2 (Beta)Model 2 (Std. Error)
Constant 0.261 0.338 0.600 0.777
Total Beds0.24 **0.0020.19 **0.002−0.18 **0.004−0.14 **0.004
Total Expenses−0.16 ***0.000−0.16 ***0.0000.14 ***0.0000.14 ***0.000
Total FTE−0.09 ***0.000−0.09 ***0.0000.11 ***0.0000.11 ***0.000
Inpatient0.24 ***0.0000.30 ***0.000−0.30 ***0.000−0.34 ***0.000
JCOMM0.0740.2710.0700.271−0.0560.623−0.0540.623
DNV0.0250.3830.0260.382−0.0020.880−0.0030.879
Surgery−0.26 ***0.000−0.26 ***0.0000.26 ***0.0000.27 ***0.000
Hospital Network Dispersion 0.143 **0.045 −0.234 *0.104
R20.046 ***3.190.052 ***3.180.044 ***7.330.047 *7.32
Adjusted R20.042 *** 0.047 *** 0.040 *** 0.042 *
Change in R2 0.006 ** 0.005 *
Note: * p < 0.05. ** p < 0.01. *** p < 0.001.
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Ritchie, W.; Shahzad, A.; Gallagher, S.R.; Hall, W. Seeking More Sustainable Merger and Acquisition Growth Strategies: A Spatial Analysis of U.S. Hospital Network Dispersion and Customer Satisfaction. Geomatics 2025, 5, 23. https://doi.org/10.3390/geomatics5020023

AMA Style

Ritchie W, Shahzad A, Gallagher SR, Hall W. Seeking More Sustainable Merger and Acquisition Growth Strategies: A Spatial Analysis of U.S. Hospital Network Dispersion and Customer Satisfaction. Geomatics. 2025; 5(2):23. https://doi.org/10.3390/geomatics5020023

Chicago/Turabian Style

Ritchie, William, Ali Shahzad, Scott R. Gallagher, and Wolfgang Hall. 2025. "Seeking More Sustainable Merger and Acquisition Growth Strategies: A Spatial Analysis of U.S. Hospital Network Dispersion and Customer Satisfaction" Geomatics 5, no. 2: 23. https://doi.org/10.3390/geomatics5020023

APA Style

Ritchie, W., Shahzad, A., Gallagher, S. R., & Hall, W. (2025). Seeking More Sustainable Merger and Acquisition Growth Strategies: A Spatial Analysis of U.S. Hospital Network Dispersion and Customer Satisfaction. Geomatics, 5(2), 23. https://doi.org/10.3390/geomatics5020023

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