contributors to this issue

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CONTRIBUTORS TO THIS ISSUE Erlyana Erlyana is Assistant Professor of Health Care Administration Department at California State University Long Beach (CSULB). She received her MD from Atmajaya Catholic University, Jakarta, Indonesia and her Ph.D. from University of Southern California in 2008. Her research interests include access to care among vulnerable population, comparative health, and global health. Dennis G. Fisher became the Director of the Center for Behavioral Research and Services (CBRS) at the California State University, Long Beach in August 2000. Prior to coming to CBRS, he was Professor of Psychology and Director of the Center for Alcohol and Addiction Studies at the University of Alaska Anchorage. He was the Principal Investigator on the National Institute on Drug Abuse (NIDA)-funded grants “Interventions to Reduce HBV, HCV and HIV in IDUs,” “IVDUs Not in Treatment in Alaska,” and “Behavioral Science Aspects of Rapid Test Acceptance”. Dr. Fisher received his B.S. from the University of California, Riverside, his M.S. in Counseling Psychology from the University of Alaska Anchorage, his Ph.D. from the University of Illinois, Urbana-Champaign, and was a NIDA post-doctoral fellow at the University of California, Los Angeles. He was the founding Associate Editor for Drugs of Psychology of Addictive Behaviors, and he is currently on the editorial board of AIDS and Behavior, the Open AIDS Journal, the Open Addiction Journal, and Behavioral Medicine. Grace Reynolds is Associate Professor of Health Care Administration and Associate Director of the Center for Behavioral Research and Services at the California State University, Long Beach (CSULB). She received her doctorate from the University of Southern California in 2004. Dr. Reynolds has published on HIV and sexually transmitted disease testing with health disparities populations and teaches graduate courses in research and quantitative methods.

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Michael Janson is the Chief of Research and Evaluation for the Division of HIV and STD Programs (DHSP) at the Los Angeles County Department of Public Health. He oversees program evaluation, data collection and reporting, as well as geographic information systems (GIS) for the County’s comprehensive HIV/AIDS service delivery system. During his junior year in college, Mike began working in the field of HIV research and evaluation in 1996. He has served as Project Manager for two NIH-funded studies which examined effective behavioral interventions for substance-using MSM and Commercial Sex Workers (CSW), was Principal Investigator for the Los Angeles Coordinated HIV Needs Assessment (LACHNA), was coInvestigator for the Rapid Testing Algorithm (RTA) study, and has studied the predictors of linkage and retention in HIV care. He has played key roles in the development and implementation of innovative HIV prevention and care programs including syndemic planning, geographical prioritization, pay-forperformance, RTA, and linkage to care and retention (TLC+). Mike has received recognition for his work in innovative program planning, development, and evaluation from the Department of Public Health, the Los Angeles County Board of Supervisors, the State of California, CDC, and the National Association of Counties (NACO). In 2006, Mike received a Master’s Degree in Public Health with an emphasis in research from the University of Massachusetts, Amherst, and also has a Bachelor’s degree in Psychology from Vanguard University. Joel Harmon is Professor of Management in the Silberman College of Business and Research Director of its Institute for Sustainable Enterprise at Fairleigh Dickinson University, Madison, New Jersey. His research and teaching focuses on corporate sustainability, including the linkages between people, learning practices and business performance. He was Principal Investigator on a four-year action-research project in the U.S. Department of Veterans Affairs funded by the National Science Foundation. Dennis J. Scotti is the Alfred E. Driscoll Professor of Healthcare & Life Sciences Management in the Silberman College of

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Business at Fairleigh Dickinson University, Teaneck, New Jersey. His research interests focus on health service strategy, health organization behavior, and patient satisfaction. Dr. Scotti is Certified Managed Care Professional (CMCP), a Certified Healthcare Financial Professional (CHFP), and holds fellowships in the American College of Healthcare Executives and Healthcare Financial Management Association .

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WHO PAYS? HOW REIMBURSEMENT IMPACTS THE EMERGENCY DEPARTMENT LAVONNE DOWNEY Roosevelt University ABSTRACT Background Nationwide from 1996 to 2004, the overall proportion of Emergency Department (ED) reimbursement ratios for outpatient ED visits decreased from 57% to 42%. The continued falling of ED reimbursement ratios, which is the share of ED charges that are ultimately paid, is an indicator of the financial pressures facing the ED. Once the healthcare reforms are put in place what will the impact be on reimbursement rates of overburdened and underfunded emergency departments. Purpose The purpose of this study is to examine if there is a declining disparity in payment rates for ED care based on payment sources in a safety net ED provider. Findings of this study could indicate how the healthcare reforms might impact these types of ED reimbursement ratios in the upcoming years. Methods This was a retrospective study that examined randomly selected charts of all ED visits charts from May 2002 to May 2008 at a level one adult and pediatric emergency trauma center with 45,000 annual visits. This study was IRB approved. Results A regression model was used to predict if there was a relationship between amount received and types of insurance payers within the ED. A significant relationship was found between types of insurance (payers) as the independent variable, and the dependent variables of charges (p= .00), payments (p= .00), amount of adjustments (p= .00), and balance remaining after 90 days (p= .00). Conclusions Who pays for the ED services does impact the ED’s bottom line. The privately funded patients will provide an ED with a higher reimbursement ratio per year as compared to those patients who are publicly or self pay. This explains why EDs that provide care for 40% or more publicly or self pay patients have seen a decline in reimbursement ratios. Healthcare reform has the potential to change and

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possibly improve safety net ED rate of reimbursement depending on how private, public and self pay patients pay for ED services. Key Words: Emergency Department, reimbursement, third party payers, Medicaid

INTRODUCTION Several studies such as Pines and Yearly (2009), Tang (2010) et al., Burt and Arispe (2004), and Kane (2003) the 24/7 availability of the Emergency Department (ED) and EMTALA rules make it very difficult for ED to select the patients they will serve. The combination of an increasing number of uninsured, under insured and economic recession have further increased the already over flowing numbers of patients ED serve. Nationwide ED has seen an increase from 1997 to 2007 of 37%. According to Tang (2010) et al., Burt and Arispe (2004) and Kane (2003) the number of adult patients who have Medicaid as their reimbursement has seen the largest increase at 17%. All the previously stated factors place ED’s and hospitals that house them in a financial strain. In studies by Tsai (2003) et al., and Hsai (2008) et al. this increase was seen nationwide but visit rates have increased the most for ED’s serving as safety net for medically underserved patients. These increasing pressures have been coupled with a decreasing reimbursement rates to Emergency Departments (ED) which could negatively affect their functioning. Tsai (2003) et al. and Hsai (2008) et al. have shown that nationwide from 1996 to 2004, overall proportion of ED charges paid for outpatient ED visits decreased from 57% to 42%. ED “reimbursement ratios” which is the share of ED charges that are ultimately paid is an indicator of financial pressures facing ED. Previous work by Tsai (2003) et al., showed a decreasing reimbursement rates for ED visits from 1996 to

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1998. Recent work by Hsia (2008) et al. has shown that prior to 2000 charges tended upward whereas payments were stable. This indicates a reimbursement ration that has decreased over time, steeply, from 2000 to 2007. Hsai (2008) et al., Tang (2010) et al., and Kane (2003) have shown that academic EDs report a collection rate on average of 38% with inner city ED receiving 22%. Two studies by Tang (2010) et al. and Hsai (2008) et al. found that private insured visits still have the highest levels of payments for services rendered and the lowest rates were seen in Medicaid and uninsured patients. They also found that reimbursement rates for pediatrics have also dropped from 63% in 1996 to 48% in 2003. Hsia (2008) et al. found that this drop was not just in publically funded patients. Hsai (2008) et al., Kane (2003), Tang (2010) et al. and Tsai (2003) et al. all confirmed that this drop was found across the board in all payers including: Medicaid/SCHIP, private and the uninsured. Sacchetti (2002) et al. study found that this can have a negative impact on the hospitals overall economic health as studies found that up to 34% of the hospitals revenue is generated from admitted ED patients. The reasons found for the reduction in reimbursement rates vary. However, most examine sources of lost income due to ED throughput and ED boarding of patients. According to Falvo (2007) et al., they found that by reducing the wait time to 120 minutes or less would increase revenue of $3,960,264 over a twelve month period. Another study by Flavo (2007) et al. also found that a hospital loses $3,881,506 in revenue due to patient elopements and ambulance diversions. The issue of lost income was more pronounced when looking at specific presenting symptoms such as chest pains patients who are waiting for beds. The fact that 91% of these patients waited more than three hours for their bed meant, according to Flavo (2007) et al, Bamezai (2005) et al. Bayley (2005) et

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al., and Schafermeyer (2003) et al., the hospital had an opportunity cost in dollars per patient waiting of $168,300. The potential impact of health care reform could exacerbate or improve the problem. The idea that fewer people will use the ED as a source of health care delivery might have a positive impact on reimbursement. However, as Chen (2011) et al. has shown in examining the impact of the Massachusetts reform on ED volume, ED use did not decrease. Instead Chen (2011) et al. saw an increase in ED usage by sicker patients who could be more expensive to treat. The ED then becomes a larger financial drain on the hospitals that house it. This is occurring at the same time as Disproportionate Share Hospital (DSH) funding is decreasing to the point of being phased out thus leaving less money available to hospitals to counteract the loss of pay for patients care in the ED. That process is occurring because the federal health reforms will be increasing access and affordability to health care. The Kaiser Family Foundation (2011) found that each state does vary as to how and when they give DSH funds to hospitals and most states reimburse for inpatient care not for care given through the ED. According to research done by Kaiser Family Foundation (2011) the DSH funding is very difficult to track so much so that the Government Accounting Office currently cannot provide a state by state analysis of how and to whom the DSH funds are given. These two occurrences of sicker patients and less funds to offset their cost could even further impact the ED’s ability to meet their mandate of care for anyone who enters their doors. The ED’s that will be most affected are those that serve as safety net facilities. According to Burt and Arispe (2004) a safety-net EDs are facilities that provide more than 30% of total ED visits to persons with Medicaid, more than 30% of total ED visits to uninsured individuals, or a combined Medicaid and uninsured patient population greater than 40%. Thus the purpose of this study is to

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examine if there is a declining disparity in payment rates for ED care based on payment source in an ED that has a majority government and uninsured payers. METHODS Study Design- Setting and Protocol This is a retrospective, chart review of randomly selected patients who presented to the ED from 2002 to 2008. The setting is a Level I pediatric and adult trauma center in the inner city with 45,000 visits. The hospital that houses the ED has 295 beds, averages occupancy rates for the time during the study is 65% and average length of stay was 4 days. Its service population consists of African American (70%), Hispanic (29%) and Caucasian (1%). The payer mixes during the years within the study were as follows: Medicaid (52.9%), Self-pay (23.1%), Medicare (8%), and private pay (16%). The ED within the study does not receive Disproportionate Share Revenue (DSH) for its patients as the state only reimburses the hospital for inpatient care not for care given in the ED. Charts used in the study were random selected. Random numbers were generated for each month within the time period. Random numbers were generated using STAT TREK for each day based on the total number of patients seen during a 24 hour period. From those numbers a random sample of patients was generated based on assignment of patient numbers. This allowed for a random selection of patients from days and times of day during the study period. According to Cohen (1988) in order to achieve a power of 80% using a regression analysis a minimum of 95 charts had to be selected per year for a total of 6 years. The data collection sheet used collected information including: triage scores, source of payment, ethnicity, chief complaint, procedures, and labs, total charges equal the amount charged by the hospital, charges paid, and balances remaining. Basic

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demographic data was also collected on all of the patients including age, gender, race, time of day, and day of the week, zip code, and transport mechanism. This data was input into a SPSS (version16, Chicago, IL) database and analyzed. A multiple regression model was used in order to predict, according to Portney and Watkins (2000) an outcome of reimbursement rates based on identifiable factor such as patients differing sources of insurance Those differing sources included: private (3rd party) insurance, self pay, Medicare and Medicaid. A regression model also allowed for an identification of the most significant factors that are related to the differences in reimbursement rates such as diagnosis, age, number of laboratory tests and radiology. This study was IRB approved. RESULTS A total of 589 patient’s charts were selected. However, only 575 charts were included in the study as 14 of the 589 contained incomplete data. The patient population was 56% (264) African American, 37% (175) Hispanic, 3% (21) white non-Hispanic with 22% (138) not responding. They were composed of 51% (249) female and 49% (235) male. The majority at 75% walked into the ED with the remaining 25% divided between being brought in by the police/EMT and by ambulance. The majority of patients 67% (377) came during PM hours with 33% (184) coming during AM hours. The reasons for their visits varied with the top three reasons as follows: 40% being general non-specific, followed by mental disorders at 10%, and 6% for musculoskeletal. The remaining categories all had less than 4 percent. The majority at 60% did not get admitted to the hospital and of those that did they stayed for one day. The payment types varied with Medicare 8%, self pay 23%,

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Medicaid 52%, private (3rd party payers) at 16% and the remaining unknown source. Using a regression model to predict amount received based on type of insurance as the independent variable there was a relationship between types of insurance (payers), and the dependent variables of charges (DDS) ( p= .03), payments (p= .00), amount of adjustments( p= .00), and balance remaining after 90 days (p= .00). This model had an adjusted r squared of 98% which means that these variables explained 98% of the variance within this model. There was also a reduction in the amount reimbursed per year over time. Reimbursement rates on average were $1876 in 2002 and fell to $1489 by 2008. Charges also dropped from on average $6483 in 2002 to $6154 in 2008. The differential between charges and receipts price related differential was at 1.3 in 2002 and expanded to 3.7 by 2008 (see table 1). The mean amount charges for each type of payer also varied with Medicaid being the highest at $7920, Medicare at $5515, Self Pay at $3221 and private 3rd party payers at $3224. This could be explained in part due to the differences in presenting complaints and diagnosis between the patients. Medicaid patients were three times more likely to present with respiratory, circulatory, musculoskeletal, ear nose and throat, nervous system, trauma and metal disorders than were either private pay or Medicare patients. Self pay patients presented with similar presenting complaints and diagnosis to private pay and Medicare with the exception of general trauma and mental illness. Medicaid patients were also twice as likely to require laboratory and radiological tests during their ED visits as compared to Medicare and private pay patients. This could be an explanation for the higher level of charges for Medicaid patients. Table 1 Mean charges and receipts per year by payer type

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Medicaid

Medicare

Self pay

Private insurance

1

407

Year 2002

Charges

Receipts

10538.4215

2841.0683

2003

2598.4644

322.7717

2004

1202.0000

114.3750

2005

4820.9400

2223.2944

2006

11313.7335

4644.9988

2007

8393.2822

1794.8638

2008

7461.0847

2011.0983

2002

5099.7959

2917.0471

2003

28583.4700

17170.7033

2004

1357.6100

544.9567

2005

2132.9314

1607.0414

2006

14695.6950

4729.6550

2007

6240.7867

1170.6667

2008

1126.9600

419.2400

2002

1912.9392

31.2917

2003

9405.7033

.0000

2004

125.0000

.0000

2005

3297.8752

80.8000

2006

1742.2782

.0000

2007

4341.6881

1.8750

2008

1390.8800

59.5000

2002

488.9350

362.2836

2003

19622.5400

1571.4000

2004

2131.9827

1217.9336

2005

1271.4993

1229.5993

2006

17141.2886

5368.6571

2007

4796.6038

1642.7712

2008

1870.4050

1781.0950

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Overall, there was also a difference when comparing payer sources in amount payment and adjustments. Medicaid had the largest difference between the mean amount charged and received followed by Medicare, private 3rd party players and self pay. Medicaid had a difference between the mean charged and paid of $5643, followed by self pay at $3195, Medicare at $2532 and private payers at $2089 (see table 1). This indicates that public and self paying sources ended up paying the lower amount for reimbursement for services as compared to private payers. The mean amounts of outstanding balances after one calendar year were between $24 from private payers, $117 for Medicaid, $498 for Medicare and $2699 for self pay. This would indicate that those with the largest amount of unpaid charges were self pay patients. DISCUSSION Payment source did affect the amount paid to the ED for services rendered. Private payer sources are charged the second lowest amounts, however as was seen in the study by Pines and Yearly (2009), private payers also pay the highest amount per charged rate for services rendered. This could mean if the ED has more privately funded payers they will receive a higher reimbursement ratio of their initial charges at the end of the year. If the amount of these patients increases due to the requirement to obtain health insurance under health reform this could have a positive impact on reimbursement rates for the ED. The largest range between charges and payments was for government (state and federal) publicly funded sources and those patients who self pay. This same finding was seen by Hsia et al. (2008) who also found that the amount reimbursed for publically funded patients has dropped especially when compared to the amount privately funded patients are reimbursed. The problem however, is that as we

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have seen from the recent study by Tang (2010) et al., the increasing number of patients coming into the ED, especially ED’s that are safety nets, are patients whose bills are paid by Medicaid. These patients, as was seen by Burt (2004) et al., are the ones that have the lowest reimbursement rates for the services rendered within the ED. The provision of the health care reform that would have mandated states to increase coverage of Medicaid patient’s medical costs and implement reforms in the way Medicaid is delivered to its recipients was struck down by the Supreme Court last summer. The federal government, under the health care reforms will be offering substantial funds to states which could still entice states to follow the intent of the reform. If however, this does not entice states then it does not bode well for stopping or reducing the decline in reimbursement rates for these safety net hospitals of Medicaid patients. This is especially concerning due to the finding that this study and Chen (2011) and Tang (2010) found an increase in sicker patients, who require more extensive care, coming to the emergency department. If states do not take the federal moneys and reform policies on Medicaid it could potentially expand the number of individuals who will be self pay to the ED.

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Table 2 Mean Charges, Receipts, Adjustments and Balances by Insurance Type Type of payer

Mean

Charges (Per DSS) Medicaid

7920.8416

Medicare

5515.0946

Self pay

3221.7214

Private insurance

3224.6961

Medicaid

2277.4584

Medicare

2983.9300

Receipts (Per DSS)

Self pay

26.7206

Private insurance

1135.9232

Medicaid

5526.5500

Medicare

2207.2087

Self pay

491.5130

Adjustments (Per DSS)

Private insurance

2066.3247

Balance (Per DSS) Medicaid

117.1359

Medicare

493.5911

Self pay

2699.1569

Private insurance

1 2 3

24.5228

For those patients who are self paid, whom comprised 23% of the patients in the study the changes in

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health care reform could benefits them and the ED. They are the patients who were given the lowest amount of adjustments to their bill and had the widest range of balances due at the end of 1 year. In this study that means that for those that are self pay the mean past due amount is $2699. The potential good news is that this population could see the biggest change in payment resources when the new federal health care plan is in place. Instead of outstanding balances, the ED could have some form a reimbursement for these patients through the required health insurance coverage mandate or from government subsidies that would pay for their care. This would thus add to the EDs overall balance sheet in that services currently not paid for would be reimbursed. However, if their reimbursement rates are similar to the other federal and state payers then the hospital will still experience issues of drops in reimbursement rates as compared to charges. The one caveat to this is that at this and many urban safety net EDs almost half of the self pay patients are illegal immigrants for whom the new healthcare reforms do not apply. This would mean that the ED would still see a potential of 10% of self pay patients. LIMITATIONS This study was done at one site an urban inner city ED. The majority of the patients seen in this ED are publicly or self funded. This may skew the results especially when comparing payment sources and their impact. A study examining two hospitals with variations in payer sources might result in findings that have a wider application. It was also a retrospective study which means that that it relies on the written record used which is the chart. Information may be incorrect with limited possibility of accessing correct information. This may have limited this study in that information about how and why different patients have differing levels of charges may not be able to be gleamed by

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chart review alone. A prospective study would allow for a more comprehensive range of variables which may or may not have impacted the cost of patient care. This study also might be affected by pre- arranged payment agreements for specific services from a state, federal and those private payers that operate within this market. Different states might have different arrangements and a wider array of private payers which would impact amounts paid. Future studies could see if there are regional differences based on payers in the market and publicly funded contracts that produce regional differences in payment amounts thus impacting ED reimbursement ratios. CONCLUSIONS Who pays for the ED services does impact the ED’s bottom line. If you have more privately funded payers you will get more money in total at the end of the year than if you have a mixture of payers with 25% or more of them being publically or self funded payers. The payer mix still determines the EDs budget. Based on the study by Chen (2011) et al health care reform has not meant that there will be a drop in the numbers of those seen in the ED (16). This is complicated by the fact that according to Tang (2010) et al. there has been an ever increasing numbers of visits by persons with Medicaid. Tang (2010) et al. found that emergency departments are increasingly serving as the “safety net of the safety net,” as the burden placed on them by the underserved population has increased, both in terms of overall volume and the types of conditions they treat. Holahan (2012) estimates that some 16 million more individuals are expected to obtain Medicaid coverage through the implementation of the Patient Protection and Affordable Care Act of 2010. Knowing these results indicates that a possible differentiation in rates may have to

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be pushed for those hospitals that are serving as safety nets for the patients they see in their ED’s. The impact that payer mix has on an ED’s reimbursement rate coupled with the knowledge of the payment mix of the patients currently served needs to result in a different type of equation for the rate of reimbursement for these institutions. There is a possibility that if higher rates are not determined, based on who is paying for the services rendered, that we will have fewer ED’s to see more publically funded patients. The hospital that houses these ED’s cannot sustain the current situation of less reimbursement for an increasing number of these patients seen every year.

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Bamezai A, Melnick GA, Nawathe AC. (2005). The cost of an emergency department visit and its relationship to emergency medicine volume. Ann Emerg Med. 45:483-490. Bayley MD, Schwartz JS, Shofer FS. (2005) The financial burden of emergency department congestion and hospital crowding for chest pain patients awaiting admission. Ann Emerg Med Feb; 45(2):110-117. Burt CW, Arispe IE. (2004). Characteristics of emergency departments service high volumes of safety net patients: United States 2000. Vital Health Stat 13. 155:1-16. Chen C, Scheffler G, BA, Chandra A. (2011) Massachusetts' Health Care Reform and Emergency Department Utilization. N Engl J Med 365:e25. Cohen , J. (1988) Statistical Power of Analysis for the Behavioral Sciences, ed.2 Hillsdate, NJ: Lawrence Erbaum. Hsia RY, MacIssac D, and Baker LC. (2008). Decreasing Reimbursement Rates for Outpatient Emergency Department Visits. Annals of Emergency Medicine March (3) 265-274.e5. Falvo T, Grove L, Stachura R. (2007) The opportunity loss of boarding admitted patients in the emergency department. Acad Emerg Med. Apr; 14(4): 332337.

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Falvo T. Grove L, Stachura R. (2007) The financial impact of ambulance diversions and patient elopements. Acad Emerg Med. Jan; 14 (1) 58-62. Hsia RY, MacIssaac D, Palm E. (2008) Trends in changes and payments for no hospitalized emergency department pediatric visits, 1996-2003. Acad Emerg Med. April; 15(4): 347-354. Holahan J, Buettgens M, Carroll C, Dorn S. (2012). The Cost and Coverage Implications of the ACA Medicaid Expansion: National and State-by-State Analysis. Washington DC: Kaiser Foundation. Kane C. (2003). Physician Marketplace Report: The Impact of EMTALA on Physicians Practices. Chicago, IL: Center for Health Policy Research, American Medical Association. Pines JM and Yearly DM. (2009). Advancing the Science of Emergency Department Crowding: Measurement and Solutions. Ann Emerg Med. Oct;54(4):511513. Epub 2009 Jul 2. Portney L and Watkins, M. (2000). Foundations of Clinical Research- Applications to Practice. Prentice Hall. 226-228. Sacchetti A, Harris RH. Warden T. (2002) Contributions of ED admissions to inpatient hospital revenue. Am J Emerg Med Jan; 20(1): 30-31. Schafermeyer, R.F., Asplin, B.R. (2003) Hospital and Emergency Department crowding in the United States. Emerg Med Feb; 15(1):22-27.

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Tang, N., Stein, J., Hsai, R. (2010). Trends and Characteristics of US Emergency Departments Visits 1997-2007. JAMA. 304(6):664-670. Tsai AC, Tamayo-Sarver JH, Cydulka RK. (2003). Declining payments for Emergency Care. Ann Emergency Medicine. 41(3)299-308. .

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LINKAGES BETWEEN ORGANIZATION CLIMATE AND WORK OUTCOMES: PERCEPTUAL DIFFERENCES AMONG HEALTH SERVICE PROFESSIONALS AS A FUNCTION OF CUSTOMER CONTACT INTENSITY

12

Acknowledgement

13 14 15 16 17 18

Work on this project was partly supported by a grant from the US National Science Foundation (NSF), Innovation & Change Division. We acknowledge the Veterans Health Administration (VHA) for providing data used in this study. The views expressed in this article do not necessarily represent those of the VHA or NSF.

19 20

ABSTRACT

21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37

The delivery of high-quality service, rendered by health service professionals who interact with customers (patients), increases the likelihood that customers will form positive evaluations of the quality of their service encounters as well as high levels of customer satisfaction. Using linkage theory to develop our conceptual framework, we identify four clusters of variables which contribute to a chain of sequential events that connect organization climate to personal and operational work outcomes. We then examine the perceptual differences of service professionals, grouped by intensity of customer contact, with respect to these variables. National data for this project were obtained from multiple sources made available by the Veterans Healthcare Administration (VHA). Cross-group differences were tested using a series of variance analyses. The results indicate that level of customer-contact intensity plays a significant role in explaining variation in perceptions of support staff, clinical practitioners, and nurses at the multivariate and univariate levels of analysis. Contact intensity appears to be a core determinant of

DENNIS J. SCOTTI JOEL HARMON Fairleigh Dickinson University

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1 2 3 4 5 6 7 8 9 10 11 12 13 14

the nature of work performed by health service professionals as well as their psychological responses to organizational and customer-related dynamics. Health service professionals are important resources because of their specialized knowledge, labor expense, and scarcity. Based on findings from our research, managers are advised to survey employees’ perceptions of their organizational environment and design practices that respond to the unique viewpoints of each of the professional groups identified in this study. Such tailoring should help executives maximize the value of investments in human resources by underwriting patient satisfaction and financial sustainability.

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In response to recent statutory pressures to improve service quality, healthcare organization managers have developed a keen interest in gaining clearer insights regarding factors that determine customer perceptions of their experiences as patients, which are substantially shaped by their contact with employees. Under the Value Based Purchasing (VBP) program, recently implemented as part of reforms contained in the Affordable Care Act of 2010, patient experiences of care will significantly impact Medicare payments to healthcare providers. Employees that come in direct contact with patients play a critical role in shaping their perceptions of such experiences (Scotti, Harmon, and Behson, 2009). Moreover, empirical findings indicate that the delivery of high-quality service rendered by frontline employees, with high-intensity customer interactions, increase the likelihood that customers will form positive evaluations of the quality of their service encounters as well as high levels of customer satisfaction (Deitz, Pugh, and Wiley, 2004; Mayer, Ehrhart, and Schneider, 2009). High levels of customer satisfaction, in turn, translate into customer loyalty and financial performance of the firm (Heskett, Sasser, and Schlesinger, 1997).

INTRODUCTION

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Evidence supports the importance of creating a work environment or “climate” that enables, motivates, and rewards the provision of high-quality service by employees within an organizational milieu that emphasizes concern for its customers (Pugh et al., 2002). Also of importance is the role played by employee perceptions of their job conditions (e.g., work stressors and empowerment) in mediating the interface between organizational climate and work outcomes such as employee evaluations of their service capability, satisfaction, and turnover intentions (Ruyter, Wetzels, and Feinberg, 2001) as well as their judgments of job performance (Arnold, et al., 2009, Leggat et al., 2010). Strategic marketers and human resource managers have struggled to discover the “ideal” combination of actions to foster an organizational climate for service; but there is no reason to expect that all employees will respond homogeneously to a uniform set of managerial practices. What if we better understood how different occupational groups, particularly among health service professionals, may respond asymmetrically to stimuli in their work environment depending on their intensity of customer (patient) contact? This very question inspired us to search for an answer that will help managers understand which specific aspects of change-producing forces in the workplace matter most to different groups of healthcare professionals (e.g., support professionals, direct clinical providers, nurses) so as to maximize the value of investments in human resources. In the following sections we offer a concise review of the extent literature undergirding the arguments made above and draw on “linkage theories” spanning several streams of research relating to service climate, job conditions, and performance outcomes. We first review the literature to illuminate our research questions regarding meaningful psychological response differentials across the previously mentioned groups of health service professionals stratified by the intensity of their patient encounters, and

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then examine these questions using data from 113 Veterans Health Administration facilities comprising 59,454 service professionals categorized by occupational groupings. Our findings are presented and discussed with attention to implications for practicing healthcare managers.

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BACKGROUND AND CONCEPTUAL FOUNDATION The present study borrows from a substantial and growing body of literature, commonly known as “linkage research,” that seeks to explain the temporal chain of events that connect employee perceptions of managerial practices with operational and strategic outcomes consequential to organizational performance (Dean, 2004 traces the evolution of linkage research). We do this in order examine the locus and magnitude of influence exerted by contact intensity in this chain across occupational groupings of health service professionals. As further detailed below, early studies conducted on retail service firms focused on linkages at the business-unit level and established robust evidence to support the positive impact of organizational “climate” on desirable organizational service outcomes such as employee and customer perceptions of service quality, employee and customer satisfaction, and profitability. Subsequent empirical inquiries studied the influence of personal factors that mediate this causal chain of events. A summary of the core linkage variables that we examine for group differences, and the position they occupy in the temporal chain, is presented in Table 1. Work Climate The “climate” of an organization is determined by employees’ shared perceptions of the managerial practices and kinds of behaviors that are expected, supported, and

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rewarded in a contextual setting (Schneider, 1990). The quality of service provided by employees is influenced by two conceptually distinguishable but interrelated environmental dimensions of workplace climate: strategic human resource management (SHRM) practices and customer orientation. Collectively, HRM practices and customer orientation reflect management’s concern for two key stakeholder groups, employees and customers, respectively (Borucki and Burke, 1999). Table 1 Summary of Core Drivers in Linkage Model and Study Measures. Linkage Clusters and Core Drivers

Study Measures

Work Climate Strategic Human Resource Practices Customer Orientation

Work Climate High-Performance Work Systems Customer Orientation

Job Conditions Role Ambiguity Role Conflict Role Overload Work-Family Conflict Job Control

Job Conditions Task Clarity Role Alignment Workload Balance Work-life Balance Job Control

Personal Work Outcomes Work Stress Service Capability Job Satisfaction Organization Commitment Turnover Intentions

Personal Work Outcomes Work Stress Service Capability Employee Satisfaction Not available Intent to Stay

Service Outcomes Service Quality Customer Satisfaction

Service Outcomes Employee-Perceived Service Quality Employee-Perceived Customer Satisfaction Not available

Customer Loyalty Organizational Performance Outcomes Operational Efficiency Return on Investment Market Performance

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Organizational Performance Outcomes Not available

Deployment of systematic assemblages of best practices in SHRM, frequently dubbed high-performance

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work systems (HPWS; e.g., Nadler and Gerstein 1992; Lawler, Mohrman, and Ledford 1995), is widely believed to yield performance results that are superior to implementing such practices on a piecemeal basis (Huselid,1995; Combs et al., 2006, Delery, 1998). Also referred to in the literature as progressive HRM practices or high-involvement work systems, HPWS comprise an integrated and aligned set of SHRM practices that enable workers’ knowledge and skills through training, align their activities with clear and important goals, increase their involvement through participation in decision making, empower them to innovatively adapt to customer needs by granting them discretion, amplify their individual effectiveness through cooperation and teamwork, and motivate them to perform by offering proper recognition and incentives (see Becker and Huselid, 1998 for a superb review and synthesis of the conceptual and empirical underpinnings of the HPWS construct). Customer orientation refers to the importance that management places on customers’ needs and expectations relating to the firm’s service offerings (Kelley, 1992). Prior research in the banking industry identified a connection between strategic HR practices and customerservice orientation and found that both played a role in shaping employee and customer perceptions of service quality (Schneider and Bowen, 1985, Schneider, White, and Paul, 1998). In their study of automobile service centers, Rogg et al. (2001) found that a customer-oriented climate formed a mediating link between HPWS and customer satisfaction. More recently, empirical confirmation of these linkages was extended to the healthcare sector (Scotti, Harmon, and Behson, 2007; Scotti, Harmon and Behson, 2009). Job Conditions The relationship between an organization’s workplace climate and employee/customer perceptions of

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service outcomes is not direct and is also influenced by worker perceptions of job conditions and their personal (attitudinal) reactions to those conditions. The occupational milieu in which employees perform their work roles is influenced by several sources of strain that engender psychological distress, which ultimately affects their service quality. Sources of work strain typically include role ambiguity − unclear work role expectations, role conflict − expectations to fulfill two or more incompatible roles, and role overload − excessive job demands with insufficient time for completion (Katz and Kahn, 1978). More recently, work-family conflict − incompatible demands from work and family life − has attracted the attention of occupational psychologists as a contributor to strain in the workplace (Frone, 2003; Kossek & Ozeki, 1998). In addition to classical work stressors, contemporary literature has underscored the importance of job control − an employee’s perception of the amount of discretion at his/her disposal to meet the demands of a task – as a critical element of job conditions that influences personal work outcomes (Liu, Spector and Jex, 2005). Personal Work Outcomes Perceptions of work stressors have been linked to lower levels of frontline employee satisfaction (Hartline and Ferrell, 1996; Chebat and Kollias, 2000), lower perceptions of performance capability (Gilboa et al, 2008), and increased turnover intentions (Chang, 2008). Similar effects of work stressors have been observed among hospital nurses (Huber, 1995). Spector (1986) concluded from his meta-analysis of over 100 studies that job control was positively linked to employee satisfaction, performance, and turnover intentions. Perceived job control and related feelings of empowerment have also been associated with heightened confidence about one’s self-efficacy and service capabilities (Gist and Mitchell, 1992; Robinson, Neeley, and Williamson, 2009)

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and job satisfaction (Schlesinger and Zornitsky, 1991). Moreover, a sense of job control has been linked to better quality of care delivered by hospital employees (Gibson, 2001). In the healthcare domain, the co-occurrence of high job stressors and low job control has been directly and negatively correlated with satisfaction and professional commitment among physicians (MacNeil, 1998). Service Outcomes Ultimately, the collective effects of these dynamic linkages translate into employee perceptions of service quality (Ma et al., 2009; Slatten, 2008) and their perceptions of customer satisfaction (Johnson, 1996). It is well worth noting that direct-contact employee perceptions of key service outcomes such as service quality and customer satisfaction have been shown to be strongly correlated with and predictive of actual customer appraisals of these outcomes in retail enterprises (Schneider and Bowen, 1985; Schneider and White, 2004) and in healthcare settings (Scotti, Harmon, and Behson, 2007). Contrary to conventional beliefs, recent evidence suggests that health service employees are actually more stringent and critical in their evaluations of service quality than are their customers (Fottler et al., 2006) and that assessments of service quality by professionally-trained providers are more accurate than other employees (Young et al., 2009). Organizational Performance Outcomes Prior research has established the empirical link between market orientation, service quality and business profitability (Chang and Chen, 1998) and has provided strong evidence that employee psychological outcomes, customer service outcomes and organizational outcomes (i.e., profit and revenue growth) are connected through a service-profit chain (Heskett, Sasser, and Schlesinger, 1997). Other investigators have added to our understanding

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of the service-profit chain by focusing attention on the importance of customer satisfaction (Bernhardt, Donthu, and Kennett, 2000) and customer retention (Anderson and Mittal, 2000) as mediating factors along the path to superior financial performance. In the healthcare sector, connections between patients’ judgments of service quality and improved financial performance at 51 U.S. hospitals have been empirically verified by Nelson et al. (1992) and, more recently, Goldstein (2003) found significant relationships among employee outcomes, customer’s satisfaction and revenue growth in a sample of 220 U.S. hospitals. At least one author (Silvestro, 2002) has called into question the positive link between employee outcomes (i.e., satisfaction and loyalty) and organizational performance (i.e. productivity and profitability) based on findings from his study of a small sample of U.K. grocery stores. This author states, however, that: “…in services where there are high levels of contact between customers and staff, few opportunities for technological substitution, staff/customer contact is a critical aspect of service value to the customer, and labour costs represent a significant proportion total costs, then there may well be a direct link between employee satisfaction, loyalty, unit productivity and profit.” (Silvestro, 2002, p.44) The contextual qualities identified by Silvestro are precisely those that characterize the provision of healthcare services. Accordingly, we feel assured that the favorable connection generally assumed to exist between employee outcomes and organization performance outcomes holds true for hospitals and other health services organizations. To be certain, the exact composition, sequencing and interaction of the complex system of relationships thus far

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described remain an issue among researchers. While there is some consensus regarding the core set of drivers constituting a comprehensive linkage model, and their causal clustering in the chain of events, a fully integrated conceptual framework has not yet been validated empirically in the extant literature. As stated in the introduction, our purpose in the present study is not to propose and test such a model. Rather, we believe that the current state of linkage research has reached a point that invites inquiry into an important question: Irrespective of the precise causal relationships among the core set of drivers, do all service providers (particularly health service professionals) perceive and evaluate the levels of these drivers homogenously? Moreover, if notable differences are observed, to what degree does contact intensity explain such differential perceptions? Contact Intensity as a Moderating Force Prior to the turn of the century, linkage research studies subsumed frontline employees under the universal rubric of “service workers.” Rogelberg, Barnes-Farrel, and Creamer (1999) were perhaps the first authors to distinguish among types of service positions and to propose a taxonomy for classifying service providers based on interaction medium (remote vs. direct), type of service outcome (standardized vs. customized), and frequency of contact. Subsequent empirical investigations found that the frequency of customer contact moderated the relationship between employees’ perspectives of work climate and customer evaluations of service outcomes at retail service firms (Dietz, Pugh, and Wiley, 2004; Mayer, Ehrhart, and Schneider, 2009), and between high-contact healthcare services vs. low-contact claim-processing services (Scotti, Harmon, and Behson, 2009). Compared with direct care providers (clinical practitioners and nurses), the work performed by healthcare

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support staff (administrative, financial, and technical staff) involves limited face-to-face contact with patients, is largely transactional in nature, and of short duration. In contradistinction to acute-care (inpatient) nurses, patient encounters with ambulatory care (outpatient) nurses can occur repeatedly over an extended period of time, but tend to be brief and discontinuous (Swan, 2007) and may entail considerable indirect interactions (Cusack, Jones-Wells, and Chisholm, 2004); a job profile descriptive of lower contact intensity than the duties of inpatient nurses. To date, we are aware of no studies that have examined or described subgroups of clinical practitioners employed in hospitals with respect to contact intensity; however, it is reasonable to argue that the nature of work performed by physicians and therapists generally demands less frequency and intimacy of contact with patients than is required by nurses. Guided by direct findings and inferences from previous research, the following research questions are proposed and tested: Research Question 1: Do perceptions of linkage model variables – specifically, workplace climate, job conditions, personal work outcomes and organizational service outcomes − vary significantly across occupational groups of health service professionals? Research Question 2: Are perceptions of linkage model variables – specifically, workplace climate, job conditions, personal work outcomes and organizational service outcomes − meaningfully ordered across occupational groups of health service professionals as a function of increasing customer-contact intensity, as follows: support staff, clinical practitioners, outpatient nurses, and inpatient nurses?

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METHODS The data for this project were obtained from multiple sources made available by the Veterans Healthcare Administration (VHA), including a national survey of employees, existing internal patient volume and caseintensity measures, and other archival data. There were 74,662 responses to a confidential and self-administered survey questionnaire (72% response rate) representing employees in 147 VHA medical centers across the United States. The survey asked for employee observations and opinions on a wide variety of topics surrounding their work experiences. However, we confined our analyses only to those facilities for which employee survey data could be reliably paired with other facility data. Initially, this yielded usable data from 113 VHA facilities; specifically, responses of 59,464 employees. Due to concerns over anonymity and confidentiality by the VHA, the data collected by them and made available to us did not include demographic variables or information that would permit us to trace employees to their specific work units within their respective facilities. Variables and Measures Dependent variables. To capture the several streams of research forming the theoretical foundation for this study, we classified the dependent variables into four related subsets of measured employee perceptions: 1) workplace climate, 2) job conditions, 3) personal work outcomes, and 4) service outcomes. Measures of organizational outcomes (e.g. financial or market performance) were unavailable and, in any case, would not be different across occupational groups. Given that the study data are drawn from a convenience sample, our selection of measures to operationalize the concepts of interest in this study was reliant on and limited to survey questionnaire items designed

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by the VHA and administered to its employees. Respondents replied to VHA employee survey items using a five–point Likert-type scale (anchored at 1 = strongly disagree, 5 = strongly agree except where otherwise noted) for all measures except role alignment, workload balance and job control, which utilized a 4-point semantically anchored scale. Where feasible, we created reliable multiitem constructs by averaging employee responses to constituent questions into a single scale. The internal consistency (reliability) of multi-item measures was estimated by Cronbach’s coefficient alpha (α), using α ≥ .70 as our level of sufficiency for basic research (Nunnally, 1978). The study’s dependent measures are discussed below and summarized in Table 2 along with their reliability coefficients (where appropriate). Work Climate. We used two measures to assess employee perceptions of the overall work climate: high performance work systems (HPWS) and customer orientation. HPWS was measured using a composite scale comprising ten factor-analytically derived indicators (goalalignment, communication, involvement, empowerment, teamwork, training, trust, creativity, performance enablers and performance-based rewards) extracted from the VA employee survey that has been previously tested and validated (see Harmon, et al., 2003, for a fuller explication of how this scale was derived and validated through a series of confirmatory factor analyses). We measured customer orientation by averaging into a single scale responses to three items that assessed the degree to which employees believed that their organization was geared towards accommodating its customers: (1) “Products, services and work processes are designed to meet customer needs and expectations,” (2) “Customers are informed about the process for seeking assistance, commenting, and or complaining about products and services,” and (3)

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“Customers have access to information about products and services.” Table 2 Dependent Measures used in Analyses Dependent Variables Work Climate High-Performance Work Systems (Cronbach’s alpha = .91) 1 = Strongly Disagree, 5 = Strongly Agree

Customer Orientation (Cronbach’s alpha =.83) 1 = Strongly Disagree, 5 = Strongly Agree

Job Conditions Task Clarity 1 = Strongly Disagree, 5 = Strongly Agree Role Alignment Workload Balance (Cronbach’s alpha = .76) 1 = Strongly Disagree, 4 = Strongly Agree Work-life Balance 1 = Strongly Disagree, 5 = Strongly Agree Job Control (Cronbach’s alpha = .84) 1 = Strongly Disagree, 4 = Strongly Agree Personal Work Outcomes

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Survey Questions A composite of ten factor-analytically derived indicators (goal-alignment, communication, involvement, empowerment, teamwork, training, trust, creativity, performance enablers, and performance-based rewards) extracted from the VA employee survey that has been previously tested and validated (see Harmon, et al., 2003, for a fuller explication of items and how this scale was derived and validated through a series of confirmatory factor analyses). A three-item scale previous tested and validated by Scotti, et al, 2007: 1. “Products, services and work processes are designed to meet customer needs and expectations.” 2. “Customers are informed about the process for seeking assistance, commenting, and or complaining about products and services.” 3. “Customers have access to information about products and services.” “Employees are kept informed on issues affecting their jobs.” “I am free from conflicting demands that other people make on me.” 1. “I have too much work to do everything well.” (reverse coded) 2. “I have enough time to get the job done.” “Supervisors/team leaders understand and support employees’ family/personal life responsibilities.” 1. “It is basically my own responsibility to decide how my job gets done.” 2. “I am given a lot of freedom to decide how to do my own work.”

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Table 2 Dependent Measures used in Analysis (cont’d) Work Stress (Cronbach’s alpha =.86) 1 = Strongly Disagree, 5 = Strongly Agree Perceived Service Capability 1 = Strongly Disagree, 5 = Strongly Agree Employee Satisfaction (Cronbach’s alpha = .87) 1 = Very Dissatisfied, 5 = Very Satisfied Dependent Variables Intentions to Stay 1 = Very Likely , 5 = Very Unlikely Perceived Service Outcomes Employee-Perceived Service Quality (Cronbach’s alpha = .87) 1 = Very Poor, 5 = Very Good

Perceived Customer Service Quality 1 = Very Dissatisfied, 5 = Very Satisfied

1. “I often feel tense and stressed on my job.” 2. “Work is a source of a great deal of stress.” 1. “Conditions in my job allow me to be as productive as I can be.” 1. “Considering everything, how satisfied are you with your job?” 2. “Considering everything, how would you rate your satisfaction with the organization at present time?” Survey Questions “How likely are you to leave your current work unit for another federal job within the next two years?

1. “Overall, how would you rate the quality of service provided to veterans by your facility or office?” 2. “Overall, how would you rate the quality of care provided at this health care facility?” 3. “Compared to a year ago, how would you rate the quality of care patients receive at your health care facility?” “How satisfied do you think your organizations customers are with the products and services it provides?”

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Job Conditions. It should be noted that our conceptualization and operationalization of job conditions in the present study preserves the original framing of the VHA survey questionnaire and is consistent with the emerging field of “positive organizational behavior” (Luthans, 2002; Cooperrider & Whitney, 2005). At the core of this perspective is application of affirmative interventions to unleash human capacities that can be developed and managed for performance improvement. Accordingly, we have exchanged negative constructs (i.e., role ambiguity, role conflict, role overload, and work-family conflict) for positive constructs such as task clarity, role alignment, workload balance and work-life balance.

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We measured Task clarity by responses to the item: “Employees are kept informed on issues affecting their jobs.” Role alignment was measured by responses on a 4point scale to the question “I am free from conflicting demands that other people make on me.” We measured Workload balance by averaging employee responses to two survey questions on a 4-point scale: (1) “I have too much work to do everything well” (reverse coded), and (2) “I have enough time to get the job done.” Work-life balance was measured by the responses to the question: “Supervisors/team leaders understand and support employees’ family/personal life responsibilities.” We measured job control by averaging employee responses to two survey questions on a 4-point scale: (1) “It is basically my own responsibility to decide how my job gets done,” and (2) “I am given a lot of freedom to decide how to do my own work.” Personal Work Outcomes. We measured employee perceptions of their Service Capability by responses to a global survey question: “Conditions in my job allow me to be as productive as I can be.” Work stress was measured by averaging the employee responses to two survey questions: (1) “I often feel tense and stressed on my job,” and (2) “Work is a source of a great deal of stress.” We measured employee satisfaction by employee responses to two survey questions: (1) “Considering everything, how satisfied are you with your job?” and (2) “Considering everything, how would you rate your satisfaction with the organization at the present time?” (1=very unsatisfied to 5=very satisfied). We measured turnover intentions by employee responses to the question: “How likely are you to leave their current work unit for another federal job within the next two years” (1=very likely to 5=very unlikely). The original coding of the questions results in a positive operational measure of intent to stay. Service Outcomes. We measured employee perceptions of service quality with a three-item scale derived

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from the employee satisfaction survey reflecting their ability to deliver high-quality customer service at their workplace. The three items were: (1) “Overall, how would you rate the quality of service provided to veterans by your facility or office?” (2) “Overall, how would you rate the quality of care provided at this health care facility?” and (3) “Compared to a year ago, how would you rate the quality of care patients receive at your health care facility?” Respondents were asked to rate each of the three items using a 5-point scale (1=very poor to 5=very good). We measured employee perceptions of customer satisfaction at their facility by the question “How satisfied do you think your organizations customers are with the products and services it provides?” using a 5 point scale (1=very dissatisfied to 5=very satisfied). Independent and control variables. Contact intensity is designated as the independent variable in this study. Individuals were assigned to one of the four categories of professional health service workers using their responses to a combination of demographic survey questions indicating their job category, clinical specialty, and service line. The resulting four occupational groups, each posited as having successively more intense (direct, frequent, intimate) service encounters with patients, are as follows: 1) support staff (trained professionals who are not directly involved in delivery of care − e.g., IT, HR, procurement, engineering, finance, accounting, program management, medical records administration, laboratory); 2) clinical practitioners (licensed professionals directly involved in diagnosis and delivery of clinical treatments − e.g., physicians, dentists, therapists); 3) outpatient nurses (usually working in ambulatory care settings), and 4) inpatient nurses (usually working in acute care settings). Wage grade workers were excluded from our analyses. The number of professional workers that could be reliably assigned was 38,821 –of

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which 14,025 (36%) were support staff, 14,044 (36%) were clinical practitioners, 4,929 (13%) were out-patient nurses, and 5,823 (15%) were in-patient nurses. Patient volume and case-mix intensity (CMI) were included as potential control variables that might exert influence on the perceptions of healthcare professionals included in this study. A recent study of caregivers in operating rooms within VHA hospitals found that the volume and relative difficulty of cases treated are important determinants of facility complexity and influenced worker perceptions of organization climate (Carney et al., 2010). Patient volume counts the total number of patients served by each medical facility and reflects the size of the organizational unit. Larger organizational entities are likely to be more formally structured, governed by standardized practices, and be able to afford greater flexibility in work scheduling due to “slack” resources, which together may well influence, for example, perceptions of task clarity, workload balance, and stress. Case mix intensity accounts for differences in the acuity and complexity of care delivered to patients. The VHA categorizes patients into one of 94 patient classes that reflect treatment resource intensity regardless of setting (i.e., inpatient or outpatient) and assigns a relative value to each class derived from national VA data of all patients in all classes. Multiplying the total number of patients by the treatment intensity value for each patient (effectively a weighted patient or risk-adjusted patient) produces the “weighted units of work” for each facility. We calculated a case-mix intensity ratio (CMI ratio) by dividing the facility total weighted units of work (numerator) by the total number of actual patients. Thus, we have a single ratio that can be compared across the system. For the 113 facilities in the study, a higher ratio indicates a facility with a more intense patient case mix. A more resource-intensive and riskier patient load is likely to associated with a more highly-trained, specialized and well equipped workforce

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faced with challenging cases, which together may well influence, for example, perceptions of service capability and satisfaction. Data analyses were conducted using the SPSS Version 19.0 software package, and propositions of crossgroup differences were tested using Multivariate Analysis of Covariance (MANCOVA) and univariate Analysis of Variance (ANOVA). Initially, we conducted a full set of analyses at both the individual and facility levels. For individual-level analyses, we simply used the scores on each measure for every individual in each professional category. For facility-level analyses, we aggregated survey data by averaging the responses of individuals in each occupational group within each of the 113 VHA facilities to produce separate facility-level group scores on each measure. In both sets of analyses, survey-based measures of employee perceptions were matched with each facility’s patient count and case-mix intensity (CMI) ratio. The patterns of cross-group differences revealed by the individual and facility-level analyses were virtually identical. We chose to report the results only at the facility level for two key reasons. First, the findings will be more conservative due to reduced sample size (113 facilities versus more than 38,000 individuals), which is in keeping with the largely exploratory nature of our inquiry. Second, using facility-level scores allows us to partially offset a myriad of unmeasured, but potentially important, contextual differences that influence groups across very diverse VHA locations; such as variations in re-organization history, organization culture and leadership, geographic culture, and economic conditions. Thus we would be better able to answer this study’s key question: In what ways do different professional occupational groups within the same local facility context have differing perceptions and evaluations of that context?

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RESULTS

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Table 3 reports the means and correlations of the measures used in this study. Preliminary analyses showed significant effects of patient count and CMI on most study measures. As can be seen in Table 3, both measures were significantly correlated with greater workload balance and role alignment, and reduced work stress. Healthcare professionals in facilities with more intense case mixes perceived greater job control and task clarity, and reported stronger service capability and satisfaction. A departure from this pattern is that greater patient volumes were associated with weaker employee intentions to stay in their work unit. However, for the most part, healthcare professionals appear to have had more positive perceptions of their work environment in larger and/or more challenging facilities. We also found significant effects of both patient volume and CMI as covariates in each of the multivariate models testing group differences discussed below. More specifically, volume was a significant predictor of group differences for three categories of employee perceptions − job conditions, personal work outcomes, and service outcomes (all Pillai’s Trace Fs > 4.1, p < .002, eta2 > .03; eta2 is equivalent to an R2 measure of explained variance). Case mix also was a significant predictor of group differences for three categories of employee perceptions – overall workplace climate, job conditions, and personal work outcomes (all Pillai’s Trace F > 2.4, p < .002, eta2 > .04). However, although the above analyses confirmed the importance of controlling for volume and case mix, the introduction of these control variables did not alter our findings with respect to any of the specific effects of job contact intensity on study measures; the focus of our investigation. Thus, for simplification, these two control variables are omitted from further reporting.

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To avoid spurious findings attributable to our relatively large number of variables, we first used multivariate analysis of covariance (MANCOVA) to test for group differences across the variables within each set of dependent measures – workplace climate, job conditions, personal work outcomes, and perceived service outcomes (relying on Pillai’s Trace criterion due to a statistically significant Box’s M test indicating unequal variance of covariance matrices across groups). Where a significant multivariate effect for the contact-intensity job types was confirmed, we then performed univariate ANOVAs to identify the specific variables that contributed to the multivariate effect. Where significant univariate effects were found, we conducted post-hoc tests on the independent (grouping) variable to determine which professional job types differed significantly in their perceptions of that dependent variable (using Dunnett’s C as a conservative test of pairwise comparisons across job classifications because Levene’s test indicated unequal error variances for almost all our dependent variables)..

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Testing of Research Questions With respect to Research Question 1, we found significant and, in some cases strong, multivariate effects affirming that contact-intensity job types (support staff, clinical practitioners, outpatient nurses, inpatient nurses) are differentially distributed in regard to their perceptions on the sets of measures comprising workplace climate (Pillai’s Trace = 6.2, p < .001, eta2 > .04), job conditions (Pillai’s Trace = 20.1, p < .001, eta2 > .19), personal work outcomes (Pillai’s Trace = 15.3, p < .001, eta2 > .12), and service outcomes (Pillai’s Trace = 7.4, p < .001, eta2 > .05). As shown in Table 3, significant univariate ANOVA effects (F values > 3.8, p < .01) were found for every dependent measure except workload balance, with effect sizes (R2) ranging from 3% to 35% variance explained. Post hoc statistical analyses were performed to establish discrete differences between the higher-intensity inpatient and outpatient nursing groups versus the lowerintensity groups of technical support staff and clinical practitioners as indicated by the subscript notation in Table 4. Several noteworthy differences were evidenced across the spectrum of healthcare service professionals:  Technical support staff clearly were most favorable in their perceptions of work-life balance, job control, and service quality, yet were also much less likely to stay in their work units.  Clinical practitioners rated job role alignment significantly more favorably than other groups of professionals. They also report being the least stressed and most likely to stay in their work unit.  In general, nurses registered the least favorable perceptions across the array of dependent variables we studied. In particular, nurses had significantly less positive readings of role alignment and job control. Viewed as a group, nursing professionals,

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



especially inpatient nurses, were significantly more stressed than clinical practitioners. Work-life balance warrants special attention. This was the only dependent variable on which all groups were significantly different from one another. As a group, nurses reported the lowest appraisals of worklife balance and inpatient nurses perceived significantly less work-life balance than outpatient nurses. Regarding perceptions of service outcomes, support professionals viewed service quality most favorably and clinical practitioners believed customers were the most satisfied; however, no remarkably distinct differences were observed.

The results in Table 4 also reveal a pattern that in most instances indicates a monotonic ordering of the cohorts when inpatient and outpatient nurses are combined: perceptual favorability decreases as contact intensity increases. This observation lends substantial support to the proposition set forth in H2 that our classification of service professionals according to customer contact intensity is not merely categorical, but behaves as a meaningfully ordered measure ranked from low to high in following succession: 1= support staff, 2=clinical practitioners, 3= nurses.

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1

Table 4

2 3

Analysis of Variance Results for Professionals Grouped by Customer Contact Intensity. Clinical Practitioners N=113

Outpatient Nurses N=113

Vars

Support Staff N=113 facilities Mean s.d.

Mean

s.d.

Mean

s.d.

Mean

s.d.

Work Climate HPWS

3.04 a

3.00 a,b

.22

2.93 b,c

.29

2.86 b,c

.30

.20

Inpatient Nurses N=113

R2 / Sig

.07 ***

Customer Orient. Job Condit. Task Clarity Role Align: 4 pts Wkload Bal.: 4pts WorkLife Bal. Balance Job Control: 4 pts

3.66 a

.18

3.62 a,b

.16

3.58 b

.25

3.59 b

.21

03 **

3.18 a

.21

3.16 a

.26

3.11 a,b

.34

3.03 b

.35

2.26 a

.13

2.37 b

.12

2.25 a

.22

2.21 a

.18

.04 ***

.11 ***

2.39

.14

2.41

.14

2.36

.21

2.37

.17

3.54 a

.18

3.45 b

.22

3.27 c

.35

3.12 d

.33

2.94 a

.10

2.80 b

.11

2.69c

.19

2.68 c

.17

.02 ns

.25 *** .35 ***

Personal Work Outcomes

Work Stress Service scale Capab Emp. Sat

3.30 a,b

.15

3.26 a

.17

3.34 b,c,

.28

3.41 c

.26

.06 ***

3.25 a

.18

3.10 b

.20

3.12 b

.31

3.05 b

.26

.09 ***

3.51 a

.17

3.50 a

.18

3.48 a

.27

3.38 b

.29

.05 ***

Intent to Stay Service

3.32 a

.24

3.64 b

.21

3.61 b

.33

3.60 b

.26

.19 ***

Outcomes

4 5 6 7

Service 3.73 a, .25 3.66 a,b .23 3.62 b .29 3.58 b .26 .04 *** Quality Customer 3.84 a,b .21 3.89 b .18 3.79 a .27 3.79 a .26 03 ** Sat Note: * denotes ANOVA p < .05, ** denotes p < .01, *** denotes p 55.7 and R2 > .49 for both measures). Moreover, perceived service quality was a significant predictor of lower average treatments costs across facilities (using a measure of treatment cost efficiency previously reported in Harmon et al (2003); F 6.0, R2.04). We also found that the overall workplace climate measures (HPWS and customer orientation) jointly predicted a significant amount of variation in: 1) all our measures of job conditions (ranging from F > 8.4 and R2 > .12 for workload balance to F > 208 and R2 > .79 for task clarity), 2) all our measures of personal outcomes except intent to stay (Fs > 21.5 and R2 > .27), and 3) both measures of service outcomes (both Fs > 55.7 and R2 > .49). Further, the five job condition measures collectively predicted a significant amount of variation in: 1) each personal outcome measure (ranging from F > 2.4 and R2 > .06 for intent to stay to F > 47.2 and R2 > .67 for satisfaction), and 2) both measures of service outcomes (both Fs > 11.3 and R2 > .32). Finally, we found the personal outcome measures to collectively be an even stronger predictor of service outcomes than those for job conditions (both Fs > 20.0 and R2 > .39).

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ORGANIZATIONAL RESTRUCTURING, GOVERNMENT CONTROL AND LOSS OF LEGITIMACY FOLLOWING AN ORGANIZATIONAL CRISIS: THE CASE OF ISRAEL’S NONPROFIT HUMAN SERVICES RITA MANO University of Haifa DENNIS ROSENBERG

ABSTRACT The study explores organizational restructuring following the occurrence of a crisis. Restructuring activities following an intervention are considered here to be indicators of an organization’s loss of legitimacy because they have lost their independent status, a basic characteristic of nonprofit human settings. The study shows that according to the Resource Based View of organization restructuring – experienced as downsizing, neglecting and abandoning of projects – organizations are affected by (a) government intervention in decision making; (b) higher demands for accountability; and (c) higher evaluations of performance gaps. On the basis of the study of a sample of 138 Nonprofit Human Services in Israel, the results show that the higher the level of restructuring, the higher the level of legitimacy. However, organization location in metropolitan areas moderates the link between restructuring and legitimacy loss. We conclude that Israel’s nonprofit human services being overly dependent on government funding are more prone to restructuring and losing legitimacy following organizational crisis.

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INTRODUCTION An organizational crisis is defined as the occurrence of events and circumstances that (a) threaten the organisation’s survivability (Greening & Johnson, 1996; Nystrom & Starbuck, 1984), (b) surprise (Rudolph & Repenning, 2002), and sometimes (c) limit the time and resources needed for coping with the threat (Scheaffer & Mano-Negrin, 2003). The occurrence and recognition of a set of events as a crisis is viewed as a disruptive and challenging aspect of organizational routines (Pauchant & Douville, 1992). Crises have far-reaching repercussions in organizations (Elliott & Smith, 2006; Estes & Alford, 1990; Pajunen, 2006; Walshe et al., 2004). Following the occurrence of an organizational crisis, the vision, values, goals and management principles that form the basis of its legitimacy need to be reevaluated (Medley & Akan, 2008). While there is current evidence of such crises occurring in Israel’s nonprofit human services (hereafter NHS). There is, however, less evidence of the way a crisis affects organizational restructuring in NHS and to what extent government involvement is to be considered as the source for unexpected organizational changes. The present study examines to what extent government funding affects NHS practices causing organizational restructuring following a crisis. Drawing upon the literature on crises and NHS dependency on government funding we seek to show that discussing whether government’s support in Israel’s NHS can lead to a loss of legitimacy. We contend in the study that crises generate the need to exercise higher government control over organizational processes in NHS because of their dependency on government funding. This dependency may undermine the independence level of nonprofit human services in organizational decision making. We consider here that organizational restructuring due to government

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pressure indicates legitimacy loss because nonprofits’ autonomy is essential to their potential to pursue social goals without interference from outside pressures. The state in Israel supports a social welfare policy, but uses “outsourcing” as well. Nonprofit organizations are willing to be provide the requested services, but they need to comply to government standards even if their independent status enables them a certain degree of autonomy. This level of autonomy is seriously affected after crisis, when the government claims more control. In these cases, we claim legitimacy loss is possible since nonprofit organizations are forced to go through restructuring that is an imposed. We are aware that this is not necessarily a problem in other western settings, where a high level of government support on nonprofits is provided. Yet, the study raises the possibility that this is a trend that alters the meaning of nonprofit provision of services as an effective alternative to the government red-tape provision of social services. Change and restructuring are major mechanisms for adapting to environmental pressures (Baum & Amburgey, 2005). Organizations strive to acquire the resources necessary for their existence and to prove their indispensability to society. Fear of non-survival in NHS is mainly related to (a) privatization processes that reduce levels of funding from public stakeholders, and (b) decreased philanthropic activity over the last two decades (Hodge & Piccolo, 2005; Gidron & Katz, 2005). This is probably why Gummer (2001) suggests combining a “small” organizational change connected to economic initiatives, drastic cutbacks, decreased organizational capacity, and structural change, with a “soft” one related to investment in organizational environment, changing organizational culture by means of feedback from the environment, and by including environment in planning forthcoming changes (Kraatz & Zajak, 2001; Balser & McClusky, 2005; Pajunen, 2006). Organizations consider and introduce changes

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including restricting of operations for at least one of four reasons (Nanus & Dodds, 1999): reaction to customers’ needs; political, social and economical forces; the need for greater effectiveness; and satisfying the environment and stakeholders (Beitler, 2003; Galaskiewicz & Bielefeld, 1998). The single major source of change is performance and striving for performance necessitates detailed reporting (Mano & Hareven, 2007). Such inspections are common for both public and private stakeholders. Regarding public stakeholders NHS must provide frequent and reliable reports to ensure that social goals are attained within the limits of an allocated budget (Keating & Frumkin, 2003) but also in regard to professional standards (Gazley, 2010). To private stakeholders, greater transparency is expected (Brown & Moore, 2001; Ebrahim, 2005) for different areas and functions of NHS activities because “technical” measures of performance are important to private stakeholders (Baruch & Ramalho, 2006). According to Hades and Mockenhaupt (2000), inspections from private stakeholders—ranging from regulations, introduction of customer service evaluations and up to ongoing financial reports—cause significant changes in the organizational mission since more professional employees are needed to cope with the demands (Kraatz & Zajak, 2001). For NHS imposed changes from “outside” may mean losing autonomy, and losing autonomy indicates the possibility of a loss in legitimacy. Organizational legitimacy has been addressed by sociologists as a crucial component for the survival of social units. Early works defined organizational legitimacy as the outcome of the congruence between the values associated with the organization and the values of its environment (Dowling & Pfeffer, 1975). When the organizational environment is not satisfied with organizational performance it generates challenges in funding or political/cultural support and organizations need

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to adequately address these challenges. An organizational crisis (term used interchangeably with the term, crisis) indicates the occurrence of these challenges and draws attention to organizational faults (Richardson, 1995; Mordaunt, 2006). In such situations, organizations are expected and often compelled to conform to higher external control to obtain/retain/regain environmental support (Richardson, 1995). Government funding in Israeli NHS often rises to 70% (Schmid, Bar & Nirel, 2008), higher than that in the USA (De Vita, 2006), and similar to The Netherlands, Canada, Belgium and Ireland (Salamon, 2006; Gidron & Katz, 2005). At the same time NHS are expected to become more independent and successful in raising funds from private sources. Private/autonomous sources though amount at best to 35% and only 10% come from philanthropy/donations. Thus despite expectations for lower dependency on government funding, being unable to seek necessary levels of autonomous funding created a state of crisis in the level and possibly quality of social services. Higher levels of dependency on government support raise the possibility of losing control over internal processes, i.e. legitimacy loss (Daft, 2004; Meyer, 1994). In the present study, we define legitimacy loss as the practices of organizational restructuring and changes in management priorities following the occurrence of crisis and test empirically the link between legitimacy loss and a set of variables related to the degree of government intervention. We also consider the effect of organizational context – age, size and location – because government funding and the occurrence of organizational crises in Israel’s NHS are often related to organizational characteristics.

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THEORETICAL BACKGROUND Organizational theory has produced a considerable number of studies relating to the way organizations address their environment (Daft, 2004). Organizational legitimacy emphasizes how social, economic and cultural agents support organizational goals. Deephouse and Suchman (2008) in their lengthy appraisal of legitimacy literature show how in Weber’s analysis of legitimacy, different authority types reflect conformity with both general social norms and formal laws in a rational way (Weber, 1946). Later studies on the rationality of the social systems led to the confirmation that organizational legitimacy reflects the congruence between the organization’s goals and values and the environment’s social laws and norms (Dowling & Pfeiffer, 1975; Pfeffer & Salancik, 1978). The concept of legitimacy has been further expressed by Powell and DiMaggio (1991) who stated that organizations need to act and react upon preexisting sets of beliefs and rituals. Likewise, Suchman (1995) determined that legitimacy has a wide theoretical background on which all “normative and cognitive forces that constrain, construct and empower organizational actors” are activated (p. 571). For NHS, organizational legitimacy comes from different agents such as providers of services/goods, recipients of services, the society, population, who may disagree on management processes in the provision of services (Cho & Gillespie, 2006; Smith & Gronberg, 2006). The most important stakeholder to Israel’s NHS is the government, which funds the provision of social services. However, such support shapes and contributes to restructuring in organizational objectives (Balser & McClusky, 2005; Galaskiewicz & Bielefeld, 1998; Pajunen, 2006). Higher levels of involvement necessitate more procedures that increase credibility and restructuring is the single major mechanism for adapting to environmental pressures (Baum &

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Amburgey, 2005). This is even more pronounced (a) when the government is involved in management practices and (b) following the occurrence of a crisis. It is then that loss of legitimacy is strongly experienced in NHS. Two major perspectives can be applied to predicting restructuring and loss of legitimacy. The first is the institutional approach, which suggests that NHS focus on the social and cultural frameworks in which they operate (DiMaggio & Powell, 1988; Cooney, 2006). They observe environmental signals (through various agents) which indicate whether organizational legitimacy is in favorable or unfavorable conditions. By contrast, the second approach, the Resource Based View, focuses on “technical” aspects. It emphasizes the organization’s ability to obtain and safeguard the resources vital for its existence (Pfeffer & Salancik, 1978). This economic dimension relates to the development and optimal use of resources so that costs are minimized and profits are higher than expenses. When NHS undergo through an organizational crisis, it can be the result of either economic or social aspects. Formal reports from the Ministry of Justice (2009) provide evidence of NHS crisis: (a) out of 1360 new NHS established in 2006, 379 fell under the category of NHS being filed to be in the “process of verifying complaints”; (b) for 455 NHS there were placed complaints; (c) 141 NHS were under full investigation by the registry’s controller because of improper management practices (Ministry of Justice, 2009). The occurrence of crisis, here broadly defined as the occurrence of unexpected circumstances, generates considerable pressure to adhere to government expectations and to "fit" within the larger social policy of NHS funding (Gidron & Katz, 2005) to ensure stable support (Cho & Gillespie, 2006; Smith & Gronberg, 2006), even at the danger of lower autonomy in management in the provision of services. In the present paper, it was not possible to tap into the source of crises, and a generalized definition is

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employed describing crisis as any unexpected set of circumstances in organizational inputs, processes and outcomes. This description enables to capture the organic way of doing things in nonprofits where even small and seemingly insignificant changes may have severe implications for management. If the reason for the crisis is economic, for example, then efficiency levels, measuring the extent to which goals and processes are financially acceptable, affects the quality and quantity of products and services. Similarly, if the crisis is generated because NHS performance is deteriorating: if processes and outcomes (projects, services, and products) are not suitable for the groups currently getting services, then lack of effectiveness is the root of the crisis, which reduces economic support and hence efficiency. Moreover, changes in taxing or monetary systems lead to a growing understanding of the need for performance improvement (Carlson, Kelley, & Smith, 2010) and collaboration (Gazley, 2010) to reduce disagreements and enable change in NHS (Child & Gronberg, 2007; Cho & Gillespie, 2006). Higher levels of support in government funding necessitate procedures that increase credibility and reduce the need for change (LeRoux & Wright, 2010). When a crisis occurs, NHS will possibly be subject to government inspections and intervention to restore balance because for NHS the government acts as a central stakeholder or “gatekeeper“ (Ben-Ner & Van Hoomissen, 1991). The government/nonprofit is a complex relationship (Abzug, 2007; Child & Gronberg, 2007; Cho & Gillespie, 2006; De Vita, 2006; Golensky & Mulder, 2006; Ju, 2008; Salamon, 2006; Smith & Gronberg, 2006; Schmid, Bar & Nirel, 2008). Government changes in policy, reflecting changes in historical contexts and political influences all affect the level of funding (Cho & Gillespie, 2006; Smith & Gronberg, 2006). The government is considered to be a “primary” and “high-demand” stakeholder (Ben-Ner & Van Hoomissen, 1991) in NHS because it has a “monopoly” over

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resources and funds, and its level of “discretion” over fund allocation” is high (Schmid, Bar & Nirel, 2008, p. 583). In their pioneer analysis of Israel’s government-nonprofit sector relationship, Gidron, Kramer and Salamon (1992) presented four types of relationships. Two types assign full responsibility to either the government or a third sector for the provision of services. The third type is typical when the government and the third sector both provide services, and there is little interaction between the providers. The fourth type is the one that enables the third sector to provide services on behalf of the government, which is, however, the main funder of the services. Indeed, according to Schmid, Bar and Nirel (2008), dependency on Israel's government budget reduces NHS autonomy, and the need to fit imposed criteria of performance and efficiency are evident (Ramanath, 2009; Ju, 2008). The levels of control may vary, though, and exerting power or imposing the stakeholders’ expectations onto the organization may take different forms. NHS compliance to government ranges from simple, informal regulations to complex formal practices. Both informal and formal procedures aim to increase: (a) accountability processes (Alexander, Brudney & Yang, 2010) and (b) performance levels (Mano, 2010; 2011). In the present study, the following forms of NHS dependency on government are examined: government influence, involvement in decision making, accountability and performance gaps. Government Influence Influence over NHS practices is relatively mild on the part of the primary funder. As the main representative of public needs, the government expects NHS to comply with regulations. Government influences became obvious when NHS entered a state of crisis due to the convergence of three complementary trends in government/NHS relationships: (a) the government adhered to proper management practices

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(Mano, 2011) and used economic/financial measures of organizational performance, often at the expense of social goals (Schmid, Bar & Nirel, 2008); (b) the government has expanded internal control on NHS. As crisis instances increased, so did government influence prevail (Elbers & Shulpen, 2011; Andreassen, 2008) redefining goals and management practices. In Israel’s NHS, government influence is experienced mostly in terms of: budget cuts in funding, shortened time allocated to – or total cancellation of – projects; changed priorities on type/number of beneficiaries for specific program or requests; instructions are given to cooperate with institutional agents, introducing new/additional forms of accountability, and performance monitoring, etc. Accordingly, we formulate the following hypothesis: H1: Higher government influence on NHS will be related to higher loss in legitimacy. Government Involvement in Decision Making Government involvement in decision making pertains more to organizational processes. It includes blocking a partner’s potential to express his opinion, not allowing the partner’s issues to be included in the agenda, directing the organizational norms and values, rules and processes, and imposing the organizational context of decision making by adding or removing authority levels, etc. (Abzug, 2007; De Man & Roijakkers, 2009). For example, it can lead to a situation in which the NHS provide services to population groups which are not necessarily their target audience, but the ones that are important to the government (Golensky & Mulder, 2006). Other outcomes include a decline in the quality of service (Cho & Gillespie, 2006) and lower levels of social activism or attempts to seek alternative sources of revenues (Schmid, Bar & Nirel, 2008). Additional aspects that affect NHS activities are regulations concerning

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the recruitment of volunteers, feasibility of projects, and costs of programs. They often lead to the “shrinking” or even termination of previous services obtained (Child & Gronberg, 2007; Andreassen, 2008) and are the cause for (a) the “goal displacement” process, and (b) restriction in the level of creative solutions for the provision of social services. These changes often signal a distancing from the norms and expectations of their normative environment (Ju, 2008; Guo & Acar, 2005), and as such, government regulations become harmful to NHS legitimacy (Brown & Troutt, 2004). Accordingly, it is expected that: H2: Higher government involvement in decision making will be related to higher loss in legitimacy. Accountability In a generalized form, accountability serves to “verify that one has met agreed-on expectations” (Benjamin, 2008, p. 206). The decline in governmental expenditures, along with broader requirements for standardized processes, has increased expectations for accountability (Golensky & Mulder, 2006; Mordaunt, 2006), expecting NHS to put a greater emphasis on efficiency issues (Salamon, 2006; Brown & Troutt, 2004). A more specific definition appears in Ebrahim (2005, p. 58), who defines accountability as “the means by which individuals and organizations report to a recognized authority (or authorities) and are held responsible for their actions”. Some forms of accountability are clearly expressed, such as reporting via the tax institutions and formal authorities, but others, like “serving the public good” (p. 295), are more complicated (Balser & McClusky, 2005). All forms of accountability place heavy demands on NHS resources (Balser & McClusky, 2005; Mordaunt, 2006; LeRoux & Wright, 2010), but their usefulness is disputed. Carman and Frederick (2008), for example, report that while 67% of NHS measure performance regularly or refer to best

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practices and benchmarking, it is not clear whether being “in line” with requested procedures also produces more efficient programs or increases legitimacy (Carman, 2007, Gazley, 2010). In fact, this may be destructive when there is a shortage of staff and available resources because it prevents attending to core activities (Luft, 2009; Carlson et al., 2010). This is why some authors distinguish between types of accountability (Leat, 1990; Ebrahim, 2005). Recently, Elbers & Schulpen (2011), examining a list of topics brought up in an NHS decision making process, pointed to differences in accountability practices and observed different trends. Explanatory accountability, for example, provides necessary information to balance disturbed relationships between stakeholders and “reestablish” legitimacy following erroneous actions (Leat, 1990; Benjamin, 2008), but it does not provide more than the bare minimum of information about processes. This is why it is necessary to distinguish between accountability for revenues and accountability for expenses. Accordingly, it is expected that: H3a: Higher degrees of revenue accountability will be related to a higher loss in legitimacy and, H3b: Higher degrees of expense accountability will be related to a higher loss in legitimacy. Performance Gaps Various performance measures have been introduced (for a review, see Paton, 2003) including best practices, benchmarking and outcome measurement, etc. Very often though, issues of defining criteria of performance arise, stemming from disagreements among stakeholders (for a review, see Alexander et al., and 2010). Baruch and Ramalho (2006) for example, show how some stakeholders use objective, while others use subjective, measures of

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performance. Alexander et al.’s (2010) review accents the change of focus from historic, social and political considerations to institutional and technical measures of performance creating dilemmas among NHS managers. These managers feel forced to find ways to accommodate expectations for measurable, rather than successful, processes and outcomes. Schmid, Bar and Nirel (2008) show that higher dependence on governmental funding causes NHS to be less active in political advocacy, thus generating performance gaps between supporters and customers. NHS develop structures so they can “fit” the imposed criteria of performance defined from external stakeholders and strive to increase accountability and efficiency (Balser & McClusky, 2005; Ramanath, 2009; Ju, 2008). In times of organizational crises performance gaps are amplified even more and the need to adjust come to the fore (LeRoux & Wright, 2010; Moxham & Boaden, 2007; Mano, 2010; 2011). It is therefore expected that: H4: Higher performance gaps will be related to higher loss of legitimacy. Complexity Variables Traditional organizational theory asserts that organizations emerge, develop and survive in response to their organizational environment (Pfeffer & Salancik, 1978). The interface between organizational activities and the "task" environment defines the organizational context (Daft, 2004). Child (1973), in his pioneer study on a variety of organizations, concluded that “size with technology, location and environmental variables, predicts complexity and complexity cannot be satisfactorily predicted or fully understood without reference to the economy of scale” (p. 168). This is why context variables and complexity are often used interchangeably (Perrow, 1979). Accordingly, crisis studies insist that organizational contexts are important in

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the detection of and coping with crisis (e.g., Richardson, 1995; Bauer & Richardson, 2009). Organizational age: Theoretical treatises and empirical findings reported in the organizational ecology literature (Hannan & Freeman, 1989; Baum & Amburgey, 2005) suggest that older organizations are more formalized. Furthermore, they have a wide network of collaborations (Guo & Acar, 2005) and have difficulty in changing goals and improving goods/services (Child & Gronberg, 2007; Watkins & Bazerman, 2003). Organizational size: Larger firms often have a record of successful, rather than deficient, activities, and they gain greater legitimacy than smaller organizations (Michael, 2004) because they are considered to be more "reliable" providers of services (Elbers & Schulpen, 2011; Schmid, Bar & Nirel, 2008). Larger organizations are also considered to be prone to crises because they tend to be inert, resistant to change and less likely to innovate routines (Richardson, 1995, p. 68). The number of affiliated institutions is one measure of size, among other indicators such as number of personnel employed, height of capital investments, number of clients etc. (Pugh & Hickson, 1976). Geographic location: Geographic location determines access to resources and has both a direct and indirect effect on performance (Johansen, Ringdal & Thoring, 2001). A central location is conducive to social legitimacy because of its proximity to potentially influential stakeholders. It can influence organizational access to funding sources, help to establish a reputation (Bielefeld & Murdoch, 2004), increase impact on policymakers (Sellers & Lidström, 2006) and empower advocacy groups (Golden, Longhofer & Winchester, 2009; De Vita, 2006). METHODS Sample

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The data file is based on a survey of 164 third-sector organizations randomly chosen from a list of 227 nonprofit organizations (Council of Volunteer Associations, 2006). The sample includes organizations that report providing human services and advocacy for the population served. 135 managers returned completed questionnaires (62% response rate). Mean organizational age was 14 years (SD=10.150), and average size in number of cooperating branches was 10.62 (SD=33.607). 30% were located in metropolitan areas. Procedure Prior to conducting the field study, interviewers contacted 11 NHS managers by telephone or email and requested permission to send a questionnaire and then interview them at a later date to check the questionnaire. If managers were not available, official members at the next managerial level were asked to respond to the survey (N=2). Corrections to the questionnaire were introduced following the pilot study. One month following the pilot study a 64item questionnaire related to their organization’s operations, structure and practices was sent. 62% (135 organizations) returned the questionnaires. The Questionnaire A closed-end based questionnaire, addressing various subjects considered to be key issues in non-profit organizations, was administered in two stages. In the first stage, managers in these organizations were contacted by interviewers and were requested to provide a date to be interviewed. Second, on the agreed-upon date, interviewers contacted the managers and an interview based on the questionnaire took place. In some cases managers preferred a face-to-face meeting (25%); in others, they requested to respond by fax (20%). 62% (135 organizations) returned the questionnaires.

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Dependent Variables Restructuring following a crisis: Managers reported on the following questions: “To what extent did the crisis(es), which took place at your organization in the past: a) increase the extent of governmental supervision upon the organization’s activities; b) increase mass-media attention to the organization’s activities; c) damage the organization’s revenues; d) change the decision-making process in the organization; e) change the organization’s board of trustees; f) change organizational structure; g) downsize human resources; h) increase top managers’ involvement in organizational activity (i) increase the extent of employee involvement” (0=No change in any area; 10=change in all areas) (Cronbach’s Alpha = 0.745). Independent Variables Four indices of legitimacy are included: (a) Influence on activity which was measured as the degree of influence experienced by the NPO. Respondents replied to the following questions: “Below is a list of stakeholders that influence your organization; please indicate the degree of influence for 'government' and 'local authorities'” (0=no influence at all to 10=a strong influence). The final variable was computed by counting answers 2-10 on these items (meaning that there is indeed an influence, no matter to what extent). The values of the target variable varied from 0 to 2 (denoting the number of influencing public sector institutions). The higher the number, the more the organization was influenced by public institutions. (b) Involvement in decision making was measured as the degree of public sector involvement in the organization’s decision making. Respondents replied to the following questions: “Below is a list of stakeholders that are involved in your organization’s decision making process; please indicate the degree of involvement” (0=no involvement at all to 10=strong involvement) for the influence of 'government'

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and 'local authorities'. The final variable was computed by counting answers 2-10 on these items (meaning that there is indeed involvement, no matter to what extent). The values of the target variable varied from 0 to 2 (denoting the number of public sector institutions involved in decision making). The higher the number, the more public institutions are involved in organizational decision making. (c) Performance gaps were measured by the level of performance gaps between the public sector – government and local authorities – and NHS organizational performance. Respondents replied to the following question: “Attached is a list of various groups with which your organization is associated. How do you evaluate the degree of disagreement about your organization’s performance from the stakeholders?” The target variable ranged from 0 to 2 (denoting the number of public sector institutions for which performance gaps were reported and the level of performance gaps (1=no performance gaps to10=large performance gaps). (d) Revenue accountability was measured through the obligatory reports provided to stakeholders on all types of organizational revenues. Respondents replied to the following question: “Does your organization provide reports regarding (a) grants from associations and institutions, (b) contracts with other nonprofit organizations; (c) donations from private sources, (d) fees, (e), bank accounts, and (f) various projects, etc.?” (0=Not accountable, reports are not obligatory; 1=yes accountable, reports are obligatory). The target variable was computed by counting all the “yes” answers reported. (e) Expenditure accountability was measured using the obligatory reports provided to the stakeholders about all types of organizational expenditures. Respondents replied to the following question: “Does your organization provide reports regarding (a) salary, (b) services, (c) allocations to other institutes, (d) advertising, (e) organizational maintenance, and (f) other expenditures?” (0=Not accountable; reports are not obligatory; 1=yes,

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accountable; reports are obligatory). The final variable was computed by counting all the “yes” answers reported. Control variables: (a) Geographic location of the organization – “In which part of Israel is the non-profit organization situated?” (0=periphery; 1=metropolitan location); (b) Organization age: years of organizational activity; (c) Organization size: number of institutes affiliated with the NPO. RESULTS First, the correlation matrix examining the relationships between variables is provided. Then, two OLS regression models are presented: The first predicts legitimacy loss including only the control variables that are related to the organizational context: age, size and geographic location. The second model controls for the effects of context and introduces the independent variables – influence on activities, involvement in decision making, revenue and expenditure accountability, and performance gaps.

Age of organization Size of organization Location (1=metropol itan) Influence Performanc e gaps Revenue accountabili ty Expenditure accountabili ty Decision making Legitimacy loss 0.099

0.068

0.132

0.050

0.180 **

0. 201

0.423 ***

-

Age of organization

0.067

0.105

-0.116

-0.077

0.046

-0.112

0.210

0.120

0.132

0.405 ***

0.042

0.075

0.294 ***

Influence

0.201 ***

Location

-0.234 **

0.054

Size of organization

-0.143

0.311 ***

0.029

0.104

Performance gaps

0.132

0.023

0.576 ***

Revenue accountability

0.270 ***

0.092

Expenditure accountability

0.296 ***

Decision making

0.246 **

Legitimacy loss

-

** p