Compare and contrast the three (3) collaboration technologies clearly identifying at least two (2) strengths and weaknesses for each of the three (3) technologies.

Assignment 08

BZ400 Strategic Information Technology

Directions: Be sure to make an electronic copy of your answer before submitting it for grading. Unless otherwise stated, answer in complete sentences, and be sure to use correct English spelling and grammar. Sources must be cited in APA format. Your response should be four (4) double-spaced pages in length; refer to the “Format Requirements” page for specific format requirements.

discuss what collaboration technologies are and how they function. Then clearly describe three (3) examples of collaboration technologies. Compare and contrast the three (3) collaboration technologies clearly identifying at least two (2) strengths and weaknesses for each of the three (3) technologies.

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The Roles of Ethics and Individual-Level Cultural Characteristics in Predicting Organizational Computer Abuse

Is Your Banker Leaking Your Personal Information? The Roles of Ethics and Individual-Level Cultural Characteristics in Predicting Organizational Computer Abuse

Paul Benjamin Lowry • Clay Posey •

Tom L. Roberts • Rebecca J. Bennett

Received: 29 November 2010 / Accepted: 3 April 2013 / Published online: 25 April 2013

� Springer Science+Business Media Dordrecht 2013

Abstract Computer abuse (CA) by employees is a criti-

cal concern for managers. Misuse of an organization’s

information assets leads to costly damage to an organiza-

tion’s reputation, decreases in sales, and impositions of

fines. We use this opportunity to introduce and expand the

theoretic framework proffered by Thong and Yap (1998) to

better understand the factors that lead individuals to com-

mit CA in organizations. The study uses a survey of 449

respondents from the banking, financial, and insurance

industries. Our results indicate that individuals who adhere

to a formalist ethical perspective are significantly less

likely to engage in CA activities than those following a

utilitarian ethical framework. In addition, the results provide

evidence that employees with individualistic natures are

linked to increased CA incidents, whereas collectivist ten-

dencies are associated with decreases in CA behaviors. Our

results also show that collectivism acts as a strong moderator

that further decreases the relationships between formalism

and CA, and utilitarianism and CA. Finally, we offer

detailed suggestions on how organizations and researchers

can leverage our findings to decrease CA occurrences.

Keywords Culture � Computer abuse � Deontological evaluations � Ethics � Formalism � Information security � Organizational security � Teleological evaluations � Utilitarianism � Collectivism � Individualism

Abbreviations

CA Computer abuse

CMD Cognitive moral development theory

IS Information systems

IT Information technology

Introduction

An organizational issue of increasing importance is

employees abusing their access to computers and organi-

zational information assets, a phenomenon known as

computer abuse (CA) (Posey et al. 2011; Straub 1990),

which spreads in scope and impact as technology is used

more widely. Straub (1990) formally defined CA as ‘‘the

unauthorized and deliberate misuse of assets of the local

organization information system by individuals’’ (p. 257).

CA is a critical problem requiring corporate leadership to

resolve (D’Arcy and Hovav 2007; Lee et al. 2004; Peace

et al. 2003; Posey et al. 2011): In one study, nearly half of

Electronic supplementary material The online version of this article (doi:10.1007/s10551-013-1705-3) contains supplementary material, which is available to authorized users.

P. B. Lowry (&) College of Business, City University of Hong Kong, P7912,

Academic Building I, 83 Tat Chee Avenue, Hong Kong, China

e-mail: Paul.Lowry.PhD@gmail.com

C. Posey

Information Systems, Statistics and Management Science,

Culverhouse College of Commerce, The University of Alabama,

Box 870226, Tuscaloosa, AL 35487, USA

e-mail: cposey@cba.ua.edu

T. L. Roberts � R. J. Bennett Department of Management and Information Systems, College

of Business, Louisiana Tech University, P.O. Box 10318,

Ruston, LA 71272, USA

e-mail: troberts@latech.edu

R. J. Bennett

e-mail: rbennett@latech.edu

123

J Bus Ethics (2014) 121:385–401

DOI 10.1007/s10551-013-1705-3

http://dx.doi.org/10.1007/s10551-013-1705-3
the participants involved were aware of CA in their orga-

nizations (Hilton 2000); in another, approximately 30 % of

business professionals admitted to pirating their employers’

software (Haines and Leonard 2007). Many organizations

have also had to discipline employees for downloading

pornography at work or abusing email (Haines and Leonard

2007). Disgruntled employees rank second to outside

hackers as sources of system attacks (Haines and Leonard

2007); clearly, internal CA is a critical problem facing

organizations today (D’Arcy and Hovav 2007; Lee et al.

2004; Peace et al. 2003; Posey et al. 2011).

The negative impact of CA can be accounted for in the

loss of hundreds of billions of dollars caused by lost effi-

ciency, software piracy, security leaks, privacy violations,

legal liabilities, and the like (Culnan and Williams 2009;

Douglas et al. 2007; Gan and Koh 2006; Moores and

Dhaliwal 2004; Siponen and Vartiainen 2007; Son and Kim

2008; Thong and Yap 1998). However, many of the cata-

strophic losses are hard to calculate as they can affect the

core of an organization’s business in complex and profound

ways (Wang et al. 2008) such as lost reputation, lost sales,

and legal liabilities (Son and Kim 2008).

The ethical and cultural issues surrounding CA are of

unprecedented importance to organizations and thus are

management not just technical issues (Posey et al. 2011;

Ransbotham and Mitra 2009). The many ethical gray areas

and issues surrounding CA are particularly problematic

(Calluzzo and Cante 2004) because such issues often

involve moral hazard.1 Moral hazard has increased as

computer use has increased (Tuttle et al. 1997) because of

the ability to hide privately held information or the often

inaccurate belief that one can hide such information. Even

when no moral hazard exists, many forms of CA do not

have clear right and wrong implications for the abusers,

and thus, they have no ethical or moral incentive to not

commit the abuse (Calluzzo and Cante 2004; Cohen and

Cornwell 1989). Therefore, CA has strong ethical and

cultural foundations.

Given the serious management and organizational

issues caused by CA, and its expansive interrelationship

with ethics and culture, the purpose of this paper is

twofold: (1) to examine the degree to which one’s dis-

position toward ethical formalism or utilitarianism affects

one’s propensity to commit CA and (2) to examine the

degree to which one’s individual-level characteristics of

collectivism and individual influence one’s propensity to

commit CA.

Background on Investigating Computer Abuse

in Organizations

Due to the growing importance of CA-related issues in the

ethical use of IS, researchers have investigated various

methods to address these issues. The most traditional

approach to preventing CA has been to try to directly block

negative employee behaviors with technical measures.

Some of these approaches have included authentication and

identification (Wang et al. 2009; Zviran and Erlich 2006),

passwords and pass phrases (Keith et al. 2009; Zhang et al.

2009), firewalls (Cavusoglu et al. 2009), intrusion detection

(Cavusoglu et al. 2009; Hansen et al. 2007; Ransbotham

and Mitra 2009), rights management, countermeasures

(Ransbotham and Mitra 2009), and system controls (Rao

et al. 2007). Additional approaches include the use of

policies and procedures, computer monitoring (Ariss

2002), audit trails, IT audits (Merhout and Havelka 2008),

IS risk analyses (Sutton et al. 2008), IS security counter-

measures (Hansen et al. 2007; Straub and Welke 1998),

and general violation-prevention strategies (D’Arcy et al.

2009).

Other approaches have innovatively coupled psychology

with traditional approaches. These integrated methods

include using fear appeals (Johnston and Warkentin 2010),

leveraging employee perceptions of IT policy so policies

appear more mandatory (Boss et al. 2009), countering

neutralization techniques (Siponen and Vance 2010), and

using general deterrence theory (Herath and Rao 2009b;

Lee et al. 2004; Straub 1990) or related penalty-oriented

techniques (Herath and Rao 2009a).

Although these approaches have shown some efficacy,

the results are highly mixed. We posit that one possible

reason is that such studies have largely ignored individual-

level ethical and cultural characteristics that likely impact

one’s decisions about various kinds of CA. Thus, we assert

that both considerations need further examination.

The Case for Studying Individual-Level Ethics

in Computer Abuse

Ethics have long been acknowledged as a key individual-

level consideration in IT use. IT studies have used ethics-

based approaches in reviewing situational ethics (Banerjee

et al. 1998), IT development (Chatterjee et al. 2009),

decision making and moral reasoning relating to IT use

(Calluzzo and Cante 2004; Cohen and Cornwell 1989;

Davison et al. 2009; Harrington 1996; Leonard and Cronan

2001; Leonard et al. 2004; Loch and Conger 1995; Myyry

et al. 2009; Smith and Hasnas 1999), and reporting or not

reporting bad IT news (Smith and Keil 2003; Smith et al.

2001). Yet little research addresses ethics and CA. The

1 Moral hazard occurs when there is ‘‘an incentive to act in one’s

self-interest in conflict with the organization’s overall goals while

being able to hide those actions through privately held information’’

(Tuttle et al. 1997, p. 7).

386 P. B. Lowry et al.

123

primary examples of ethics and CA studies include specific

contexts of software piracy (Gan and Koh 2006; Moores

and Chang 2006; Moores and Dhaliwal 2004) and avoiding

privacy violations (Culnan and Williams 2009), but no

studies have applied ethics to the broader construct of CA.

Furthermore, an opportunity exists in studying the effect

of ethical dispositions themselves. Haines and Leonard

(2007) noted two approaches to studying ethics: one based

on dispositions and traits and the other based on the deci-

sion-making process. The IT literature is replete with

studies that examine the decision-making process in rela-

tion to appropriate and inappropriate technology-based

individual behaviors, yet little research has been conducted

in terms of individual ethical dispositions and traits. For

example, Winter et al. (2004) studied on Machiavellianism

and ethical idealism. A much more common research

perspective on ethical dispositions and traits, which has

produced several promising non-technology-related studies

in the management literature (Brady and Dunn 1995; Brady

and Wheeler 1996; Hunt and Vitell 1986; Schminke et al.

1997), involves the study of formalism and utilitarianism.

The promise of studying the effects of individual ethical

dispositions on CA yields our first research question:

RQ1 In what way(s) do individuals’ ethical tendencies

toward formalism or utilitarianism predict the individuals

propensity to commit CA in the workplace?

The Case for Studying Individual-Level Cultural

Characteristics in Computer Abuse

Husted and Allen (2008) indicated that researchers still

understand little about how culture affects perception and

evaluation of abusive practices such as software piracy and

other behaviors. Worse still, how cultural dimensions

might affect CA more broadly—particularly culture at the

individual level—has yet to be fully examined. This limi-

tation is a glaring gap because increased globalization and

heterogeneity in organizations make cultural consider-

ations of ethical decisions all the more important, espe-

cially because cultural differences—primarily based on the

collectivism and individualism dimensions—have been

consistently shown to promote substantial differences in

ethical decision making (Bailey and Spicer 2007; Beekun

et al. 2008; Davison et al. 2009; Husted and Allen 2008;

Husted et al. 1996; Patel and Schaefe 2009; Ralston et al.

2008; Robertson and Crittenden 2002; Schlegelmilch and

Robertson 1995).

The few studies that have addressed forms of CA from a

cultural perspective have looked at software piracy at an

exploratory national culture level. For example, studies

have shown that software piracy is less frequent in the U.S.

than in Asia (Donaldson 1996; Swinyard et al. 1990).

Husted (2000) found that software piracy is correlated to

national GNP per capita, income inequality, and collec-

tivism. More recently, another study found substantial

differences in software piracy and related corruption

practices at the national culture level (Robertson et al.

2008). Davison et al. (2009) applied CMD theory to IT

professionals working in Japan and China, and showed that

the ethical reasoning in these two national cultures had

notable differences.

These studies suggest that the cultural dimensions of

collectivism and individualism may play a key role in

ethical decision making. The conceptualization of culture

on the national level conforms to the traditional view of

culture and is most often seen in studies comparing indi-

vidualistic societies (e.g., US) and collectivistic societies

(e.g., China) (Chen and Li 2005; Hofstede 1984, 1991,

2001; Tsui and Windsor 2001; Zhang et al. 2008). The

national-level perspective has been traditionally used in

ethics research. Some studies have shown that moral and

ethical reasoning differs across national cultures, but have

done so primarily as exploratory research without a strong

theoretic basis or explanation (Robertson et al. 2002; Tsui

and Windsor 2001). Other studies have proposed an even

stronger theoretic basis that national culture can be a

determinant of ethical differences (Robertson and Fadil

1999; Tavakoli et al. 2003; Vitell et al. 1993).

However, there are several research concerns about

national-level cultural studies, particularly in comparing

individualism and collectivism (e.g., Earley 1989; Fiske

2002; McCoy et al. 2005; Srite and Karahanna 2006; Tri-

andis and Gelfand 1998). The chief limitation of national-

level studies is that increasingly, in a globalized world,

national cultures are becoming highly heterogeneous, and

thus, multiple cultural perspectives can be found within

cultures (McCoy et al. 2005). Thus, applying national-level

cultural characteristics to predict an individual’s behavior

can result in inaccurate and misleading stereotyping and

can be too dichotomous (Triandis and Gelfand 1998).

For instance, a 2002 meta-analysis evaluating the con-

struct of individualism–collectivism across 82 studies

compared cross-national and within-United States studies

of individualism–collectivism and found that the Japanese

sample scored significantly lower on collectivism than the

U.S. sample did and that the Korean sample was not dif-

ferent from the U.S. sample (Oyserman et al. 2002). This,

of course, is consistent with Hofstede’s (1984, 2001) caveat

that national cultural values may change over time; how-

ever, it calls into question the methodology of assuming

that nations have the same culture now as they did 40 years

ago when Hofstede characterized them. Oyserman et al.’s

(2002) meta-analysis demonstrated a between-group effect

(for the U.S. samples) for ethnicity on individualism, with

Individual Ethics and Cultural Characteristics in Predicting Computer Abuse 387

123

European Americans the most individualistic; however, the

effect was present only in comparison with Asian Ameri-

can groups and that effect was small. Similarly, recent

research demonstrates that individuals hold nationally

espoused cultural values to vastly different degrees, and

thus, the individual level of measurement of analysis is

more accurate and appropriate (Srite and Karahanna 2006).

Importantly, using individual-level cultural measures for

predicting individual behavior helps researchers avoid

ecological fallacies widely found in cultural research. That

is, per Hofstede (2002), country scores should not be used

to predict individual behavior. ‘‘Doing so is to commit

ecological fallacy, which assumes that one can validly use

ecological correlations (which apply to collective entities

such as groups) to substitute for individual correlations’’

(Srite and Karahanna 2006, p. 681). Subsequent research

shows that it is valid to study culture at the national,

organizational, and individual levels, but that the key is

using the proper level of measurement (Fischer et al. 2005).

Consequently, for this study, rather than use a 40-year-

old assessment of between-country differences on indi-

vidualism–collectivism or using respondents’ ethnicity as a

proxy for individualism–collectivism, we followed the

approach of many other researchers and measured indi-

vidualism and collectivism directly, at the individual level

(Earley 1989; McCoy et al. 2005; Srite and Karahanna

2006; Triandis and Gelfand 1998). This decision leads to

our second and last research question:

RQ2 To what extent do individuals’ tendencies toward

collectivism or individualism predict individuals’ propen-

sity to commit CA in the workplace?

Theoretic Model and Hypotheses

The thrust of the theoretic work in ethics-based outcomes has

focused primarily on the decision-making process, with less

focus on how dispositions and traits affect the outcomes. One

notable exception to this gap was a study by Winter et al.

(2004) that reviewed IT ethics in terms of Machiavellianism

and ethical idealism and found that those who lean toward

Machiavellianism were more likely to violate intellectual

property and privacy rights than those with ethical idealism.

However, a more prominent and accepted theoretic per-

spective is a body of theory and research that shows that an

individual’s disposition toward a moral philosophy based on

deontological or teleological evaluation maps directly to

one’s ethical dispositions toward formalism and utilitarian-

ism, respectively (Brady and Dunn 1995; Brady and Wheeler

1996; Hunt and Vitell 1986; Schminke et al. 1997). Yet this

well-accepted theoretic approach has not been applied in a

CA context. We do so here.

When employees commit CA in the workplace, they

knowingly violate implicit or explicit ethical rules of

conduct. Ethics can be defined ‘‘as an inquiry into the

nature and grounds of morality where morality is taken to

mean moral judgments, standards, and rules of conduct’’

(Thong and Yap 1998, p. 215). Accordingly, we follow the

standard raised by other computer-based ethics studies in

conducting research based on moral philosophies (e.g.,

Gattiker and Kelley 1999; Thong and Yap 1998). The use

of moral philosophies can provide a systematic perspective

for assessing the ethical appropriateness of individual

behavior, also called normative ethics theory (Thong and

Yap 1998).

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Calculate the Cumulative Empirical Distribution (CDF) for part c.

IE 151: Computational Methods in Industrial Engineering

Homework 3

Due: Thursday, March 21st at 11:59 p.m.

Before start, please read the following. 1. You should submit your homework through Canvas by Thursday, March 21st

11:59 pm. Note that the due date/time remains unchanged in any case.

1. In answering the solutions, you must submit your document along with

EXCEL files indicating that you have actually implemented the commands in

EXCEL. (i.e., electronic submissions must have snapshots of all commands in

EXCEL.)

1. You are responsible to send the correct file. If your file is corrupted or not

opened, you will get ZERO.

1. For this homework, late submissions are allowed. The late submission due

is Friday, March 22nd with 30% penalty.

1. You are allowed to share ideas with other students in the class, but your

submission must reflect your own effort in solving the questions. Cheating,

if proved, is subject to cheating policy stated in the course syllabus.

Good Luck! 

Question 1 (10+10 = 20 points)

An Internet service provider company has installed a modem to serve the needs of 1 customer

in an arbitrary region. Suppose that once a connection service is requested by a customer, one

unassigned modem must be immediately assigned to the customer or the request will be denied.

At each time period, a customer may request for a connection service with a probability of 0.7.

a. What is the probability distribution of a customer request for a connection? It suffices to

write the exact formula.

b. Use Excel to generate 10 random numbers for customer requests with Bernoulli

distribution with parameter p = 0.65 (please also add the screenshot of Excel in your

answer sheet)

Question 2 (10+10 = 20 points)

Consider the following system with 15 identical components. As you may guess, this system is

functional provided that every single component is working. Hence, once one of the component

is broken, the system stops working. Each of the components is assumed to independently work

with a probability of 0.8 over a day.

a. What is the probability that this system works the whole day?

b. Now use Excel to compute the probability formula that you provided to part (a).

Question 3 (6++6+6+6+6 = 30 points)

Two dices are rolled at the same time and the squared difference between their top faces is

reported.

a. What is the probability that the squared difference of the top faces is 0?

b. What is the probability that the squared difference of the top faces is 1?

c. In general, how can we calculate the probabilities for all possible values of the squared

difference of the top faces in Excel?

d. Compute the Cumulative Empirical Distribution (CDF) for part c.

e. Plot column charts for both probability (PDF) and CDF.

Question 4 (5+5+5+5 = 20 points)

For each of the parts (a) through (d), write a syntax in Excel.

a. Generate a real number in the range [0,15].

b. Generate a real number in the range [11,19]

c. Generate an integer number in the inclusive range from 1 to 10

d. Generate a fractional number with its integer nominator extracted randomly in the

inclusive range 1 to 2 and its integer denominator extracted randomly in the inclusive

range 1 to 4

Question 5 (10 points)

Using Excel calculate the matrix multiplication. (Hint: use MMULT(array1,array2))

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Training And professional Development

Prior to beginning work on this discussion, read the articles How HR Can Support Better IDPs (Links to an external site.)Links to an external site. and Developing Employee Career Paths (Links to an external site.)Links to an external site.. Complete and attach the following individual development plan (IDP) based on your own career goals. After you complete your IDP, discuss the training involved and how you can assess the effectiveness of the trainings. Explain HR’s role in impacting the career development of employees. How have virtual training methods and technology impacted HR’s role in employee development?

Your initial post must be a minimum of 300 words and include your IDP attached. Cite at least one scholarly source to support your response.

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