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# How do I run Powershell or Batch scripts (locally) from a Task Sequence?

• ### Question

• Hi All,

I am new to MDT 2013 and trying to fully automate my build of Windows 10 (or as much as possible).  To be clear I can't use GPOs since this machine is not networked joined since its going to act as my gold image. Once complete I'll add the automated step to capture back as a WIM and then its completely hands off.  Windows 10 16whatever comes out, I point the TS to that base OS ISO and just re-run since all the configuration is already captures.

To do this -- Essentially I have a TS that creates the base Windows 10 build, but then I have several powershell and command line scripts I want to run after the OS is built.  Where *exactly* do I put this in the Task Sequence sequence, and how do I *exactly* call the scripts?  Part of my issue maybe how I am trying to do this so bear with me.

After the OS is built I have a script to copy my scripts and related files from \$OEM\$\\$1 to C:\RUNONCE.  In this directory are installation files for Office 2016, Language Packs, etc.  This works perfect.  Its when I try to execute the scripts in C:\RUNONCE from the TS that fails.

For example, What I am *trying* to do is have the task sequence run setup.exe for Office 2016 from the C:\RUNONCE directory instead of installing it over the network from the %DEPLOYMENTSHARE%.  I dont know how to run a local command-line from a TS such as this:

C:\RUNONCE\OFFICE_2016_MUI\setup.exe /config c:\RUNONCE\office_2016_MUI\proplus.ww\config.xml

or

Powershell –executionpolicy bypass "& 'c:\RUNONCE\win10regKeys.ps1'"

I've tried using cmd /c in front but my sequence always fails at that step.  But I also don't know would this step go post-installation?  After OS install?

What is the best way to have this stuff run locally from C: drive in a task sequence?  Or is that not possible and I am restricted to installing from the network deployment share

Appreciate it

Christopher Westpoint - Infrastructure Architect

Tuesday, January 10, 2017 9:06 PM

• I figured out my own answer.  Sharing here in case others run into the same issue.

If you want to add powershell or a command line to the task sequence, place the task after state restore but before imaging section.  I created a group for all of mine, but it is not necessary.

For each task, create a "General | Run Commandline"

For the command (which should be stored in the Deploymentshare\scripts directory) the syntax is:

cmd /c <c:\windows\system32>\netsh.exe <command>

where c:\windows\system32 is the full path to the executable you want to run (this is what I was missing)

For powershell it is slightly different, since I am calling powershell from a command line in a bach

cmd /c %WinDir%\system32\WindowsPowerShell\v1.0\powershell.exe -executionpolicy bypass "& <full path to PS1 script>.ps1"

Since I had so many and I wanted to test each script/batch individually I modified the beginning of each batch to

start /wait cmd.exe /k <batch or powershell line>

Doing that brought up a cmd window, ran the script, and stops, until I exit out of the cmd window, where the next one pops up.  This allowed me to troubleshoot any issues.  Once that worked without issue put the commands back to their original state and could run silently with confidence.

No I have a fully automated Win 10 core build.

Hope this helps.

Christopher Westpoint - Infrastructure Architect

Wednesday, January 11, 2017 8:03 PM

### All replies

• I figured out my own answer.  Sharing here in case others run into the same issue.

If you want to add powershell or a command line to the task sequence, place the task after state restore but before imaging section.  I created a group for all of mine, but it is not necessary.

For each task, create a "General | Run Commandline"

For the command (which should be stored in the Deploymentshare\scripts directory) the syntax is:

cmd /c <c:\windows\system32>\netsh.exe <command>

where c:\windows\system32 is the full path to the executable you want to run (this is what I was missing)

For powershell it is slightly different, since I am calling powershell from a command line in a bach

cmd /c %WinDir%\system32\WindowsPowerShell\v1.0\powershell.exe -executionpolicy bypass "& <full path to PS1 script>.ps1"

Since I had so many and I wanted to test each script/batch individually I modified the beginning of each batch to

start /wait cmd.exe /k <batch or powershell line>

Doing that brought up a cmd window, ran the script, and stops, until I exit out of the cmd window, where the next one pops up.  This allowed me to troubleshoot any issues.  Once that worked without issue put the commands back to their original state and could run silently with confidence.

No I have a fully automated Win 10 core build.

Hope this helps.

Christopher Westpoint - Infrastructure Architect

Wednesday, January 11, 2017 8:03 PM
• MDT has Run Powershell steps that already do what you want for PS.

Many questions such as where do I find logs and what logs are interesting are found in: MDT TechNet Forum - FAQ & Getting Started Guide Please take the time to read it. Also if you don't post logs your problem won't be easily solved.

Wednesday, January 11, 2017 8:53 PM


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# The Relative Costs of Accidents Following the Establishment of the Health, Safety and Environment Management System (HSE-MS) for the Construction Industry in Tehran

## AUTHORS

Javad Vatani 1 , Gebraeil Nasl Saraji 1 , * , Abolghasem Pourreza 2 , Mahmood Salesi 3 , Iraj Mohammad Fam 4 , Seyed Abolfazl Zakerian 1

### AUTHORS INFORMATION

1 Department of Occupational Health, School of Public Health, International Campus, Tehran University of Medical Sciences (IC-TUMS), Tehran, IR Iran

2 Department of Health Management and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, IR Iran

3 Truma Research Center, Baqiyatallah University of Medical Sciences, Tehran, IR Iran

4 Department of Occupational Health, School of Public Health, Hamedan University of Medical Sciences, Tehran, IR Iran

How to Cite: Vatani J, Saraji G N, Pourreza A, Salesi M, Mohammad Fam I, et al. The Relative Costs of Accidents Following the Establishment of the Health, Safety and Environment Management System (HSE-MS) for the Construction Industry in Tehran, Iran Red Crescent Med J. 2016 ; 18(12):e27140. doi: 10.5812/ircmj.27140.

### ARTICLE INFORMATION

Iranian Red Crescent Medical Journal: 18 (12); e27140
Published Online: August 20, 2016
Article Type: Research Article
Revised: February 15, 2015
Accepted: May 12, 2015

# Crossmark

#### CHEKING

##### Abstract

Background: Accidents in the construction industry are a significant problem in many countries around the world. Occupational accidents result in reduced working hours, loss of life, and other related costs. These accidents result in socio-economic losses such as disability support costs, loss of working time, and increased medical care needs. They also create a lot of waste for communities and labor organizations.

Objectives: Despite extensive studies calculating the cost of accidents, our understanding of these costs is still not clear or practically applicable; consequently, the ultimate aim of the present study is to provide a new framework for the calculation of the (direct and indirect) costs of construction accidents.

Methods: In this paper, using a new and structured seven-step approach, the costs of construction accidents were calculated. In order to calculate the total cost of construction accidents in the city of Tehran for 2013 - 2014, the severity of accident results were first classified into five groups: 1, short-term absences from work; 2, long-term absences; 3, partial disability; 4, total disability; and 5, death. The types of costs resulting from accidents were also categorized: 1, production disturbance costs; 2, human capital costs; 3, medical costs; 4, administrative costs; 5, transfer costs; and 6, other costs. These costs were classified according to the direct or indirect costs resulting from the incident and the imposition of costs on workers, employers and society. Finally, the calculated amount of investment in HSE-MS was analyzed using statistical tests.

Results: The present study indicates that before and after the establishment of HSE-MS that the maximum calculated cost was related to the production disturbance cost (before: $568,000; after:$80,500) and the lowest cost was related to transfer costs (before: $15,000; after:$3,000) and other costs (before: $98,000; after:$28,500). Statistical analyses indicates that there is a significant difference (P = 0.007) between the direct and indirect costs of accidents for before and after the establishment of HSE-MS. In other words, the direct and indirect costs had multiple, significant differences. The present study indicates that the indirect cost is four times greater than the direct costs.

Conclusions: Accidents resulting in death, total disability and partial disability impose huge costs that are borne by society, and employers bear relatively low costs due to the health, treatment and welfare systems provided by society for the treatment and rehabilitation of injured workers and their families. Also, the results of this study show that investment in the HSE-MS is effective and associated with reduced accidents.

## Keywords

Accidents Indirect Costs Direct Costs HSE-MS Construction Industry

Copyright © 2016, Iranian Red Crescent Medical Journal. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.

### 1. Background

1.1. Definition of an Accident

1, An unfortunate incident that happens unexpectedly and unintentionally, typically resulting in damage or injury (1).

2, A sudden event (such as a crash) that is not planned or intended and that causes damage or injury (2).

3, An event that is not planned or intended: an event that occurs by chance (2).

The construction industry plays an important role in the economy of many countries; for example, in the UK it represents 10 percent of GDP and more than 1.5 million (3), and there are a large number of occupational accidents in this industry. The most important parts of the costs of occupational accidents are the human costs that result in reduced working years, the loss of life, and other related losses (4, 5). Every year, millions of occupational accidents occur worldwide; some of these accidents are fatal, and others leads to temporary or permanent disability (6). Human contemporary history has recorded multiple disasters with multi-billion dollar financial losses and numerous human casualties including the explosion of the space shuttle Challenger (1986), the nuclear reactor explosion at Chernobyl (1986), the San Juanico accident in Mexico (1984), and the Bhopal plant accident in India (1984) (7). Each year, there are approximately 120 million occupational accidents worldwide, and more than 200 thousand deaths are caused by these accidents (6). In India, annually 7.7 million occupational accidents are reported, which represent an average loss of 3.25 working days per accident occurrence (8). In the year 2007, across 15 European countries the number of occupational accidents with more than three days’ absence from work in manufacturing companies was about 942,000 (9). In the US, occupational accidents in the construction industry resulted in the death of about 17.29 workers out of 100,000 between 1980 and 1992 (10). These accidents have brought great losses for communities, organizations and workers, and related costs along with adverse effects on economic indicators at both the national and household levels are significant.

Accidents in the transportation industry are a major cause of morbidity and mortality of youth worldwide; in developing countries, the rate of deaths due to accidents is quite high, for example in the transport sector in Iran there are 25.8 deaths per 100,000 people in the population, but in developed countries it is only 19.9 deaths (11, 12).

Currently, the worst consequence of occupational accidents is the premature mortality of a notable percentage of the global labor force (3, 4). Work-related injuries are the biggest health problem worldwide, and about 14 deaths per 100,000 are due to occupational accidents. These accidents cause socio-economic losses such as disability, reduction of working time, and increases in health care costs (4). The annual human cost per foot is $3.36 billion, and the total human cost of occupational accidents resulting in death was 3% of GDP (5, 13, 14). Bahrampur et al. studied the construction workers of Yazd city in Iran and showed that if this trend in accident continues, over the next few years the amount of these accidents will continue to increase dramatically (13). A study by Feng (15) showed that safety investments in the construction industry will increase safety culture and behavior . Also, other studies showed that investment in safety management reduces the number of accidents and the costs borne by industry, and due to increased employee satisfaction increases the quality of life of workers (15, 16). One of the most dangerous fields of employment worldwide is the construction industry, but along with the number of accidents leading to death and total disability, its risks and losses remains somewhat unclear (14, 17). The Health, Safety and environment management system (HSE-MS) of Iran is a regular, systematic, comprehensive, and explicit approach to planning, documenting, and changing the methods for managing detrimental factors, safety threats, and risk analyses. Like other management systems, the HSE-MS system was developed in order to maintain a healthy working environment with the minimum amount of job-related incidents and dangers (18, 19). ### 2. Objectives Despite many studies on the costs of accidents and investments in the HSE-MS, there is still no clear and scientific method available for calculating the cost of accidents; the ones that are available have some blind spots. The ultimate aim of the present study is to provide a new framework for the evaluation of the HSE-MS management system’s implementation. In order to reduce accidents using the new methods, the main goal of the present study is to develop a new method for the calculation of accident costs (direct and indirect), specifically for calculating the costs borne by society, the employer and the injured person, and demonstrate the required analyses of the newly-developed method. ### 3. Methods In this paper, the costs of accidents both before and after the establishment of the HSE-MS are calculated by the use of a new, structured seven-step approach: in order to calculate the cost of construction accidents in the city of Tehran in 2013 - 2014, first accidents were classified into five groups according to the severity of the accident results: 1, short-term absences from work; 2, long-term absence; 3, partial disability; 4, total disability; and 5, death. The types of costs resulting from accidents were also categorized: 1, production disturbance costs; 2, human capital costs; 3, medical costs; 4, administrative costs; 5, transfer costs; and 6, other costs. These costs were classified according to the direct or indirect costs resulting from the incident and the imposition of costs on workers, employers and society, calculating the investment value in the HSE-MS system. Finally, using the formulas below the direct and indirect costs of accidents and also the costs borne by workers, employers and society were measured. These procedures are described in detail below. 3.1. Step One In order to calculate the cost of accidents, first of all accidents were classified into one of five groups according to the severity of the result of accident: 1, short-term absences from work; 2, long-term absence; 3, partial disability; 4, total disability; and 5, death (15, 16, 20). The type of accident, severity of its outcome, and definitions are presented in Table 1. Table 1. Definition of Accidents Based on the Severity of the Outcome of Accident Type of AccidentIntensity LevelDefinition S1Short-term absenceAbsence of less than 5 working days, after which an employee can return to all regular work tasksA minor work-related injury or illness that requires an absence of less than 5 working days for recovery, after which the employee is able to continue regular work tasks S2Long-term absenceAbsence of 5 working days or more, after which an employee can return to all regular tasksA work-related injury or illness that requires an absence of 5 or more working days but less than 6 months, after which the worker is able to continue regular work tasks S3Partial disabilityAbsence of 6 months or more, after which an employee can return to work with only short-term or reduced tasks; frequently resulting in a lower income than beforeA work-related injury or illness that requires more than 6 months’ absence from work; after returning to work, the worker’s job duties must be modified S4Total disabilityPermanent disability without return to workA work-related injury or illness that causes permanent disability and leaves the employee unable to work S5DeathDeathA work-related injury or illness that leads to death 3.2. Step Two Accidents were classified based on the types of costs resulting from them: 1, production disturbance costs; 2, human capital costs; 3, medical costs; 4, administrative costs; 5, transfer costs; and 6, other costs. The following definitions were used to calculate each type of costs. Production disturbance costs (PDC) are costs incurred in the short term until the production returns to the level it was at before the accident. Human capital costs (HCC) are long-term costs, including a potential reduction of production, referring to a restructuring of the production level from the way it was before the accident happened. Medical costs (MEDC) are costs incurred by workers and the society during the medical treatment of workers injured in work-related accidents. Administrative costs (ADMINC) are incurred through the implementation of indemnification projects and accident investigations, and also include legal fees. Transfer costs (TRANC) are deadweight losses associated with the administration of taxes and welfare. Other costs (OTC), including costs not classified elsewhere, cover things such as costs of career retraining, assistance changes and renovations (15, 16, 20). 3.3. Step Three Each cost group in more detailed classification was classified in cost cases and then a code was allocated to each of the cost sections. 3.4. Step Four All of the accidents were classified as having direct (D) or indirect (I) costs, then were analyzed based on the costs borne by employers (E), workers (W), and society (S) (15, 16, 20). Table 2 indicates the summary of the above-mentioned classifications. Table 2. Summary of Classifications of Accidents in Terms of Cost Group, Severity of Result, and Direct/Indirect Costs Cost GroupVariableTypes of CostsCosts Borne byDirect or Indirect CostsSeverity of Accident Production disturbancePDC1OvertimeEI- PDC2Additional employer paymentsEI- PDC3Cost of employee turnoverED- PDC4Employee training and retrainingEIS3, S4, S5 PDC5Loss of current incomeWI- PDC6IndemnificationSIS3, S4, S5, S2 Human capitalHCC1Loss of future earningsWIS3, S4, S5 HCC2Loss of governmental revenueSIS3, S4, S5, S2 HCC3Social welfare payments for lost earning capacitySD- MedicalMEDC1Threshold of medical paymentsED- MEDC2Medical and rehabilitationWI- MEDC3RehabilitationSD- MEDC4Health careSD- AdministrativeADMINC1Fines and legal sanctionsED- ADMINC2StudyEI- ADMINC3TravelWI- ADMINC4Legal feesWIS4, S5 ADMINC5Cost of death (funeral)WD- ADMINC6Inspection and examinationSD- ADMINC7Travel for workers with total disabilitiesSD- TRANC1Welfare payments and tax lossesSD- TransferOTC1CareWIS4 OtherOTC2Help and changesWIS4 3.5. Step Five Finally, using the formulas provided below, the costs of the accident its direct and indirect costs, and also the costs borne by employers, workers, and society were measured (15, 16, 20). - Total cost of the accident: (PDC1 + PDC2 + PDC3 + PDC4 + PDC5 + PDC6) + (HCC1 + HCC2 + HCC3) + (MEDC1 + MEDC2 + MEDC3 + MEDC4) + (ADMINC1 + ADMINC2 + ADMINC3 + ADMINC4 + ADMINC5 + ADMINC6 + ADMINC7) + (TRANC1) + (OTC1 + OTC2) - Direct costs of accident: (PDC3) + (HCC3) + (MEDC1 + MEDC3 + MEDC4) + (ADMINC1 + ADMINC5 + ADMINC6 + ADMINC7) + (TRANC1) - Indirect costs of accident: (PDC1 + PDC2 + PDC4 + PDC5 + PDC6) + (HCC1 + HCC2) + (MEDC2) + (ADMINC2 + ADMINC3 + ADMINC4) + (OTC1 + OTC2) - Costs borne by employer: (PDC1 + PDC2 + PDC3 + PDC4) + (MEDC1) + (ADMINC1 + ADMINC2) - Costs borne by worker: (PDC5) + (HCC1) + (MEDC2) + (ADMINC3 + ADMINC4 + ADMINC5) + (OTC1 + OTC2) - Costs borne by society: (PDC6) + (HCC2 + HCC3) + (MEDC3 + MEDC4) + (ADMINC6 + ADMINC7) + (TRANC1) 3.6. Step Six Calculation of required investment in HSE-MS: Investment in the HSE-MS includes eight main sectors: 1, administrative costs; 2, educational costs; 3, equipment costs; 4, committee costs; 5, costs of promotion and incentives; 6, cost of new technologies, methods or design tools; 7, costs for identifying, measuring, and controlling risks to health and the environment; 8, costs for investigation, documentation and certification of HSE-MS (15, 16, 18-20) 3.7. Step Seven Statistical analyses: Cost data were entered into SPSS 16 statistical software and analyzed via statistical tests as follows: First the Kolmogrov Smirnov test was used to determine the normality of the accident cost data. In the case of a normal cost variable, a two-way ANOVA was used to determine the significance of the differences among the type of cost of accidents by the outcome of accidents, accident costs, and costs borne by workers, employers and society both before and after the establishment of the HSE-MS. Results were considered significant at P < 0.05. ### 4. Results 4.1. A Practical Example of Calculating Costs in the Construction Industry In order to test the calculation of accident costs in accordance with the above-described steps, this study examined the construction industry in Tehran in 2013 - 2014. The HSE-MS, which was established in 2014, aimed to build 2000 units of residential buildings over 5 years with an investment of$45 million. Thus, this study calculated the number of accidents that occurred and the costs associated with these accidents during 2013 (prior to the establishment of the HSE-MS) and 2014 (after its establishment). In 2013, 22 accidents occurred, of which one was fatal, two resulted in total disability, three resulted in partial disability, four resulted in long-term absence, and 12 resulted in only short-term absence and minor injuries. In comparison, during 2014, just 8 accidents occurred: one resulted in total disability, two resulted in long-term absence, and five resulted in short-term absences. The Kolmogrov Smirnov test indicates the normality of the accident cost data (P = 0.604).

Table 3. Calculation of the Required Investment in the HSE-MS
InvestmentAmount (US$) Administrative costs34,200 Educational costs30,000 Equipment costs200,000 Committee costs33,000 Costs of promotion and incentives31,200 Cost of new technologies, methods or design tools140,000 Costs for recognition, measure and control of risks to health and the environment32,500 Costs for investigation, documentation and certification of HSE-MS23,500 Total524,400 In 2014, the total amount of investment in the HSE-MS was$524,400, which included administrative costs ($34,200), educational costs ($30,000), equipment costs (USD$200,000), committees costs ($33,000), the costs of promotion and incentives ($31,200), the cost of new technologies, methods or design tools ($140,000), the costs for identifying, measuring, and controlling risks to health and the environment ($32,500), and the costs for investigation, documentation and certification of the HSE-MS management system ($23,500).

Table 4. Analysis of the Calculated Costs of the Accidents in the Construction Industry (US$) Cost GroupResult of Accident,$Sum, $P-Value Short-Term AbsenceLong-Term AbsencePartial DisabilityTotal DisabilityDeath Accident costs pre-HSE-MSPDC86,000172,000168,000100,00042,000568,0000.000 HCC60,000120,00075,00036,0000291,000 MEDC5,00080,00030,00012,00012,000139,000 ADMINC15,00026,00021,00024,00012,00098,000 TRANC3,0006,0006,0000015,000 OTC11,00022,00027,00020,00018,00098,000 Accident costs post-HSE-MSPDC013,000050,00017,50080,5000.000 HCC060,000018,000078,000 MEDC040,00006,0005,00051,000 ADMINC013,000012,0005,00030,000 TRANC03,0000003,000 OTC011,000010,0007,50028,500 Who bore costs pre-HSE-MS?W53,000148,000129,00040,00030,000400,000 E41,00072,00096,00076,00048,000333,000 S86,000206,000102,00080,0000474,000 Who bore costs post-HSE-MS?W074,000020,00012,500106,500 W036,000038,00020,00094,000 S0103,000040,0000143,000 Type of costs pre-HSE-MSD29,00082,00054,00048,00018,000231,0000.007 I151,000344,000273,000148,00066,000982,000 Type of Costs post-HSE-MSD041,000024,0007,50072,500 I0172,000074,00027,500273,500 Total Cost of pre-HSE-MS Accidents180,000426,000327,000196,00084,000121,3000 Total Cost of post-HSE-MS Accidents0213,000098,00035,00034,6000 The present study indicates that before the establishment of the HSE-MS that the maximum calculated costs was related to production disturbances ($568,000) and human capital ($291,000), and the lowest costs were related to medical care ($139,000), administrative expenses ($98,000), transfers ($15,000) and others ($98,000). Although lower overall, after the establishment of the HSE-MS, the maximum calculated costs were again related to production disturbances ($80,500) and human capital ($78,000), and the lowest costs were similarly related to medical care ($51,000), administrative expenses ($30,000), transfers ($3,000) and others ($28,500). Analyses showed that the difference in the costs of accidents before and after the establishment of the HSE-MS was statistically significant (P = 0.000). Statistical analyses for the differences between the accident costs borne by society, employers and workers before and after the establishment of the HSE-MS were statistically significant (P = 0.000), i.e., the highest accident costs were borne by society and the lowest were borne by employers. A significant difference was seen among the costs of accidents resulting in death, total disability, partial disability, long-term absence, and short-term absence (P = 0.005) Accidents resulting in long-term absence had highest costs both before ($426,000) and after ($213,000) the establishment of the HSE-MS. Pre-HSE-MS, the lowest costs were for accidents resulting in death ($84,000); after the establishment of the HSE-MS, the lowest costs were for accidents resulting in short-term absences and partial disability (both $0). Statistical analyses indicated that there was also a significant difference (P = 0.007) in the direct and indirect costs of accidents before and after the establishment of the HSE-MS. In other words, direct and indirect accident costs had multiple significant differences. Figure 1 shows that in terms of both direct and indirect costs, before the establishment of the HSE-MS the most expensive costs are for accidents resulting in total disability and partial disability, the lowest-cost accidents were those resulting in short-term absence; this difference was statistically significant (P = 0.004). But, after the establishment of the HSE-MS accidents resulting in total disability and long-term absence had the most expensive direct and indirect costs, and accidents that resulted in death and partial disability were the least expensive; this difference was also statistically significant (P = 0.004). The present study indicates that the indirect costs are four times greater than the direct costs. Figure 1. Costs Based on Type and Results of Accidents Figure 2 shows that before the establishment of the HSE-MS most of the costs of accidents resulting in death and total disability were borne by society ($86,000 and $206,000, respectively) and workers ($53,000 and $148,000) and the smallest amount of these costs was borne by employers ($41,000 and $72,000). More of the costs of accidents that resulted in partial disability were borne by workers ($129,000) than by society ($102,000) or employers ($96,000). When accidents resulted in long-term absences more of the costs were borne by society ($80,000) and employers ($76,000) and less by workers ($40,000). In the accidents resulting in only short-term absences most of the costs were borne by employers ($48,000), and the remaining costs were borne by workers ($30,000) in this case no costs were borne by society. These differences were all statistically significant (P = 0.041). Figure 2. Costs of Accidents in Terms of the Costs Borne by Workers, Employers and Society by the Type of Accident Results Following the establishment of the HSE-MS, all of these costs were markedly reduced. The costs of most of the accidents resulting in total disability were borne by society ($103,000) and workers ($74,000) with only$36,000 being borne by employers. But, in the accidents that resulted in long-term absences most of the costs were borne by society ($40,000) and employers ($38,000); workers ($20,000) bore the smallest amount of these costs. In accidents resulting in short-term absences most of the costs were borne by employers ($20,000) and the remainder was borne by workers ($12,500); as before, no costs were borne by society in this case. These differences were also all statistically significant (P = 0.041). ### 5. Discussion The present research studied accidents in the construction industry in Tehran during 2013 and 2014. The purpose of the HSE-MS project was to construct a residential building of 2000 units over 5 years with an investment of$45 million. In 2014, the total amount of investment in HSE-MS was $524,400. This study analyzed the number of accidents that occurred during 2013 (prior to the establishment of the HSE-MS) and 2014 (after its establishment) and the costs associated with these accidents. In 2013, 22 accidents occurred, of which one was fatal, two resulted in total disability, three resulted in partial disability, four resulted in long-term absence, and 12 resulted in only short-term absence and minor injuries. In comparison, during 2014, just 8 accidents occurred: one resulted in total disability, two resulted in long-term absence, and five resulted in short-term absences. The greatest reduction in 2014 was in accidents that related in death and partial disability. The decrease in the number of accidents in 2014 compared with 2013, and the$867,000 reduction in the cost of accidents during that time frame, show that investment in the HSE-MS was effective.

The highest cost stemming from construction accidents can be categorized as production disturbance costs and human capital costs, and the lowest fall under medical costs, administrative costs, transfer costs and other costs. This indicates that generally, the costs arising from accidents result from the loss of work, reduced income and additional payments including overtime costs, employee training and retraining costs, loss of current income, and indemnification for loss of future earnings. Most of the costs of accidents resulting in death, total disability and long-term absences are borne by society, and the lowest costs in these cases are borne by the employers. The majority of the costs of accidents leading to partial disability, however, are borne by workers, and the lowest is on employers. In accidents leading to short-term absences, however, most of the costs are borne by employers, and none by society. In other words, accidents with serious consequences such as death, total and partial disability impose a huge cost on society, and employers bear relatively little of these costs, due to the payments provided by the health, treatment and welfare system of society for the treatment and rehabilitation of injured employees and their families; only in low-impact accidents with less severe results such as short-term absence are the greatest portion of the costs borne by employers because of the additional costs of additional and health payments for the injured employees. In long-term absences a large part of the cost is borne by employees in the form of reduced salaries.

According to the present study the amount of accidents leading to total disability, partial disability and death are high, due to the failure to observe basic safety rules and guidelines. The high costs of production disturbance costs and human capital costs in the construction industry are due to indemnifications, the cost of training new workers, loss of efficiency and current money making. In addition to the high incidence of accidents leading to total and partial disability and death in the construction industry, consequently the greatest financial and mental costs are primarily borne by society, and secondly fall on family and the injured workers, which causes increasing financial pressure. Also, this study showed that investment in the HSE-MS is effective and associated with a reduced rate of accidents. Based on the obtained results, the HSE-MS system implementation is strictly advisable because of its benefits.

It is suggested that new research should be conducted to provide a control strategy to reduce the incidents occuring in the construction industry, and to reduce the indirect costs and costs borne by both society and workers, with the aim of increasing welfare and public health.

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• 5. Mehrdad R, Seifmanesh S, Chavoshi F, Aminian O, Izadi N. Epidemiology of occupational accidents in Iran based on social security organization database. IRCMJ. 2014; 16(1)
• 6. Lopez Arquillos A, Carlos Rubio Romero V, Alistair Gibb V. Analysis of construction accidents in Spain 2003-2008. Safety Res. 2012;
• 7. Cox SJ, Tait NR. Safety, reliability and risk management: An integrated approach. 1998;
• 8. Nag PK, Patel VG. Work accidents among shiftworkers in industry. INT J IND. 1998; 21(3) : 275 -81
• 9. Nenonen S. Fatal workplace accidents in outsourced operations in the manufacturing industry. Safety Sci. 2011; 49(10) : 1394 -403
• 10. Lopez M, Ritzel DO, Fontaneda I, Alcantara O. Construction industry accidents in Spain. J Safety Res. 2008; 39(5) : 497 -507
• 11. Moharamzad Y, Taghipour H, Firoozabadi N, Firoozabadi A, Hashemzadeh M, Mirjalili M, et al. Mortality pattern according to autopsy findings among traffic accident victims in Yazd, Iran. Chinese J Traumatol. 2008; 11(6) : 329 -34
• 12. Rasouli MR, Nouri M, Zarei M, Saadat S, Rahimi-Movaghar V. Comparison of road traffic fatalities and injuries in Iran with other countries. Chinese J Traumatol. 2008; 11(3) : 131 -4
• 13. Bahrampour A, Jafari Nodoushan R, Vatani Shoaa J. Five-year epidemiological study and estimation of accidents distribution in construction industry workers in yazd city by the year 2011 by applying time series model. KUMS. 2015;
• 14. Vatani S, Salasi M, Bahrampour A, Raei M, Asadi M, Jafari N, et al. An epidemiological study of accidents among construction workers in Kerman. 2011;
• 15. Feng Y. Effect of safety investments on safety performance of building projects. Safety Sci. 2013; 59 : 28 -45
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• 20. Teo E, Feng Y. The indirect effect of safety investment on safety performance for building projects. Architect Sci Rev. 2011; 54(1) : 65 -80

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text:
# Prove that the product of two consecutive positive integers is divisible by 2.

Question:

Prove that the product of two consecutive positive integers is divisible by 2.

Solution:

To Prove: that the product of two consecutive integers is divisible by 2.

Proof: Let − 1 and n be two consecutive positive integers.

Then their product is $n(n-1)=n^{2}-n$

We know that every positive integer is of the form 2q or 2q + 1 for some integer q.

So let n = 2q

So, $n^{2}-n=(2 q)^{2}-(2 q)$

$\Rightarrow n^{2}-n=(2 q)^{2}-2 q$

$\Rightarrow n^{2}-n=4 q^{2}-2 q$

$\Rightarrow n^{2}-n=2 q(2 q-1)$

$\Rightarrow n^{2}-n=2 r$ (where $r=q(2 q-1)$ )

$\Rightarrow n^{2}-n$ is even and divisble by 2

Let n = 2q + 1

So, $n^{2}-n=(2 q+1)^{2}-(2 q+1)$

$\Rightarrow n^{2}-n=(2 q+1)((2 q+1)-1)$

$\Rightarrow n^{2}-n=(2 q+1)(2 q)$

$\Rightarrow n^{2}-n=2 r(r=q(2 q+1))$

$\Rightarrow n^{2}-n$ is even and divisble by 2

Hence it is proved that that the product of two consecutive integers is divisible by 2


text:
(Analysis by Nick Wu)

In this problem, we want to shoot a laser from a source point to a destination point. The laser starts out being horizontal or vertical, and diagonal mirrors can be inserted in certain locations to change the orientation of the laser. We want to insert the minimum number of lasers needed to reach the destination.

We start by making an observation about any optimal path of the laser - given any horizontal or vertical line, the laser will only cover at most one contiguous interval of the line. If the laser covers two disjoint intervals, we can "skip" part of the path that goes off of that line and get a more optimal path.

Therefore, there are at most $2N+2$ lines that the laser can travel on - $N+1$ horizontal ones and $N+1$ vertical ones each corresponding to the initial point and the $N$ points where we can place mirrors.

We can then model this problem as a shortest path problem. We want to get to either a horizontal or vertical line that goes through the destination point, and we start on a horizontal or vertical line that goes through the start point. We can transfer between two lines if and only if their intersection point is one of the $N$ points given to us, and we want to minimize the number of transfers we do.

import java.io.*;
import java.util.*;
public class lasers {
public static void main(String[] args) throws IOException {
PrintWriter pw = new PrintWriter(new BufferedWriter(new FileWriter("lasers.out")));
int n = Integer.parseInt(st.nextToken());
int x1 = Integer.parseInt(st.nextToken());
int y1 = Integer.parseInt(st.nextToken());
int x2 = Integer.parseInt(st.nextToken());
int y2 = Integer.parseInt(st.nextToken());
Map<Line, Integer> dist = new HashMap<Line, Integer>();
dist.put(new Line(y1, true), 0);
dist.put(new Line(x1, false), 0);
Map<Integer, ArrayList<Integer>> xtoY = new HashMap<Integer, ArrayList<Integer>>();
Map<Integer, ArrayList<Integer>> ytoX = new HashMap<Integer, ArrayList<Integer>>();
for(int i = 0; i < n; i++) {
int x = Integer.parseInt(st.nextToken());
int y = Integer.parseInt(st.nextToken());
if(!xtoY.containsKey(x)) {
xtoY.put(x, new ArrayList<Integer>());
}
if(!ytoX.containsKey(y)) {
ytoX.put(y, new ArrayList<Integer>());
}
}
int ret = -1;
while(!q.isEmpty()) {
Line curr = q.removeFirst();
if(curr.horizontal && curr.val == y2) {
ret = dist.get(curr);
break;
}
if(!curr.horizontal && curr.val == x2) {
ret = dist.get(curr);
break;
}
Map<Integer, ArrayList<Integer>> source = curr.horizontal ? ytoX : xtoY;
if(source.containsKey(curr.val)) {
for(int dest: source.get(curr.val)) {
Line nextLine = new Line(dest, !curr.horizontal);
if(!dist.containsKey(nextLine)) {
dist.put(nextLine, dist.get(curr) + 1);
}
}
}
}
pw.println(ret);
pw.close();
}

static class Line {
public int val;
public boolean horizontal;
public Line(int val, boolean horizontal) {
super();
this.val = val;
this.horizontal = horizontal;
}
@Override
public int hashCode() {
final int prime = 31;
int result = 1;
result = prime * result + (horizontal ? 1231 : 1237);
result = prime * result + val;
return result;
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (getClass() != obj.getClass())
return false;
Line other = (Line) obj;
if (horizontal != other.horizontal)
return false;
if (val != other.val)
return false;
return true;
}

}
}


text:
# Deconvolution in Deep Learning

In deep learning literature, the term deconvolution is used in a way that is different from its meaning in conventional usage. This blog post is to briefly explain this difference.

##### Deconvolution in Image & Signal Processing

Oxford dictionary defines deconvolution as “a process of resolving something into its constituent elements or removing complication.” In image and signal processing, deconvolution is known as an image/signal restoration technique used to enhance an image/signal degraded by blurring and noise. The observed degraded image, g, is modeled as g = h∗f + η, where f is the input image and h represents the degradation function. The degradation process is treated as a convolution operation represented by symbol and η stands for additive noise in the image acquistion process. Thus, the aim of an image restoration process or deconvolution is to recover f given the degraded image g. There are several methods for this task, both in the frequency and the spatial (time-domain for one-dimensional signals) domains, using different assumptions. One such method for deconvolution is the regularized filter method that minimizes a loss function consisting of the squared error between the estimated image $\hat{f}$ and the observed image g subject to preserving image smoothness constraint. An example of deblurring using the regularized filter approach is shown below.

##### Convolution and Deconvolution in Deep Learning

Deconvolution in deep learning is not concerned with restoring a degraded signal or image; it is rather concerned with mapping a set of data values to another larger set of data values, that is up-sampling the data. To understand this, lets begin by considering a 4×4 data patch and a 3×3 convolution filter as shown below.

Lets assume that we want to calculate the convolution result only for those patch positions where placing of the convolution mask doesn’t extend it outside the patch. In other words, we want to perform convolution with zero or no padding. This leaves us only four positions, second row-second column, second row-third column, third row-second column, and third row-third column, where the convolution operation is to be applied. Just to refresh the convolution operation, the result of convolution at second row-second column position is given by 0*3 + 1*3 + 2*2 + 2*0 +2*0 + 0*1 + 0*3 + 1*1 + 2*2, where the first element in each multiplication term comes from the convolution mask. The result is 12. Calculating similarly at the remaining positions, we get the following 2×2 matrix as convolution result for the given data patch and the convolution mask.

Lets look at the above convolution operation in a matrix-vector form; that is how the computation is carried out. Converting the 4×4 data patch by reading its elements left to right and top to bottom, we can write its vector representation as $\bold D^T$ = [3 3 2 1 0 0 1 3 3 1 2 2 2 0 0 2]. Lets represent our 3×3 convolution mask with generic elements as

To obtain vector representation for the convolution mask, we create a 4×4 array and position the mask at four positions where we want to calculate convolution. The remaining elements in the 4×4 array are filled with 0’s. We then convert each array to a vector by reading elements left to right and top to bottom to generate four 16-dimensional arrays as shown in the figure below.

We Combine the four arrays representing the convolution operation at four positions in our data patch, and get a 16×4 matrix C. The transpose of matrix C is shown below.

Replacing the generic convolution mask values with actual mask elements used earlier, matrix C can be written as

With the above matrix vector representation, the convolution operation can be simply written as $\bold {D}^T\bold {C}$ = [12 12 10 17] which upon rearanging as a 2×2 array gives the result shown earlier.

##### Transposed Convolution

In deep learning, deconvolution essentially refers to the operation that gets performed when the computation is being done from the output to input layer during error propagation or segmented image generation as in semantic segmentation. Taking again a simplified view without pooling and using the above example, deconvolution in this case involves mapping a 2×2 array to a 4×4 array. Once we roll out the 2×2 array into a 4-dimensional vector, E, we can obtain 16 data values as a 16-dimensional row vector by performing the matrix multiplication $\bold{E}\bold{C}^T$ using the transposed convolution matrix. Rearranging the resulting 16-dimensional vector gives us an up-sampled result. Thus, deconvolution in deep learning refers to transposed convolution and it has no connection with deconvolution for image/signal restoration.

While the above illustration was done with zero padding, unit stride, and no pooling, it is not difficult to see how a similar matrix vector representation can be used be used with padded data/image patches and with different strides and pooling. In those situations, shaping of the data patches and convolution masks into matrix vector representation will require additional considerations but the basic idea remains the same.


text:
## Abstract

Some of the academic research on ISO 14001 has focused on analyzing the benefits of its adoption. However, this international standard has also received some criticism, particularly in respect of the adoption of ISO 14001 when not accompanied by significant improvements in environmental performance. This study analyzes the relationship between the symbolic environmental behavior and the adoption of ISO 14001. In so doing, it uses binary logistic regression to analyze an international sample of 1961 manufacturing facilities that each employs more than 50 people. The results indicate that the higher the symbolic environmental performance of the firm, the greater the probability of adopting ISO 14001.

## JEL classification

Q530;Q560;M190;M140

## Keywords

Symbolic environmental behavior;ISO 14001;Environmental performance;Logistic binary regression

## 1. Introduction

The aim of this paper is to analyze whether a symbolic environmental behavior is related to the adoption of ISO 14001. This is based on the premise that managers do not choose to uniformly adopt ISO 14001 (i.e., adopting yes or no), but the result of their decision may also include the option of adopting the standard in a symbolic manner. To analyze this relationship this study draws on data from a survey conducted by the Environmental Directorate of the Organisation for Economic Co-operation and Development (OECD) and uses binary logistic regression to analyze an international sample comprised of 1961 facilities in different manufacturing sectors. The results suggest a positive relationship between symbolic environmental behavior and the adoption of ISO 14001.

## 2. Benefits and criticism of the adoption of ISO 14001

The adoption of ISO 14001 can generate competitive advantage for firms (e.g., Darnall, 2006; Delmas, 2001 ;  Russo, 2009) through the promotion and development of distinctive skills in organizational, commercial, and related stakeholder management. With regard to organizational skills, the adoption of ISO 14001 may represent a valuable and intangible resource because it provides an ideal frame for the effective development of an EMS (Delmas, 2001). Improvements in operational efficiency can emerge because ISO 14001 is based on the principle of continuous improvement (Bansal & Hunter, 2003). ISO 14001 promotes internal assessments in the consumption of energy and resources, the implementation of cost analysis in the life cycle, and other similarly advanced practices of environmental management that are directly related to the reduction in environmental impacts (Potoski and Prakash, 2005 ;  Ferrón Vílchez and Darnall, 2016). In addition, the adoption of ISO 14001 is positively associated with the development of complementary resources and skills related to obtaining competitive advantage, such as the adoption of quality management systems or the investment in new technologies and innovation (Darnall, 2006 ;  Darnall and Edwards, 2006).

In terms of skills related to managing stakeholders (e.g., customers, suppliers, labor unions, communities, environmental groups, regulators, etc.), the adoption of ISO 14001 is often motivated by normative4 pressures. This is because the adoption of ISO 14001, being voluntary, facilitates and legitimates firms environmental practices to meet the demands of stakeholders (Heras-Saizarbitoria & Boiral, 2013). For example, Castka and Prajogo (2013) found that secondary stakeholders (e.g., local communities, social groups, NGOs, etc.) might be influential when adopting ISO 14001 in firms interested in obtaining the benefits associated with the improved reputation that the standard can generate. In addition, those firms that continually seek innovative environmental solutions to address the pressures of external stakeholders (Henriques & Sadorsky, 1999) tend to adopt ISO 14001 in order to facilitate the integration of the demands of the stakeholders in the decision-making process ( Castka and Prajogo, 2013 ;  Delmas, 2001). Including the objectives of the stakeholders in the design of an EMS, and the subsequent adoption of ISO 14001, may involve the development of a valuable skill that is difficult to imitate by competitors because of the complexity and the inherent causal ambiguity of this process (Delmas, 2001).

In contrast, the aspiration for legitimacy, as the main advantage related to the reputation granted by the adoption of ISO 14001, can become a double-edged sword. The adoption of the standard for the sole purpose of legitimizing management practices sometimes generates symbolic or superficial adoption (Aravind and Christmann, 2011; Boiral, 2007 ;  Iatridis and Kesidou, 2016). This symbolic adoption involves the use of ISO 14001 as a way to legitimate the environmental practices of firms seeking the support of the institutional context but without necessarily implying an effective commitment to internal improvement (Aravind & Christmann, 2011). For example, Boiral (2007) found a “ritual integration” of ISO 14001 in firms with a low level of employee involvement and a high level of intensity in the pressures of their institutional context. This symbolic adoption damages ISO 14001 bases, such as continuous improvement in environmental performance, pollution prevention, and compliance with environmental regulations (ISO, 2014). Thus, confidence in the ability of standard to reduce the problems of asymmetric information (King et al., 2005) can increase the number of adopters of ISO 14001, but, in turn, this work suggests that is also positively related to environmental symbolic behavior, which can harm ISO 14001 as a signal. Thus, the adoption of ISO 14001 may be closely linked to the decoupling between achieving institutional legitimacy and achieving significant improvements in environmental performance (Aravind and Christmann, 2011 ;  Boiral, 2007). This calls into question the confidence in ISO 14001 as a signal of the environmental performance of the firm (Montiel et al., 2012 ;  Rondinelly and Vastag, 2000).

## 3. Symbolic behavior and environmental performance

Symbolic behavior, as one of the reasons that firms’ give for certifying several management systems (e.g., quality, environmental, among others), has been analyzed in literature about ISO standards in general (e.g., Boiral, 2011; Christmann and Taylor, 2006 ;  Heras-Saizarbitoria and Boiral, 2013) and about ISO 9001 in particular (e.g., Terlaak & King, 2006). In the case of environmental management, this study assumes that environmental symbolic behavior refers to firm’ adoption of advanced practices of environmental management with the purpose of legitimizing actions but without achieving significant improvements in environmental performance. In the case of ISO 14001, previous literature has demonstrated a positive relation between its adoption and the achievement of improvements in environmental performance (Castka and Prajogo, 2013; Potoski and Prakash, 2005; Rondinelly and Vastag, 2000 ;  Russo, 2009). However, the voluntary nature of ISO 14001 adoption (due to managers having to decide whether to commit resources for this adoption) could generate the impression that the firm is environmentally responsible when, in fact, that might or might not be the case (Darnall, 2006 ;  Rondinelly and Vastag, 2000). This study considers that different profiles of ISO 14001 adoption exist, and variations among them could be associated with different results on firms’ environmental performance.

When managers choose to adopt ISO 14001 they take into account their own internal motivations (González Benito & González Benito, 2005), the isomorphic pressures of the context in which the firm develops its activity (Yin & Schmeidler, 2009), as well as the potential advantages they achieve through its adoption (Castka and Prajogo, 2013 ;  Heras-Saizarbitoria et al., 2016). Depending on their ability to address these circumstances, they will decide whether to adopt (or not) ISO 14001 based on a symbolic approach or, in contrast, with a greater level of involvement in terms of environmental commitment, time, and resources (Boiral, 2007; Delmas and Montes-Sancho, 2010; Lannelongue et al., 2015 ;  Yin and Schmeidler, 2009).

In contrast to the symbolic adoption, firms that adopt ISO 14001 with a substantive approach (Delmas & Montes-Sancho, 2010), that is, firms that are able to develop an effective response in reducing negative environmental impacts, evaluate, manage, and control a wide range of these impacts with the primary aim of decreasing (and even eliminating) them. Not only are they interested in appearing environmentally responsible, but also of being so. Firms that adopt this profile de facto can benefit not only from the commercial, reputational, and stakeholders-related advantages of ISO 14001, but they can also achieve internal or operational improvements (i.e., those related to organizational efficiency). As opposed to this de facto environmental behavior, a symbolic environmental behavior is achieved by adopting environmental practices (e.g., ISO 14001) with the aim of legitimization through the institutional context but without necessarily implying significant improvements in environmental performance ( Aravind and Christmann, 2011 ;  Boiral, 2007).

This symbolic behavior attempts to acquire the signaling that ISO 14001 confers to its adopter (Jiang & Bansal, 2003), even though the negative environmental impacts to which these firms pay attention are low (or even zero) and, therefore, they do not achieve significant improvements in their environmental performance. Consequently, this paper proposes that there is a positive relationship between this symbolic environmental behavior and the adoption of ISO 14001.

## Hypothesis.

The higher the firms symbolic environmental behavior (i.e., adoption of environmental practices without achieving significant improvements in environmental performance), the greater the probability of adopting ISO 14001.

## 4. Method

### 4.1. Data

Data for this study were obtained through a questionnaire developed by the Environmental Directorate of the Organisation for Economic Co-operation and Development (OECD) and a group of internationally renowned researchers.5 The questionnaire was sent to facilities with at least fifty employees from different manufacturing industries in Germany, Canada, the United States, France, Hungary, Japan, and Norway. Note that these industries produce higher levels of pollution in the air, water, and land than do the services sectors (Stead & Stead, 1992). The OECD questionnaire was tested in France, Canada, and Japan prior to being translated into the official language of each country. The respondents were facility managers responsible for environmental issues. The OECD sent two consecutive mailings to ensure obtaining additional answers. During the development of the questionnaire four specific biases in the use of surveys were avoided: non-response, lack of generalization, social desirability, and common method variance.6 The final response rate was 24.7% (4186 facilities), which is consistent with response rates of previous studies about environmental practices (e.g., Christmann, 2000 ;  Melnyk et al., 2003). The final sample for this study consists of 1961 facilities.

### 4.2. Variables

The dependent variable of this study was the adoption of ISO 14001. This variable was measured using an item of the OECD questionnaire that asked managers: “Has your facility acquired ISO 14001 environmental certification?” Respondents answered: (1) “Yes” or (0) “No”. There were three explanatory variables: “improvements in environmental performance”, “importance of corporate image in adopting environmental practices”, and “symbolic environmental behavior”.

First, to measure the variable “improvements in environmental performance” I relied on several items that asked respondents: “Have you experienced a change in your facility in the following environmental impacts per unit of output of your product or production process in the last three years: use of natural resources (energy, water, etc.), solid waste generation, wastewater effluent, local or regional pollution of air and global pollutants (e.g., greenhouse gases)?”. Respondents could answer: (1) “significant decreases”, (2) “decreases”, (3) “no change”, (4) “increases”, and (5) “significant increases”. For each of the five mentioned environmental impacts, responses “1” and “2”, which were identified with “significant decreases” and “decreases” respectively, were grouped under the label “improvements”, whereas responses “3”, “4”, and “5”, which were identified with “no changes”, “increases”, and “significant increases” respectively, were grouped under the label “no improvements”. Thus, five dichotomous variables (i.e., one for each of the five impacts) were created in which the score “1” corresponded to the label “improved environmental performance” and the score “0” corresponded to the label “without improvements in environmental performance”.

Following this, an ordinal variable was created that grouped the five dichotomous variables related to improvements in environmental performance so that the maximum improvement that a facility could achieve was 5 (i.e., there are improvements in the five environmental performance measures) and the minimum was 0 (i.e., no improvement in any of the measures of environmental performance). The average of this new ordinal variable was 2.26.

Second, the variable “importance of corporate image in adopting environmental practices” was measured by an item in OECD questionnaire that asked managers: “What has been the importance of the motivation for “improved corporate image” on the adoption of the environmental practices of your facility?” Respondents could answer: (1) “not important”, (2) “moderately important”, or (3) “very important”. Based on this item, a new dichotomous variable was created in which the score “1” corresponded to “improving corporate image is a very important motivation to adopt environmental practices” and the score “0” corresponded to the remaining options.

Finally, the explanatory variable “symbolic environmental behavior” was measured using a combination of the two categories of the explanatory variables previously explained. A new dichotomous variable was created as follows. On the one hand, from the ordinal variable that reflected the number of improvements in environmental performance (explained above), only cases in which environmental improvements were equal to or less than “2” were considered (since the average improvement was 2.26). On the other hand, only cases in which the “importance of corporate image in adopting environmental practices” was equal to “1” (i.e., “improved corporate image” is very important when adopting environmental practices) were considered. Based on this combination a new dichotomous variable was formed in which the score “1” corresponded to the “symbolic environmental behavior” (i.e., considering those facilities that simultaneously had not experienced improvements in their environmental performance but whose managers considered corporate image to be very important motivation in the adoption of environmental practices) and “0” corresponded to no such symbolic behavior (i.e., the remainder of the cases). Table 1 shows the descriptive statistics and correlations of each of the OECD items.

Table 1. Descriptive statistics and correlations.
N = 1961 facilities 1 2 3 4 5 6 7
2. Use of natural resources .255** 1.00
3. Solid waste generation .262** .388** 1.00
4. Wastewater effluent .096** .376** .382** 1.00
5. Local or regional air pollution .097** .275** .293** .332** 1.00
6. Global pollutants .175** .324** .255** .281** .518** 1.00
7. Importance of corporate image (very important) .077** .066** .102** .066** .096** .079** 1.00
Mean .33 .53 .56 .43 .41 .33 .53
Standard deviation .470 .499 .496 .495 .492 .469 .499
Minimum 0 0 0 0 0 0 0
Maximum 1.00 1.00 1.00 1.00 1.00 1.00 1.00

• . Correlations are significant at |0.01| (bilateral).

Since the sample used in this work consists of facilities located in countries with heterogeneous environmental legislation, Table 2 shows the distribution of the sample size, differentiating, by rows, the proportion of facilities that participated in the sample by country and, by columns, the dependent variable “adoption of ISO 14001”, the explanatory variable “symbolic environmental behavior”, and the percentage of symbolic adoption of ISO 14001 over the total.

Table 2. Sample size differentiating by variables and country.
Na ISO 14001 adopters With symbolic environmental behavior ISO 14001 adopters with symbolic behavior
Total 1.961 645 535 154
U.S. 312 63 101 17
15.9% 9.8% 18.9% 11.0%
Germany 288 87 36 12
14.7% 13.5% 6.7% 7.8%
Hungary 212 56 99 23
10.8% 8.7% 18.5% 14.9%
Japan 762 327 188 77
38.9% 50.7% 35.1% 50.0%
Norway 137 41 38 11
7.0% 6.4% 7.1% 7.1%
France 111 35 32 6
5.7% 5.4% 6.0% 3.9%
7.1% 5.6% 7.7% 5.2%

a. Values on percentages show the proportion over the total of each variable for each country.

### 4.3. Statistical technique

The procedure used to test the hypothesis of this work is binary logistic regression. This technique is useful when trying to predict the relationship between a dichotomous dependent variable (in this case, adoption of ISO 14001: yes or no) and a set of explanatory variables (in this case, symbolic environmental behavior). The method used in this case was the step forward binary logistic regression. In the first step (base model), “improvements in environmental performance”, “importance of corporate image”, and the control variables “size” (measured by the number of employees in each facility) and “country” were included, whereas in the second step (full model) all variables contained in the base model were included and the explanatory variable “symbolic environmental behavior” was added. The coefficients estimated by the model, that is, Exp (B), may be used to ascertain the odd ratio of each independent variable introduced into the model. Thus, the values of Exp (B) represent the relationship between change in the probability of the dependent variable (i.e., adoption of ISO 14001) and change in a unit in the explanatory variable (i.e., symbolic environmental behavior) in the case of being statistically significant.

## 5. Results

Table 3 shows the measure of the models goodness of fit through the result of the classification. The diagonal of the classification table shows the successes between what is predicted and what is observed. The success percentage of the classification is between 67.2% and 72.7% in the base model (step 1) and in the full model (step 2) respectively. This increase in the success percentage manifests the significant improvement that the inclusion of the explanatory variable “symbolic environmental behavior” implies in the goodness of fit of the final model.

Table 3. Classification table.
Step 1 (base model) Step 2 (full model)
Observed Predicted Success percentage Observed Predicted Success percentage
No Yes No Yes
ISO 14001 Adoption No 1292 0 100.0 ISO 14001 Adoption No 1174 118 90.9
Yes 632 0 0.0 Yes 408 224 35.4
Global percentage 67.2 Global percentage 72.7

Table 4 shows the results of the binary logistic regression. Both models are statistically significant (χ2 = 316,766; p < .01 and χ2 = 324,728; p < .01 for base model and full model respectively). The R2 values are especially useful when comparing the R2 values of two models that use the same data, the fit being better in those models with higher R2 value. In this case, the increasing progression of the R2 value (e.g., from 0.211 to 0.216 in Nagelkerkes R2) shows that the inclusion of the explanatory variable “symbolic environmental behavior” improves the explicative quality of the full model.

Table 4. Binary logistic regression results.
Base modela Full modela
B S.D. Exp(B) B S.D. Exp(B)
Constant −2.998 .203 .050*** −3.168 .213 .042***
Environmental performance .312 .033 1.366*** .388 .043 1.474***
Importance of image .364 .112 1.439*** .094 .147 1.098
Size .001 .000 1.001*** .001 .000 1.001***
Germany .749 .213 2.116*** .733 .213 2.081***
Hungary .623 .228 1.864** .596 .227 1.815**
Japan 1.625 .179 5.079*** 1.618 .178 5.043***
Norway 1.074 .255 2.926*** 1.066 .255 2.904***
France .866 .272 2.377*** .846 .272 2.330**
Canada .347 .268 1.414 .365 .267 1.440
Symbolic behavior .526 .187 1.691**
Chi2 block 316.766*** 7.962**
Chi2 model 316.766*** 324.728***
−2 log likelihood 2119.397 2111.435
Cox & Snell R2 .152 .155
Nagelkerke R2 .211 .216

a. The dependent variable is “ISO 14001 adoption” (yes or no); U.S. is the excluded “country” dummy.

• . p < .05.
• . p < .01.

In the base model, the estimated coefficient for the variable “improvements in environmental performance” (B = 0.312, p < .01) is positive and statistically significant, a result that corroborates previous literature that defends the existence of a positive relationship between the adoption of ISO 14001 and improvements in environmental performance ( Delmas, 2001 ;  Russo, 2009). Similarly, also in the base model, the estimated coefficient for the variable “importance of corporate image” (B = 0.364, p < .01) is positive and statistically significant, which shows the positive relationship between the adoption of ISO 14001 and managers’ motivation for improving corporate image as very important when adopting environmental practices in the firm.

With regard to the full model, the estimated coefficient for the variable “symbolic environmental behavior” (B = 0.526, p < .05) is positive and statistically significant, indicating the existence of a positive relationship between the adoption of ISO 14001 and symbolic environmental behavior. The interpretation of this result, by the value of Exp (B), for the explanatory variable indicates that the probability of adopting ISO 14001 is 1.691 times more likely when a symbolic environmental behavior exists, everything else remaining constant. The change in the probability of the dependent variable to a change of the explanatory variable is calculated as follows:

 ${\displaystyle {\mbox{Likelihood}}{\mbox{ }}({\mbox{ISO}}{\mbox{ }}14001{\mbox{ }}{\mbox{adoption}})=}$${\displaystyle {\frac {Exp{\mbox{ }}(B)}{1+Exp{\mbox{ }}(B)}}={\frac {1.691}{1+1.691}}=}$${\displaystyle 62.84\%}$

Consequently, the probability of the adoption of ISO 14001 increases by 62.84% when there is a symbolic environmental behavior. This result supports the hypothesis of this study that states that the higher the firms symbolic environmental behavior, the more likely it is to adopt ISO 14001.

## 6. Discussion, conclusion, and implications

One of the main criticisms of ISO 14001 refers to its questionable potential to develop a firms capacity related to the reductions in negative environmental impacts, which can cannibalize confidence in the standard as a consequence of providing a symbolic signaling of the environmental behavior of the firm. This research has examined the relationship between the firms symbolic environmental behavior and the adoption of ISO 14001. The results contribute to the previous literature that has studied the symbolic adoption of ISO 14001 (Aravind and Christmann, 2011; Castka and Prajogo, 2013; Iatridis and Kesidou, 2016 ;  Yin and Schmeidler, 2009), indicating that the more symbolic the environmental behavior of the firm, the greater the likelihood of adopting ISO 14001.

Firms with symbolic profiles try to gain legitimacy through the adoption of ISO 14001 but they do not necessarily achieve improvements in environmental performance. Consequently, this symbolic adoption of ISO 14001 results in corporate behavior that contributes to the degradation of confidence in the standard. It is important to note that ISO 14001 is adopted not only by firms with symbolic environmental behavior, but also by environmentally committed firms. However this lack of differentiation between these two groups involves combining under one label (i.e., “firms with ISO 14001”) both symbolic behaviors (without significant improvements in environmental performance) as well as sincere behaviors (with significant improvements in environmental performance), thus undermining the confidence of the standard.

The results of this study open up new lines of research in relation to the symbolic adoption of environmental practices in general, and ISO 14001 in particular. First, once again demonstrating the link between symbolism and the adoption of ISO 14001, it would be particularly interesting to know whether this symbolic behavior is associated with improvements in profitability (i.e., economic and financial results), even differentiating between firms with and without ISO 14001. Second, the literature has shown that symbolic adoption may be facilitated by the weakness of external audits as a result of their lack of rigor (e.g., Aravind and Christmann, 2011; Boiral, 2011; Curkovic and Sroufe, 2011 ;  Heras-Saizarbitoria et al., 2013). At times, external audits do not really evaluate the integration of environmental practices in the firms decision-making, neither are they focused on measuring the evolution of the improvements achieved, if any (Heras-Saizarbitoria et al., 2013). Future studies might analyze how, and how much, the rigor of these external environmental audits affects the development of environmental symbolic (or de facto) behaviors.

One limitation of this study is the use, from a methodological point of view, of symbolic environmental behavior as a variable formed from the combination of several items. The measure of the symbolic environmental behavior offered here opens the possibility for future work that might consider alternative ways to measure this variable, for example, by using both primary information (i.e., surveys) and secondary information. Finally, although this work has considered the main criticisms concerning the symbolic adoption of ISO 14001, an in-depth study on the confidence of certifier firms is highly recommended, especially in contexts with high levels of political corruption (Montiel et al., 2012). A further research theme might also focus on whether managers today are prioritizing investment in the adoption of ISO 14001 or “decertificating” due to the economic recession (Heras-Saizarbitoria et al., 2016).

This study also provides important contributions for managers. Some firms are reluctant to adopt ISO 14001 due to the excessive bureaucracy that the standard requires (Aravind & Christmann, 2011). In fact, Curkovic and Sroufe (2011, pp. 75) argue that some of the main criticisms of ISO 14001 are based on “a limited focus on continuous improvement” and “the ability of a registered company to still produce large amounts of waste”. The results of this study suggest that these criticisms can be overcome by the substantive adoption of ISO 14001, rather than its symbolic adoption, since it is possible that managers who choose this symbolic adoption would not obtain all the benefits that the standard is capable of generating for the firm.

## Acknowledgments

Vera Ferrón Vílchez thanks the Spanish Ministry of Education (Research Project ECO2012 31780), the Andalusian Regional Government (Research Project P11-SEJ7988), and Nicole Darnall for her collaboration and contribution. She also thanks the constructive comments made by two anonymous reviewers and by several members of the Research Group ISDE (SEJ-481).

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## Notes

1. An EMS is “a formal system for articulating goals, making choices, gathering information, measuring progress, an improving performance” ( Florida & Davison, 2001: 64).

2. Asymmetric information problems occur when information about a transaction between a supplier and a buyer is not available equally to both (King et al., 2005).

3. The signaling is understood as activities that firms adopt in order to try to demonstrate that they have certain characteristics that, in other circumstances, would be hidden from third parties (Montiel et al., 2012).

4. DiMaggio and Powell (1983) argued that organizations operating in similar institutional contexts tend to exhibit isomorphism, i.e., a consistent behavior pattern among them. Specifically normative isomorphism refers to the professionalization of certain management practices in the industrial sector.

5. The author is grateful for the collaboration of Professor Nicole Darnall, one of the researchers who participated in the survey elaboration.

6. For more detail, see Ferrón Vílchez and Darnall (2016).

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Try this beautiful problem Based on Integer useful for ISI B.Stat Entrance.

## Integer| ISI B.Stat Entrance | Problem 72

The number of integer (positive ,negative or zero)solutions of $xy-6(x+y)=0$ with $x\leq y$ is

• 5
• 10
• 12
• 9

Integer

Algebra

Divisor

## Check the Answer

But try the problem first…

Answer: 10

Source
Suggested Reading

TOMATO, Problem 72

Challenges and Thrills in Pre College Mathematics

## Try with Hints

First hint

Factorize the given equation

Can you now finish the problem ……….

Second Hint

Find the divisor

can you finish the problem……..

Final Step

Given equation is $xy-6(x+y)=0$

$\Rightarrow xy-6x-6y=0$

$\Rightarrow xy-6x-6y+36=36$

$\Rightarrow (x-6)(y-6)=3^2 \times 2^2$

Now the numbers of factpr of $36=9$ i.e $\{1,2,3,4,6,9,12,18,36\}$

Thus we may say that 36 has 9 positive divisors, and 9 negative. and x=0 and y=0 is also a solution

the given condition $x\leq y$ ,so there are 10 non-negetive solution