Business Information System for the Control of Workforce Through

Behaviour Monitoring Using Reactive and Terminal-based Mobile

Location Technologies

Sergio Ríos-Aguilar

, Francisco-Javier Lloréns-Montes

and Aldo Pedromingo-Suárez

Engineering and Architecture Department, Pontifical University of Salamanca, Madrid, Spain

Management Department, University of Granada, Granada, Spain

Keywords: Workforce Control, LBS, Behaviour-based HR Control, BYOD, Consumerization.

Abstract: This paper analyzes the viability of the use of employees’ smartphones following the BYOD paradigm as a

valid tool for companies in order to conduct presence control (primarily for remote workforce). A Mobile

Information System is also proposed for Presence Control using exclusively terminal-based reactive

location technologies, meeting cost minimization and universal access criteria. Qualitative and quantitative

references are proposed, adequate to the location information accuracy demanded in different business

remote workforce control scenarios, and taking into consideration the strictest international regulation in

force relevant to the location of individuals in Emergency Systems, promoted by the North American FCC.

A prototype for the proposed Information System was developed to evaluate its validity under different real

world conditions, and valuable information was obtained on the accuracy and precision of location data

using real devices (iOS and Android) under heterogeneous connectivity conditions and workplace premises.

1 INTRODUCTION AND

DEFINITION OF THE

PROBLEM

Nowadays it is commonly accepted that companies

must use Information Systems that allow the

collection and organization of all the information

available to their disposal in order to help the

success of the company’s business strategy. The

increasing competitiveness of the actual market

forces companies to try to have a deeper

understanding of the relationship of cause and effect

of their actions on profitability, being necessary to

have specific information that guides the process of

improvement of their competitive performance

(Bradley, 1998; Lahoz y Camarotto, 2012).

Performance measurements related to time,

quality and productivity complement financial

measurements, and allow the introduction of

improvements in operational processes. Referring to

the importance of time as a key factor in the

performance of task completion, Ballard and

Seibold, (2004) identified ten dimensions of time in

the workplace. Among them, the lack of punctuality

and absenteeism can be regarded as the most

persistent obstacles that affect to business

competitiveness (Campbell et al. 2012).

Early detection, evaluation and rapid intervention

are crucial when managing tardiness and absences in

the workplace, and help prevent them from

becoming a serious problem for the competitiveness

of companies.

Usually, to achieve such detection, Information

Technologies investment is required, among other

things for the acquisition and implementation of

Control, Access and Presence Systems, which are

often expensive, not only because of the initial costs

(equipment for physical identification using card

reader technology or biometric identification), but

also the maintenance of the equipment and software

that form the system’s back-end, not to mention the

possible costs for the integration with pre-existent

Information Systems (Sen et al. 2009; Kauffman et

al. 2011).

On the other hand, this kind of systems is proven

to be ineffective when it comes to extend the control

to mobile workforce, which in numerous private

service sector enterprises it may represent a high

percentage of the staff due to the very nature of its

business. In this case what is needed is a “proof of

presence” in places and times established in advance

Ríos-Aguilar S., Lloréns-Montes F. and Pedromingo-Suárez A..

Business Information System for the Control of Workforce Through Behaviour Monitoring Using Reactive and Terminal-based Mobile Location

Technologies.

DOI: 10.5220/0004872200300038

In Proceedings of the 16th International Conference on Enterprise Information Systems (ICEIS-2014), pages 30-38

ISBN: 978-989-758-027-7

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

(Kumar & Pandya 2012).

The implementation of support for mobile

workplace by the introduction of Mobile

Information Systems (mobile devices and

applications engineered for the mobile

environment), that allow the control of said spatial

and temporal dimensions of a mobile work, grants

not only a competitive advantage but also labour

productivity growth to companies (Yuan et al.

2010).

1.1 Opportunities

In the current economic context, it is of vital

importance for a business to improve their

competitiveness by rationalizing the necessary

investment to achieve it. As has already been

highlighted, having a Mobile Information System

that permits the rational and non-intrusive control of

the workforce, is one of the direct and effective

means of achieving such improvement.

Two unique situations have been detected that

can allow small and medium enterprises (SMEs) to

have a presence control Information System for both

local and remote workforce with a very reduced cost

and with minimum need of infrastructure:

(A) The current maturity of mobile location

technologies, using different transparent

positioning mechanisms (A-GPS, GPS, WiFi

and Cell-ID).

(B) The growing trend BYOD ("Bring Your Own

Device") allowing employees the use of their

own mobile communication devices

(smartphones, tablets…etc) in the business as a

complementary tool that takes a double role as

a personal device for private use and as an

access to the company’s Information Systems.

On this basis, this paper tries to ascertain if it is

feasible the implementation of an Information

System for the behaviour-based control of workforce

that makes a non-intrusive use of technology for

getting the employees locations from their own

smartphones, regardless of the make and model they

have.

This paper first analyzes the viability of the use

of employees’ smartphones following the BYOD

paradigm as a valid tool for companies in order to

conduct presence control (primarily for remote

workforce). Then, a Mobile Information System is

proposed for Presence Control using exclusively

terminal-based reactive location technologies,

meeting cost minimization and universal access

criteria.

Later, this paper proposes qualitative and

quantitative references, adequate to the location

information accuracy demanded in different business

remote workforce control scenarios. And finally, this

paper discuss the results of testing the accuracy and

precision of location data using real devices (iOS

and Android) under heterogeneous connectivity

conditions and workplace premises

1.2 Granularity in the Control of

Workforce

It has been noted that the use of monitoring

technology can often lead to unwanted effects and

behaviours (Stanton 2000), particularly, continuous

monitoring with mobile location technologies

increases the occurrence of such effects (Weckert

2005). In this context, it would seem reasonable to

note that an Information System designed to avoid

this behaviours must use non-intrusive and reactive

location technologies (avoiding continuous

monitoring). (Ghose et al. 2012; Kumar Madria et al.

2002).

On the other hand, to quantify the level of

demand of accuracy suitable for the location of the

workforce, reviewing existing international

regulations on the subject, it was found that the

United States Federal Communications Commission

(FCC) E-911 mandate, concerning the precision of

the location of calls from mobile devices to

Emergency Service E911, is the most specific and

strict set of regulations (Table 1). And it also

provides a methodological framework for

verification processes regarding compliance. At

present, E-911 is the only regulation set that clearly

quantifies the required location accuracy. So, for the

proposed Information System, the most restrictive

location accuracy values of the aforementioned

regulations will be considered as a valid working

quantitative reference.

2 INFORMATION SYSTEM AND

PROTOTYPE

Taking into consideration all conditioning elements,

it is proposed in this paper an Information System

specifically oriented towards business use for the

behaviour-based control of remote workforce, using

smartphones under the BYOD paradigm, and

putting the focus on universal access (device

independence).

Additionally, a fully operational prototype of this

Information System has been developed; It consists

BusinessInformationSystemfortheControlofWorkforceThroughBehaviourMonitoringUsingReactiveand

Terminal-basedMobileLocationTechnologies

in a mobile web application along with a control

panel serving as a balanced scorecard and SaaS

(Software as a Service, in the cloud).

Table 1: Mobile devices location accuracy requirements

from which emergency service E-911 is requested.

Location

Type

Required

Accuracy

Regulation date

of entry into

force

Mobile

terminal

based

50 meters for

67% of the calls

and

150 meters for

80% of the calls

Already in force

(since 18/1/2013)

50 meters for

67% of the calls

and

150 meters for

90% of the calls

Scheduled to

enter into force on

1 January 2019

Source: Adapted from FCC (2010)

This prototype fulfills a double purpose of (i)

serve as a proof of concept for an Information

System for the control based on behaviour of the

remote workforce using a mobile web application,

and (ii) to allow an empirical analysis on the current

feasibility of its use under real conditions, using the

most strict obtained geographical location accuracy

requirements from mobile devices to emergency

services established by the FCC to be complied in

2019. (FCC 2011)

2.1 Description and Components of the

Information System

The proposed Information System for the behaviour-

based control of remote workforce consists of two

discrete functional components:

First, a Mobile Web application that will be

accessed by the workforce using their smartphone

mobile devices in order to clock in when arriving at

the customer’s facilities or other installations

deemed appropriate (Fig.2). This application also

allows to send remarks concerning the clocking in

that will be registered in the system in that moment.

Concerning the design, it was taken into account

the various factors that affect the customer perceived

usability, identified by Ho, (2012) y Lee, Lee,

Moon, & Park, (2012). Technologies such as

HTML5 and JQuery Mobile have been chosen due

to them being the most cost-effective for companies,

and also involve less costs in development as it

require just one code base (only one production line)

and it can be deployed in almost all mobile devices

that have a browser and access to the Internet

(Heitkötter et al. 2013; Zhu et al. 2013)

Secondly, a Control Panel Web Application for

the control of the remote workforce, accessible from

any Web browser, it allows real-time queries

regarding the workforce clocking in processes,

including georeferenced information in maps, as

well as make individual historical analysis of such

processes for each employee. This individual

historical analysis is in line with behaviour analysis

in the context of ubiquitous monitoring (Moran &

Nakata 2009).

Figure 1: Information System Architecture for behaviour-based control of the workforce.

ICEIS2014-16thInternationalConferenceonEnterpriseInformationSystems

The proposed Information System has a client-server

three-layered software architecture (See Figure 1).

On the other hand, it should be noted that the present

Information System has been modeled considering

also the system proposed in the general model of

location-based information system “Location Aware

Mobile Services” (LAMS), tailored for terminal-

based and network-assisted physical location of

mobile devices.A core principle of the proposed

Information System is ubiquitous access,

consequently all server components can be deployed

on the Internet o within a corporate intranet as

services provided using HTTP (Web), in a way so

that they remain accessible even if the corporation

has perimetral security solutions or traffic filters (in

this case, HTTP traffic is not usually restricted)

All the components used in the Server and

Database layers in this Information System are open

source software (LAMP platform: Linux OS,

Apache Web Server, PHP, and MySQL database), in

order to minimize costs involved in the deployment

of the proposed Information System.

Another benefit of this approach is related to

scalability; both vertical and horizontal scaling of

the LAMP platform are well known issues, and

there are several proven architectures that show

useful when there is the need to accommodate an

increasing workload.

3 LOCATION DATA ANALYSIS

METHODOLOGY

An empirical analysis is to be made to determine the

location accuracy obtained with mobile devices

using the developed mobile web application, in

order to verify the feasibility for business use as a

mobile device for the behaviour-based control of the

workforce and the effective application of the

BYOD paradigm.

Even though the accuracy of conventional GPS

receivers is well documented for various devices

(such as PNDs), the study on the accuracy of devices

with Assisted GPS (A-GPS), usually available in

smartphones like those intended to be used in the

proposed Information System, has not yet evolved as

much. Due to hardware limitations, it is to be

expected a worse performance concerning location

precision and accuracy using smartphones than

using a conventional GPS device (Zandbergen &

Barbeau 2011).

To study the feasibility of the use of location

data obtained from smartphones by means of a web

app in the proposed Information System, sets of GPS

location data under real conditions have been

collected. Location data obtained is compared with a

real known location (“truth point”), and with this an

absolute accuracy measurement will be obtained.

3.1 Instrumentation and Data

Collection

The GPS location data quality tests were done using

two of the most widespread smartphone mobile

terminals actually in the European market: Samsung

Galaxy SIII and Apple iPhone 4S. These devices are

also highly representative since they belong to two

of the most prevalent smartphone groups

(corresponding to the Galaxy and iPhone

trademarks) with the biggest market growth, and

evolving with constant improvements in their

hardware specifications.

Samsung Galaxy S III uses a Broadcom

BCM47511 GPS receiver chip with active

suppression of sources of interference. The device

used in the tests has Android v4.0.1 Operating

System. Apple iPhone 4S uses a Broadcom

BCM4330 GPS receiver chip and the operating

system used was iOS 6. Both mobile devices

registered geographical positions as decimal degrees

in the WGS-1984 datum, GPS original geodetic

reference system and direct equivalent to the

European ETRS-89 used in most topographic or

cartographic applications (IGN 2013). In both cases

the tests were made with the GPS circuits activated

and using network assisted mode (A-GPS),

additionally WiFi based location mechanisms were

active to help increase the accuracy.

The tests were carried out strictly during

standard business hours, at random times between

8h and 17h, representative of workforce daily

activities times in working days. Also, no value of

Horizontal Dilution of Precision (HDOP) was taken

into account for the completion of the observations,

no planning of field mission prior to the test was

conducted, thus better reflecting the use of the

Information System under real conditions by the

workforce.

The geographical coordinates of this point

(446737m, 4114616m UTM zone 30S, European

Datum 50) were obtained using a topographic grade

sub-meter accuracy professional GPS receiver

Topcon Hiper+ with a 10mm + 1.0ppm horizontal

accuracy, properly calibrated and using Differential

Global Positioning System (DGPS)

The mobile devices registered location data in

WGS-84 format, in intervals of 3 minutes,

BusinessInformationSystemfortheControlofWorkforceThroughBehaviourMonitoringUsingReactiveand

Terminal-basedMobileLocationTechnologies

generating data sets of 25 samples each. Data was

gathered, in different days, 10 sets of samples,

totaling 250 measurements for each study group.

Figure 1: Log-in screen requesting the user’s credentials to

access the Information System.

3.2 Data Analysis

To assess the accuracy of both devices used in the

gathering of the data, the variability in the data will

be contrasted against a previously determined real

value, corresponding to the observation point (truth

point).

To determine the possible normal distributions of

the data sets (variable: accuracy- planimetric or

horizontal error) several tests were conducted:

Kolmogorov-Smirnov, de Ryan-Joiner (similar to

Shapiro-Wilk) and Anderson-Darling (See Table 2)

Once again it is proved that the result is similar

for the three conducted tests, which provide p-values

of less than 0,05 which confirms with a 95% of

reliability that for both data origins, Galaxy SIII and

iPhone, the hypothesis of normality for horizontal or

planimetric accuracy is rejected.

Having verified that horizontal error distributions

(for accuracy) are not normal, the non-parametric

Mann-Whitney U test was used in order to contrast

if both data origins have the same distribution, the

obtained result is a statistical value W=54544,5, this

test is also statistically significant in 0,0000 (p<0,05)

therefore the hypothesis of equal distributions of

horizontal error (planimetric accuracy) for both

populations is also rejected with a 95% reliability.

In the data analysis it has been shown that the

average values of the precision variable and the

accuracy variable differ significantly for both mobile

devices.

Also it has been shown that deviations are

significantly different for both data origins.

Distributions are different not only in precision but

also in accuracy for both populations (Android and

iOS data). Both mobile devices provide data of

enough quality, and the mean of their values is close

to the real value, but a greater data dispersion in

found for iOS devices.

Table 2: Results of the normality tests of samples of

accuracy from both data origins.

Data origin device

Samsung Galaxy SIII

(Android)

Normality test Statistical p-value

Kolmogorov-

Smirnov

0,087 <0,010

Ryan-Joiner 0,972 <0,010

Anderson-Darling 3,581 <0,005

Data origin device Apple iPhone 4S (iOS)

Normality test Statistical p-value

Kolmogorov-

Smirnov

0,133 <0,010

Ryan-Joiner 0,946 <0,010

Anderson-Darling 8,126 <0,005

4 RESULTS

Figure 3 shows the horizontal error (in meters) of all

the gathered samples. This confirms, as evidenced

earlier, the variability of location data collected by

the iPhone device around the well known spot (truth

point) is greater than the data set collected with the

Android device.

For this reason, the collected data will be

analyzed quantitatively, performing the calculation

of the horizontal accuracy taking the root mean

square of the errors (RMSE) of each data origin, first

for each component and afterwards calculating the

planimetric or horizonal.value

(1)

RMSERMSERMSE 

(2)

4.1 Accuracy Results for the Android

Device

The planimetric RMSE error value obtained is

17,44m for the horizontal component (XY). This

value means that for a 67% of the observations made

with the Android device, the accuracy will be less at

RMSE





RMSE





ICEIS2014-16thInternationalConferenceonEnterpriseInformationSystems

17,44m. In this case, the value for the Samsung

Galaxy SIII device is 2DRMS = 34.88m.

It might be concluded that at a 95% confidence

level the accuracy obtained with the mobile web

application using the GPS of the Samsung Galaxy

SIII device is within a 34.88 meter radius from the

real location value in an urban environment with

demanding conditions for GPS signal propagation

and shifting HDOP values, non planned in the tests

conducted.

4.2 Accuracy Results for the iOS

Device

The planimetric RMSE value obtained is 33,74m for

the horizontal component (XY). This value means

that for a 67% of the observations made with the

iOS device, the accuracy will be less at 33,74m. In

this case, the value for the iPhone 4S device using

the mobile web application is 2DRMS = 67.48m.

It might be concluded, that with the samples

taken in this test, that at a 95% confidence level the

accuracy obtained with the mobile web application

using the GPS of the iPhone 4S device is within a

67.48 meter radius from the real location value in an

urban environment with demanding conditions for

GPS signal propagation, and shifting HDOP values,

non planned in the tests conducted.

5 VERIFICATION OF

COMPLIANCE WITH FCC

RULES

The FCC proposes a method for determining if a set

of location errors demonstrates compliance with

accuracy requirements of location data, using order

statistics. The FCC’s confidence intervals for

accuracy standards can be selected with a 90%

confidence based on the number of samples. (FCC

2000).

Generally, when

 The number of measurements is n,

 The r-th and s-th largest measurements are x

and

, respectively

 x and y are the percentile points associated with

probabilities p

y p

, respectively, then

the probability that x is less than x

, while

simultaneously y is less than y

, is given by the

formula

Figure 3: Horizontal error measurements for both devices.

BusinessInformationSystemfortheControlofWorkforceThroughBehaviourMonitoringUsingReactiveand

Terminal-basedMobileLocationTechnologies

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Being in this particular case p

=0.67 y p

=0.95

From this expression, upper bounds on the

percentile points can be determined, searching pairs

of values (r, s) for which the level of confidence

desired of 90% is achieved (Table 3)

As stated in Table 1, the most strict accuracy

requirements established by FCC to be met in 2019

are 50 meters for 67% of the samples and 150

meters for 90% of terminal based samples. If not, the

data set is rejected for not meeting the proper

standards. In this case, a data set of location errors

obtained using the mobile web application

(developed as part of the proposed Information

System) under real conditions, will be tested using

the methodology proposed by the FCC for the

assessment of compliance of accuracy requirements.

(FCC 2000)

To that end, data tables are sorted in ascending

order of accuracy for each observed location error,

and for each 100 samples obtained, according to

Table 3 it must satisfy the following expression

[(accuracy

< 50m) and (accuracy

100

< 150m)]

[ (accuracy

< 50m) and (accuracy

< 150m)]

5.1 Android Device Verification

First, the verification process will be performed with

those samples obtained using the mobile web

application in an Android device. The results show

that:

Accuracy

74-android

=19.2 m < 50m and

Accuracy

100-android

=41.1 m < 150m

Accuracy

75-android

=19.7 m < 50m and

Accuracy

99-android

=34.2 m < 150m

Therefore, it can be stated that following FCC’s

proposed methodology, with samples obtained with

the Android device, using the mobile web

application of the proposed Information System

under real world conditions, with a 90% confidence

level, the compliance of the strictest requisites set by

this organization is verified.

5.2 IOS Device Verification

Equally, a verification process will be performed

with the samples obtained using the mobile web

application in an iOS device. The results show that:

Accuracy

74-iOS

=35.2 m < 50m and

Accuracy

100-iOS

=68.2 m < 150m

Accuracy

75-iOS

=35.5 m < 50m and

Accuracy

99-iOS

=70 m < 150m

Table 3: Horizontal error value sample identification for

its comparison with thresholds of 67% and 95% as

required by the FCC for a 90% confidence level.

Sample size Test sample pairs

50 (x

, y

)

60 (x

, y

)

70 (x

, y

)

75 (x

, y

)

80 (x

, y

) o (x

, y

)

85 (x

, y

) o (x

, y

)

90 (x

, y

) o (x

, y

)

95 (x

, y

) o (x

, y

)

100 (x

, y

100

) o (x

, y

)

Source: Adapted from (FCC 2000)

Therefore, it can be stated that following FCC’s

proposed methodology, with samples obtained with

the iOS device, using the mobile web application of

the proposed Information System under real world

conditions, with a 90% confidence level, the

compliance of the strictest requisites set by this

organization is verified.

5.3 Results Discussion

The most important conclusion of these results is

that an Information System for the behaviour-based

control of workforce can always identify the

employee’s location with an accuracy level that

comfortably fulfills actual and future people location

requirements of the most strict Emergency Systems,

which nowadays provide the only valid quantitative

reference to validate the quality of people

geographical location data in this context.

This also entails the protection of the investment

made by the company in the implementation of the

proposed mobile based GPS Information System.

Due to the evolving nature of technological

innovation, mobile GPS receivers sensitivity will

increase still further, thus, obtaining more precise

and accurate results than those here obtained (Cao

et al. 2003; Elnahas & Adly 2000; Schiller &

Voisard 2004) Also, both iOS and Android devices

used in the verification process performed quite well

in terms of power consumption throughout the

working hours without battery recharges, and this is

specially important taking into consideration the

typical battery drain usually associated to the GPS

receiver circuit.

ICEIS2014-16thInternationalConferenceonEnterpriseInformationSystems

6 CONCLUSIONS

This paper proposes an Information System for the

behaviour-based control of workforce. It defines the

architecture and functionality, always in line with

business needs relative to control and taking into

consideration the implementation costs. The latter is

achieved by using open software technologies and

adapting those technologies to give adequate support

to the BYOD paradigm.

A prototype was developed and tested under real

world conditions, not evaluating strictly controlled

parameters of a device’s accuracy -like in empirical

tests-, but testing the Information System as a whole

in real conditions that reflect normal business

activity. Under these conditions, it has also been

verified the compliance with the strict accuracy

demands proposed here as a reference.

In the proposed Information System three

concepts, which until now have been evaluated

separately by previous studies, have been brought

together: (i) IT consumerization, (ii) actual

capabilities of personal mobile devices which

employees can use in the workplace, and (iii) the

opportunity represented by the new Mobile Web

technology which provides information on the move

-no matter the device being used and without any

app installation hassles-, and significantly reduces

costs in comparison to other technological options.

Lastly, the study establishes a new reference

framework regarding qualitative and quantitative

requirement levels which must be set in relation to

the accuracy of mobile location systems used in

business Information Systems, particularly those

related to the control of the remote workforce.

The results obtained in this study confirm that at

present it is viable for companies to implement an

Information System for the control of remote

workforce that allows the companies to gain

competitiveness, at the same time reducing costs and

increasing the ROI, adopting a BYOD paradigm

which allows their employees to use their own

smartphone mobile devices in the workplace.

As for future work, the ongoing research focuses

on evolving this Information System so that (i) it can

be integrated seamlessly with the different emerging

indoor positioning technologies, allowing the

companies to get more accurate position data of

remote workforce under very adverse indoor

conditions, and (ii) it can take advantage of

contactless technologies, like NFC, in order to

speed up the check-in process when the workforce is

at local premises, and to provide location proof

using the NFC tag as an extra authentication factor.

REFERENCES

De Assis Lahoz, M. & Camarotto, J. A., 2012.

Performance indicators of work activity. Work

(Reading, Mass.), 41 Suppl 1, pp.524–31. Available

at: http://www.ncbi.nlm.nih.gov/pubmed/22316777

[Accessed May 28, 2013].

Ballard, D. I. & Seibold, D. R., 2004. Organizational

Members’ Communication and Temporal Experience:

Scale Development and Validation. Communication

Research, 31(2), pp.135–172. Available at:

http://crx.sagepub.com/content/31/2/135.short

[Accessed May 31, 2013].

Bradley, S.P. /Nola., 1998. Sense and Respond: Capturing

Value in the Network Era, Harvard Business School

Press. Available at: http://dl.acm.org/citation.

cfm?id=550962 [Accessed May 31, 2013].

Campbell, B.A. et al., 2012. Who leaves, where to, and

why worry? employee mobility, entrepreneurship and

effects on source firm performance. Strategic

Management Journal, 33(1), pp.65–87. Available at:

http://doi.wiley.com/10.1002/smj.943 [Accessed

August 26, 2013].

Cao, H., Wang, S. & Li, L., 2003. Location dependent

query in a mobile environment. Information Sciences,

154(1-2), pp.71–83. Available at: http://dx.doi.org/

10.1016/S0020-0255(03)00035-5 [Accessed August

26, 2013].

D’Aveni, R., 1994. Hypercompetition, New York: Free

Press.

Elnahas, A. & Adly, N., 2000. Location management

techniques for mobile systems. Information Sciences,

130(1-4), pp.1–22. Available at:

http://dx.doi.org/10.1016/S0020-0255(00)00079-7

[Accessed August 26, 2013].

FCC, 2011. FCC strengthens enhanced 911 location

accuracy requirements for wireless services. July

2011. Federal Communications Commision. Available

at: http://hraunfoss.fcc.gov/edocs_public/attachmatch/

DOC-308377A1.pdf [Accessed May 29, 2013].

FCC, 2000. Guidelines for Testing and verifying Accuracy

of E911 Location Systems, Available at:

http://transition.fcc.gov/pshs/services/911-

services/enhanced911/archives/oet71.pdf.

FCC, 2010. Wireless E911 Location Accuray

Requirements, Second Report and Order, Sept. 2010.

Federal Communications Commision. Available at:

http://fjallfoss.fcc.gov/edocs_public/attachmatch/FCC-

10-176A1.pdf [Accessed February 5, 2013].

Ghose, A., Goldfarb, A. & Han, S.P., 2012. How Is the

Mobile Internet Different? Search Costs and Local

Activities. Information Systems Research,

p.isre.1120.0453–. Available at: http://

isr.journal.informs.org/content/early/2012/12/20/isre.1

120.0453.abstract [Accessed August 14, 2013].

Heitkötter, H., Hanschke, S. & Majchrzak, T.A., 2013.

Evaluating Cross-Platform Development Approaches

for Mobile Applications. In J. Cordeiro & K.-H.

Krempels, eds. Web Information Systems and

Technologies. Berlin, Heidelberg: Springer Berlin

BusinessInformationSystemfortheControlofWorkforceThroughBehaviourMonitoringUsingReactiveand

Terminal-basedMobileLocationTechnologies

Heidelberg, pp. 120–138. Available at: http://

link.springer.com/10.1007/978-3-642-36608-6

[Accessed June 5, 2013].

Ho, S.Y., 2012. The effects of location personalization on

individuals’ intention to use mobile services. Decision

Support Systems, 53(4), pp.802–812. Available at:

http://dx.doi.org/10.1016/j.dss.2012.05.012 [Accessed

August 26, 2013].

IGN, 2013. Geodesia- Instituto Geográfico Nacional.

2013. Available at: http://www.ign.es/ign/

layoutIn/faqgd.do [Accessed June 12, 2013].

Kauffman, R. J., Techatassanasoontorn, A. A. & Wang,

B., 2011. Event history, spatial analysis and count data

methods for empirical research in information

systems. Information Technology and Management,

13(3), pp.115–147. Available at: http://

link.springer.com/10.1007/s10799-011-0106-5

[Accessed August 26, 2013].

Kumar, M. D. & Pandya, S., 2012. Leveraging

Technology towards HR Excellence. Information

Management & Business Review, 4(4), pp.205–216.

Available at: http://0-search.ebscohost.com.cataleg.

uoc.edu/login.aspx?direct=true&AuthType=ip,cookie,

url,uid&db=bth&AN=74607680&lang=es&site=ehost

-live.

Kumar Madria, S. et al., 2002. Mobile data and transaction

management. Information Sciences, 141(3-4), pp.279–

309. Available at: http://dx.doi.org/10.1016/S0020-

0255(02)00178-0 [Accessed August 26, 2013].

Lee, J. et al., 2012. Factors affecting the perceived

usability of the mobile web portal services: comparing

simplicity with consistency. Information Technology

and Management, 14(1), pp.43–57. Available at:

http://link.springer.com/10.1007/s10799-012-0143-8

[Accessed August 25, 2013].

Moran, S. & Nakata, K., 2009. The Behavioural

Implications of Ubiquitous Monitoring. In 2009

IEEE/WIC/ACM International Joint Conference on

Web Intelligence and Intelligent Agent Technology.

IEEE, pp. 327–330. Available at: http://0-

dl.acm.org.cataleg.uoc.edu/citation.cfm?

id=1632189.1632323 [Accessed August 27, 2013].

Schiller, J. & Voisard, A., 2004. Location Based Services.

Available at: http://dl.acm.org/citation.cfm?id=983362

[Accessed June 10, 2013].

Sen, S., Raghu, T. S. & Vinze, A., 2009. Demand

Heterogeneity in IT Infrastructure Services: Modeling

and Evaluation of a Dynamic Approach to Defining

Service Levels. Information Systems Research, 20(2),

pp.258–276.

Stanton, J.M., 2000. Reactions to Employee Performance

Monitoring: Framework, Review, and Research

Directions. Human Performance, 13(1), pp.85–113.

Available at: http://dx.doi.org/10.1207/

S15327043HUP1301_4 [Accessed June 1, 2013].

Weckert, J., 2005. Electronic Monitoring in the

Workplace: Controversies and Solutions, Idea Group

Publishing.

Yuan, Y. et al., 2010. Identifying the ideal fit between

mobile work and mobile work support. Information &

Management, 47(3), pp.125–137. Available at:

http://dl.acm.org/citation.cfm?id=1752250.1752323

[Accessed May 22, 2013].

Zandbergen, P. a. & Barbeau, S.J., 2011. Positional

Accuracy of Assisted GPS Data from High-Sensitivity

GPS-enabled Mobile Phones. Journal of Navigation,

64(03), pp.381–399. Available at: http://

www.journals.cambridge.org/abstract_S03734633110

00051 [Accessed May 21, 2013].

Zhu, X., Chen, D. & Chen, Y., 2013. A resource

integration approach for HTML5 mobile applications.

Information Technology and Management. Available

at: http://link.springer.com/10.1007/s10799-013-0158-

9 [Accessed August 26, 2013].

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