Do online social networks support decision-making?

Decision Support Systems 70 (2015) 15–30

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Decision Support Systems

j ourna l homepage: www.e lsev ie r .com/ locate /dss

Do online social networks support decision-making?

Valeria Sadovykh a, David Sundaram a, Selwyn Piramuthu b,⁎a Department of Information Systems and Operations Management, University of Auckland, 12 Grafton Road, Auckland, New Zealandb ISOM Dept. University of Florida, 351 Stuzin Hall, Gainesville, FL 32611-7169, USA

⁎ Corresponding author.E-mail address: [email protected] (S. Piramuthu).

http://dx.doi.org/10.1016/j.dss.2014.11.0110167-9236/© 2014 Elsevier B.V. All rights reserved.

a b s t r a c t
a r t i c l e i n f o
Article history:Received 19 July 2014Received in revised form 7 October 2014Accepted 29 November 2014Available online 9 December 2014

Keywords:Online social networksDecision-makingParticipation style

The rapid adoption of online social networks (OSN) across different stakeholders raises several interestingquestions on different facets of its dynamics. Properly governed and designed OSN can play an important rolein supporting different types of decision making (DM), as they provide their participants/stakeholders variousforms of support, ranging from the instrumental to the emotional and informational. The synergy of these themesprovides an innovative and unique perspective on the actual process of DM within OSN. We use online surveymethod to address the potential utilization of OSN as a support tool for the DM process. Our results indicatethat OSN support and empower users in their decision making process specifically in three key phases thatinclude Intelligence, Design and Choice. Our results also reveal that different types of users (observers, seekersand advisers) have significantly different participation styles, which in turn have an impact on the efficacy ofthe DM process. We discuss policy implications for OSN designers based on results from this study.

© 2014 Elsevier B.V. All rights reserved.

1. Introduction

The history of decision making (DM) research is long, rich anddiverse. In terms of quantity, there is no shortage of frameworks, taxon-omies, approaches and theories. Decision making is a complex field; itcan involve the adoption of various technologies, in addition to the ac-commodation for different psychological perspectives of individuals.Over the years, the DM process has been extensively studied byresearchers. These studies have resulted in several dominant DMperspectives.

Before computer-mediated communication (CMC), people met andcommunicated with one another via face-to-face interactions. Thiswas achieved by making social connections within different types ofnetworks. A social network (e.g., [28,31]) is a social structure that con-sists of individuals who are interconnected with one another throughcommon interests, beliefs and/or values. For an excellent introductionto social networks, the interested reader is referred to Wasserman andFaust [29]. From the mid-nineties, social network research has evolvedto include online social networks. The idea of building a communitybased upon a common interest is of great interest within social networkresearch, with online social networks (OSN) as the primary focus.Researchers in this general area are interested in understanding andlearning about OSN: How are they used, and how do they affect oursocieties and businesses? Given their varied nature, OSN are multi-faceted, and researchers have explored various such facets over thepast several years. We study DM in OSN.

It is generally acknowledged that DM process comprises severalphases (e.g., intelligence, design, choice, implementation, monitoring),and the decision makers themselves have different DM styles(e.g., rational, dependent, intuitive, spontaneous). Moreover, at anygiven point in time in OSN, a participant decision maker plays a specificrole (e.g., adviser, seeker, observer). We study the dynamic among DMphases, DM styles, and decision maker roles in OSN. Specifically, thegoals of this study include understanding (1) how OSN are used as asupport tool for DM, (2) which DM phases are most used by OSNusers for DM, (3) how different stakeholder participation stylesinfluence the support for DM phases through OSN use, and (4) relatedpolicy implications for developers of OSNWeb sites. To operationalizeour study, we use online survey methodology to observe, elicit, andunderstand the problems and requirements of OSN support fordecision-making. Our results have policy implications for both OSN par-ticipants and designers.

The rest of the paper is organized as follows: We discuss necessarybackground and related literature in Section 2. In Section 3, we discussour online survey as well as develop hypotheses that we then testusing survey results. We conclude the paper with a brief discussion onthe contributions as well as limitations of this study in Section 4.

2. Background and related literature

Decision-making is a theoretical and practical concept that is affect-ed by cognitive insights of the decision maker. The process throughwhich people make decisions ranges from structured to the anarchical.We now discuss decision making and its phases as well as decision-making styles. We then follow this with a brief discussion on decision-making as it relates to online social networks (OSN).

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16 V. Sadovykh et al. / Decision Support Systems 70 (2015) 15–30

2.1. Decision-making (DM)

Extant literature in this area includes a vast selection of decision-makingmodels, frameworks and theories that work towards evaluationof the decision-making processes. While clear distinctions exist be-tween different decision-making artifacts, there are two dominantviews of decision-making. One view clearly supports rational decisionmaking, wheremodels are sequential, decisions are structured, process-es are analytical and solutions are terminated in a definite environment.In the other view, decision making is defined as an anarchical process,where problems are unstructured, decisions are irrational and the envi-ronment is uncertain.

Several variants and extensions of Simon's [26] seminal theory of ra-tional decision making have been proposed over the years. Many re-searchers followed Simon's view and even based further studies onSimon's rationality theory. For example, Mintzberg et al. [19] extendedthe Simon model by adding additional phases to the initial process;Rowe and Boulgarides [23,24] revised and modified the Simon modelby adding an additional decision maker to the DM process. March [18]proposed an anarchical view of the DMprocess with his theory of ambi-guity and bounded rationality. Supporters of this perspective focus on adifferent set of aspects of decision making and argue that the analyticaldecision-making approach does not covermost of the aspects of real-lifedecisions. Cohen et al. [3] view the DM process as a garbage can, wherethe solution does not have structure and choice, and alternatives can beretrieved at any point of the DM process.

Simon's [26] model is the most recognizable and acceptable amongexisting decision-makingmodels, so much so that it serves as the foun-dation for decision-making research. Simon [26] suggested that thedecision-making process can be structured and ordered in three phases:intelligence, design, choice. Intelligence is where the decision makercollects information about the problem, and identifies its cause(s). Thesecond phase is recognition and understanding of possible alternativesand consequences of the future decision. In the last phase – choice –

identified alternatives are narrowed down to the best utility optionthat leads to a decisionmaker's choice. Table 1 summarizes the taxono-my of the three phases of DM process.

Later, Huber and McDaniel [14] extended this model by adding twoother phases: implementation and monitoring. Implementation iswhen the decision is put into effect, and monitoring comprises thepost-analysis activities that evaluate the implementation of that deci-sion; feedback and possible adjustments are also used in the develop-ment of direction for future DM situations.

2.2. Decision-making style

Decision-making style provides an understanding of decision-makerbehavior that is taken for granted and unconsciously applied to decisionmaking [32]. To understand the decisionmaker's different styles it is im-portant for the development of a decision model that can deal with in-dividual behavior. Driver et al. [6] argue that the main differenceamongDMstyles occurs during information processingwhere the alter-natives are identified. An influence on selection among alternativecourses of action is recognized to be dependent on the decision maker'scognitive make-up [12].

Table 1Common operations in decision-making process.Adapted from: Malczewski [16].

Intelligence Design Choice

–Involves searching orscanning theenvironment forconditions calling fordecisions

–Involves inventing,developing, and analyzinga set of possible decisionalternatives for theproblem identified in theintelligence phase

–Involves selecting aparticular decisionalternative from thoseavailable

Most published empirical research in this area has focused on as-pects of the decisionmaker'smental abilities such as experience, knowl-edge, cognitive processes or factors that can influence the decisionoutcome. Chermack and Nimon [2] posit that measuring or developingthe specific indicators for decision-maker performance is extremely dif-ficult, but that it is essential to have an instrument that can study thepattern of decision-making performance. One of these instrumentswas developed by Scott and Bruce [25] tomeasure the DM style of an in-dividual. DM style has been defined as “a habitual pattern individualsuse in decision-making” (Driver, 1979, as cited in [25, p. 818]). Five de-cision styleswere identified, and are defined in behavioral terms: (1) ra-tional DM style is characterized by a thorough search for, and logicalevaluation of, alternatives, (2) intuitive DM style is characterized by asearch for advice and direction from others, (3) avoidant DM style ischaracterized by individuals who attempt to avoid the decision-making process entirely [8], (4) spontaneous decision makers have atendency to implement decisions immediately, and (5) dependent areindividuals who constantly search for advice and depend on directionfrom others [8]. The resulting instrument has been named the GeneralDecision-Making Style (GDMS) ([25, p. 820]).

The GDMS is designed tomeasure the participant's decision-makingtendencies towards the decision process. Even in the original model,Scott and Bruce [25] differentiate decision makers according to theirstyle; later, after testing the model, they came to the conclusion that adecision maker can rely on more than one style, but that is unlikely inthe case of opposing styles such as rational and spontaneous. Driveret al. [6] agree with Scott and Bruce and conclude that the decisionmaker has a primary and a secondary decision-making style.

2.3. Online social networks (OSN)

OSN have evolved from general friendship sites (i.e. Orkut,Facebook, Friendster, MySpace, and Classmates) to more specific user-orientated sites. OSN have grown from a small niche group of young-sters to a significant fraction of Internet users who generate the highestuser engagement rate [15]. While some of the OSN focus on growingglobally (e.g., Facebook, Youtube, Google Plus), others explicitly seek aspecific audience [1]. Examples of specific audience sites includeChristianity.com and MyChurch.com, with these sites or similar onestargeting a particular demographic of participants. Others deliberatelyrestrict access to selective individuals; an example is aSmallWorld.comwhich is said to be a network for elite only, where membership isstrictly through invitation.

There are dozens of OSN sites, each offering something unique to itsmembers. There are plenty of groups and classifications for distinguishingthe online social communities. The main criteria for classification aretaken from human interactionwith each other in an offline environment.

2.3.1. Decision-making process in online social networksTo understand how OSN can support the DM process, we go back to

the origin of the decision-making theory, specifically to Simon's [26]DM-process. OSN are capable of many things that can positively andnegatively influence the decision makers. The main question is howOSN can attenuate or amplify the decision-making process. OSN are in-formation portals, and consequently they influence human informationprocessing, where cognitive biases introduce barriers to adequatedecisions.

People useOSN to support various phases of theDMprocess that ful-fill the requirements to make a decision in a specific domain. OSN canattenuate or amplify the strengths and weaknesses of human informa-tion biases related to DM. This in turn can improve or disregard thedecision-making process.

The Internet and online communications are appealing to organiza-tions not only as a low cost way of reaching an audience, but also for theindividual thoughts, opinions and histories that are accessed throughthe global community of Internet users. There are an immeasurable

Table 2Hypothesis 1.

Hypothesis 1: OSN support DM process Hypothesis 0: OSN do not support DM process

Hypothesis 1A — OSN support the Intelligence phase of the DM process Hypothesis 0 — OSN do not support the Intelligence phase of the DM process.Hypothesis 1B — OSN support the Design phase of the DM process Hypothesis 0 — OSN do not support the Design phase of the decision-making process.Hypothesis 1C — OSN support the Choice phase of the decision-making process. Hypotheses 0— OSN do not support the Choice phase of the DM process in order to solve a problem.Hypothesis 1D— OSN support the Implementation phase of the DM process. Hypothesis 0 — OSN do not support the Implementation phase of the DM process.Hypothesis 1E — OSN support the Monitoring phase of DM process. Hypothesis 0 — OSN do not support the Monitoring phase of the DM process

17V. Sadovykh et al. / Decision Support Systems 70 (2015) 15–30

number of online community sites that are different or similar to eachother in terms of various technological features that support users andtheir practices. OSN sites have attracted millions of participants whohave integrated those into their daily lives. Online social networkingsites strive to fulfill the needs of participants through various means.

2.3.2. Users of online social networksUsers in online communities are unique based on their participation

style. OSN users are typified by their engagement levels in their partic-ipation communities. While some people may post message(s) everyday or once per week, others just collect information or knowledgefrom already developed conversations [10,21,22].

When the decision makers face a problem, they actively seek for in-formation that can assist them. The process of DM through use of OSNcan be explained in three steps.

1. The decision maker uses OSN to find information about decisionsthat can assist in the DM process. The information can be presentedas the source ofmodels, future choices, possible alternatives and con-sequences that come together with a decision.

2. OSN, by providing information, support the phases of the DM pro-cess; this support can be evaluated by understanding the problemthrough using OSN (intelligence); development of alternatives/determination of consequences of various courses of action(design); selection of an option/alternative/course of action andcomparison of consequences to resolve the problem by usingOSN (choice); identification of resources and implementing an al-ternative course-of-action to make a decision (implementation);and monitoring/evaluation of implemented decision using OSN(monitoring).

3. Following the phases of the DM process, by sequence or anarchy, isunimportant; the OSN supported DM process fulfills the decision-maker requirements.

The DM process by the use of OSN requires a proper investi-gation in order to understand the effectiveness of OSN in therole of decision supporter and information provider. We developand use a survey methodology to understand the dynamics ofDM in OSN.

3. Online survey

The primary focus of this survey is to identify how online social networks support the decision-making process. The data collected for thisstudy consisted of responses on how social networking services (SNS) and associated systems enhance or attenuate the decision-makingstrengths, weaknesses, and biases. We use the responses from online survey for hypothesis testing for decision-making in OSN. The surveyquestions are designed and based on the instruments extracted from the literature review. We used a pilot test as the simulation test to identifyany problems prior to the data collection process.

3.1. Hypotheses to be tested

The decision-making process can be regarded as a mental process that results in the selection of a course of action.Wemake our decisions basedon a decision process that involves five phases (intelligence, design, choice, implementation andmonitoring).When decisionmakers face a problemto be solved, they go through these five phases of DMprocess. In general, OSN are capable of providing support to the decisionmaker in the selectionof the five phases of the decision-making process to solve a problem.

The research question for Hypothesis 1: What are the phases that are most supported by OSN users?The research objective for Hypothesis 1: To compare each DM phase and its relationship to OSN use.

H1-1-Hypothesis 1. Online Social Networks support the decision-making process.

Explanation: Decision makers use OSN to support a certain DM process in order to solve a problem.

H1-0-Hypothesis 0. Online Social Networks do not support the decision-making process.

Explanation: Decision-makers do not necessarily use OSN to support a DM process to solve a problem.As discussed above, the decision-making process comprises five phases. To evaluate and test this hypothesis, and to identify the phases that are

supported by decision makers through use of OSN, we divided the main hypothesis into five associated hypotheses (Table 2).The second hypothesis tests the relationship between decision maker style (rational, dependent, intuitive and spontaneous) and phases of the

decision-making process (intelligence, design, choice, implementation and monitoring). Decision makers who tend to follow a particular style canuse OSN in a different manner that reflects the support function of OSN for the DM process. Decision-making style is an important indicator of deci-sion maker behavior that uses OSN to solve a problem.

The research question for Hypothesis 2: How do different decision maker styles influence the support function of OSN for DM process?The research objectives for Hypothesis 2: To explore how different decision maker styles influence the different phases of the decision-making

process.


Hypothesis 2: DM style of OSN users affects thedecision-making process.

Hypothesis 0: DM style of OSN users does not affectthe decision-making process.

Hypothesis 2A: Rational and dependent decision-makers tend to find support in most(N2) phases of the DM process, whereas spontaneous and avoidant tend to findsupport in fewer phases of the decision-making process by use of OSN.

Hypothesis 0: Decision-makers of any style tend to find similar support in phases of thedecision-making process by use of online social networks. There is no relationshipbetween decision-making style and support for phases of the DM process by use of OSN.The null hypothesis is that the two variables, DM process and DM style, are independent.Hypothesis 2B: Spontaneous/intuitive and avoidant decision-makers will use OSN to

support an intelligence and choice phase only.


H2-1-Hypothesis 2. Decision-making style of OSN users affects the decision-making process.

Explanation: The DM process followed is dependent on the decision-making style of the OSN user. Therefore, the supportive use of OSN fordecision-making will be different for each style.

H2-0-Hypothesis 0. Decision-making style of OSN users does not affect the decision-making process.

Explanation: The support use of OSN is not influenced by the decision-maker style.The DM style is divided into five characteristics. According to Scott and Bruce [25], the decision maker can rely on more than one style. For the

purpose of this study, two categories of DM style emerged: the first is rational and dependent and the second is spontaneous/intuitive and avoidant.Table 3 presents Hypothesis 2 and its sub-hypotheses that relate to the previously stated categorization of DM styles.

The third hypothesis tests the relationship between OSN users' participation roles and DM process phases.The research question for Hypothesis 3: Is there any difference in OSN participation roles towards the use of OSN as the support mechanism for

the DM process?The research objective for Hypothesis 3: Compare OSN participation roles with the five phases of the DM process that a decision maker goes

through in order to solve a problem.

H3-1-Hypothesis 3. OSN participation role affects the decision-making process.

Explanation: Diverse OSN participation roles will support the different phases or combination of phases of the decision-making process.

H3-0-Hypothesis 0. OSN participation role does not affect the decision-making process.

Explanation: OSN participation role and phases of the DM process are independent of each other. Therefore, the support function of OSNwill notbe affected by the participation role. The OSN participation role is split into three categories: Adviser, Seeker and Observer. For the purpose of thisstudy, advisers and seekers were grouped together as active users of OSN and observers as passive users of OSN. Table 4 lists the sub-hypothesesof Hypothesis 3 to test how the different groups of participation roles support the different phases of the DM process. The null hypothesis is thatthere is no relationship between participation role in the DM process with respect to any group or characteristic of participation roles.

3.2. Relevant instruments and literature

Developed by Scott and Bruce [25], the GDMS instrument typifies individual differences in decision-making habits and practices. The 25 state-ments contain five items forming five different scales, each measuring a distinct behavioral approach to decision-making: spontaneous, rational, in-tuitive, dependent, and avoidant [7] (see Appendix A). GDMS is used with the Likert scale to measure the scores that range from strongly disagree tostrongly agree [27]. Simon's [26] model divides the decision-making process into three stages: intelligence, design and choice. Huber [13] later ex-tended this model by adding two other stages: implementation and monitoring. We consider this model to be a research instrument that can directthe researcher to how OSN supports each phase of the decision-making process.

3.3. Data collection

We administered the survey online and distributed a link to the survey via social media. The primary target population had a loose definition be-cause the purpose of the study overall is exploratory, and therefore the opinion of individuals who use OSN for decision-making or for any other par-ticular purpose is essential. The data collection period took five months. This period does not include design or pilot testing activities. 113 OSN usersparticipated in completing the questionnaire and 73 completed responseswere obtained; the response ratewas 64.6%. It is important to note that outof 113 participants, 7 do not use OSN. Moreover, the response rate varied across different sections of the survey. Table 5 shows the number of par-ticipants who answered/skipped questions for each section with the associated response rate.


Hypothesis 3: OSN participation roles affects the DM process Hypothesis 0: OSN participation role does not affect the DM process.Hypothesis 3A: Seeker and Adviser tend to find support in most (N3) of the phases of the DM processHypothesis 3B: Observers tend to skip some of the phases of the DM process

Table 5Response rate for each section of the survey.

Sections Answered questions Skipped questions Response rate

Section A — general information 100 13 88.5%Section B — do you use OSN? 100 13 88.5%Section C — use of OSN 83 30 73.4%Section D — OSN features 70 43 62%Section E — DM process by use of OSN 83 30 73.4%Section F — DM style 78 35 69%Section G — biases in use of OSN (not compulsory) 72 41 63.7%


3.4. Data analysis

3.4.1. Descriptive statisticsAmajority of participants were from 18 to 34 years of age, the gender distribution was slightly skewed to the female dominance— 60% of partic-

ipants, and men presented 40% of the sample size. The life stage was equally distributed between singles and ‘in relationship’ participants, 34% and32% respectively, followed by 22% who were married. The majority of the typical respondents were full-time employed — 67% of the sample size.More than 15 countries were represented, with 61% of respondents living in New Zealand.

Most of the participants have used OSN sites formore than 3 years. The same distribution indicated that they are always logged on to socialmediaor at least have access several times a day. On the question of ‘howmanyOSN sites communities/groups are youmember of?’ — 42.2% of respondentsreported that they are members of 3 to 5 OSN sites and a similar percentage, 43.4%, reported that they use OSN for 75% personal purpose and 25%professional purpose.

The survey data shows a weak indication of professional users; the main explanation is the age of participants with a range of 18–34 years.According to DiMauro and Bulmer's [5] study on OSN and professional use, their sample presented that younger (20–35) professionals aremore active users of social media for professional purpose than middle-aged professionals.

With regard to participation style, 53% of respondents indicated that they are primarily observers, 31.3% are seekers and only 15.7% are advisers.The next question captured participation frequency, where results show that even with a small percentage of advisers, 24.1% of users provide adviceat least once every fewweeks, seek advice once in a few days— 25.3%, and observe advice several times a day— 44.6%. These results provide a goodindication that users can admit the fact that they use OSN for seeking and providing information. These answers are supported by the next question,where users indicate that OSN are about information search, knowledge, news and entertainment: “I use OSN because they allow me to”, and themost popular answers are: be informed and updated with current news— 78.3%, find information— 69.9%, find knowledge— 57.8%, and find enter-tainment— 56.6%.

Among these different uses of OSN, we are interested in how OSN are useful in terms of observing, advising, seeking and solving issues and prob-lems for decision-making. Most of the participants indicated that OSN are useful for seeking information and observing discussions with regard toparticular issues. 39.8% of participants found OSN to be a good tool for seeking information for professional issues, consumer issues — 39.8%, theneducational issues — 34.9%, and health issues — 33.7%. As for using OSN to observe discussions, most of the participants also relate to consumerand professional issues — 37.3%, health issues comprise the same percentage as for seeking information — 33.7% and the new groups of concern

Table 6Cognitive biases in DM by use of OSN.

Biases Bias increased by use of OSN

Decision-making and behavioral biasesBandwagon effect 69.0%Anchoring 48.6%Mere exposure effect 58.6%Choice-supportive bias 45.1%Selective perception 48.5%Framing effect 54.9%Confirmation bias 57.7Information bias 68.1%

Biases in probability and beliefBase rate neglect 47.1%Clustering illusion 50.0%

Social biasesEgocentric bias 47.9%False consensus effect 60.0%Projection bias 53.6%Illusion of transparency 65.7%Out-group homogeneity bias 50.7%

Memory errorsVon Restorff effect 48.6%


are personal relationships— 38.6%. Since thequestion allowed formultiple responses, the sumof frequencies does not equal 100%. A surprising aspectfrom these results is the high popularity in the use of OSN for seeking purpose and the high demand for seeking and observing professional issues.Even without statistical analysis, it can be seen that results contradict with the previous question where participants did not indicate ‘professional’as the primary reason for using OSN. That shows the design of the questionnaire to be very important; participants cannot always realize who theusers are without verbal elements of assistance [4]. This question gave an opportunity for users to provide free response. One of the responses isindicted below:

i m still young professional to advise smone on smth,while he canfind a solution for an issue from a professional with considerablymore exprnce than i ve got. When i advise,though, i do it out ofmyOWNexperience.When u seek for an advise frm social ntwrksto solve a problem i believe u should be sure abt whom u asking,because faceless interaction gives little guarantees on credibilityof the opinion

Hypothesis 1: Online Social Networks support the decision-makingprocessHypothesis 0: Online Social Networks do not support the decision-making process

This opinion expresses the concern with providing advice to others without being qualified to do so. The respondent is concerned with thequality of the provided information and subsequent results and makes a point that face-to-face advice is more valuable than interactionwith OSN users.

Section D captures the responses on OSN features and their relevance for DM. This section mainly serves the purpose of evaluating OSN featuresthat are useful forDM. Features that received thehighest relevant feedback are the ability of OSN to host forum conversations, tag people or links fromother OSN providers, share photos, notification updates, join groups of similar interests, and search information by subject of interest or categories.Security settings are found to be the category with the highest percentage of extreme relevance for OSN users for the DM process. The high impor-tance of security settings indicates that the main concern of OSN users is their privacy.

We discuss Section E of this survey in the next section on statistical analysis, as this section is mainly oriented towards answering the studyhypothesis.

Section F, Decision-Making Style. The GDMS instrument was modified and users had to choose one option that suits their DM style. 51.3% of re-spondents refer to themselves as rational decisionmakers and 20.5% selected an intuitive DM style. This question can be biased, “presumably truthfulresponse” (Hansen, 1980, as cited in [4, p. 27]) by information presentation and cognitive thinking; not everyonewants to admit that they are spon-taneous or dependent decision makers.

Section G enquires whether respondents think that OSN can have an effect on human cognitive biases. Questions in this section are purely infor-mative and were not designed for any statistical purpose. One of the study goals is to explore the effect of biases and their user's awareness. Most ofthe responses fell towards the opinion that biases increase with OSN use. Table 6 shows the response percentages for biases that people found to bethe most affected with OSN use.

The descriptive analysis of the survey helps build a profile of a typical user of the OSN service. For example, a user has used OSN for more than 3years and is amember of 3–5 sites, has a habit of accessing the networksmore than several times per day, but participatesmainly in the evenings andmost probably in a home environment. This user can be identified mainly as an observer, and uses OSN excessively for seeking information andknowledge for decision-making. The user is familiarwithmost of the OSN features and can use them according to their purpose and also understandsthe effect of biases that can influence the decision-making process.

3.4.2. Statistical analysisWe used appropriate statistical procedures that include chi-square and multivariate statistical analysis for hypotheses testing. The sum-

mary of the research questions, objectives, variables, hypotheses and statistical methods that are used in this data analysis is presented inTable 7.

From the three above-stated hypotheses,we test the phases of theDMprocess and its relationshipwith the use of OSN, participation styles of OSNusers and DM style. The data collected from Section D (Qs23–Qs29), Section F (Qs37), Section C (Qs17, Qs18) are used for statistical analysis to testthese hypotheses. The sample size includes only participants who use OSN in their daily lives, with respondents being identified from Question 9 bythose who answered “yes” to the question: “Do you use Online Social Networks?” The total response count for these participants is 93. The missingresponses were recorded and statistically handled with SPSS ‘missing values add-on module’. The missing values from all the questions wereremoved by using the ‘listwise deletion’ method. This is the most commonly used method for dealing with missing data and it introduces theleast bias into the statistical analysis [9,20]. We used listwise deletion on data that were included in hypotheses testing, and discarded cases withany missing variable values — only cases with complete records were included [20].

3.4.2.1. Hypothesis 1

Hypothesis 1 is divided into five sub-hypotheses that test how OSN supports each phase of the DM process. To show the statistical significanceof these hypotheses, we chose the chi-square test as the non-parametric test. The chi-square tests the independence between variables [17]. By


using the chi-square test, this hypothesis testing examines the relationship between two variables (OSN users who support the phases of the DM pro-cess and OSN users who do not support the phases of the DM process). The research hypothesis states that the two variables are dependent or related.The null hypothesis is that the two variables are independent [17].

3.4.2.2. Testing Hypothesis 1a

1 The Z-value was calculated using the formula Z ¼ffiffiffiffiffiffiffi

Chip

‐Square ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

14:520p

¼ 3:810:2 p-value = 1

2 (Asymp.Sig.) = (0.000) = 0.000.

Hypothesis 1a— Online Social Networks support the Intelligencephase of the decision-making process.Hypothesis 0 — Online Social Networks do not support Intelli-gence phase of the decision-making process.

Hypothesis 1b—OnlineSocial Networks support theDesign phaseof the decision-making process.Hypothesis 0—Online Social Networks do not support theDesignphase of the decision-making process.

This hypothesis tests the relationship between OSN users and the intelligence phase of the DM process. We claim the existence of relationshipbetween OSN users and support of an intelligence phase. Question 23 of the survey asked the respondents to choose the best option that re-flects their participation style from three options: ‘I use online social networks to help ME search for information to support decision-making’; ‘I provide information on online social networks to support the decision-making of OTHERS’; ‘I DO NOT use online social networksfor understanding problems’. This question allows multiple responses only for the first two options. If the respondent chose ‘I use online socialnetworks to helpme search for information to support decision-making’ and/or ‘I provide information on online social networks to support thedecision-making of others’, then the replies were combined and identified as supporters of the intelligence phase of the DM process as follows:‘Qs23Yes=Qs23(001)+ qs23(002)’ and labeled as ‘I Do Use’. Respondents who answered ‘I do not use online social networks for understand-ing of problems’were identified as non-supporters of the intelligence phase of the DMprocess in order to solve a problem andwere labeled as ‘IDo not Use’.

Qs23_Combined

Percent
Valid percent Cumulative percent
Valid
I do not use 18.6 28.0 28.0 I do use 47.8 72.0 100.0 Total 66.4 100.0
Missing
System 33.6 Total 100.0
The frequency of supporters of the intelligence phase (72%) was found to be greater than non-supporters of the intelligence phase (28%). Thisdifference was tested for significance using a Z-test for comparison of the two proportions. The results of the Z-test were found to be statistically sig-nificant (3.810; p b 0.05).

X2 (1) = 14.520, p b 0.05

Z = 3.8101; p-value = 0.002

Since p-value = 0.000 b 0.005 = α, the null hypothesis is rejected.The result shows that users who use OSN to support the intelligence phase of the DM process have a greater weight compared to non-supporters

of the intelligence phase. Under these terms the null hypothesis can be rejected and the conclusion can be drawn from this test that data support theresearch hypothesis. There is a relationship between OSN users and the intelligence phase of the DM process.

3.4.2.3. Testing Hypothesis 1b

This hypothesis tests the relationship between OSN users and the design phase of the DM process. We claim the existence of a relationship be-tween OSN users and the design phase (development of alternatives, determination of consequences). The design phase is divided into twoparts, the first being when the decision maker uses OSN for the development of alternatives to resolve a problem (Question 24). The second


part is when the decision-maker uses OSN for the determination of consequences of various courses-of-action for the developed alternatives(Question 25).

Hypothesis 1c—Onof the decision-makHypothesis 0c—Onlphase of the decisio

Hypothesis 1d—Ontion phase of the deHypothesis 0—Onlitation phase of the

Percent

line social networks support thing process.ine social networks do not supn-making process.

line social networks support thcision-making process.ne social networks do not supdecision-making process.

Valid percent

e Choice phase

port the Choice

e Implementa-

port Implemen-

Cumulative percent

Qs24_Combined
Valid I do not 19.5 29.3 29.3
I do
46.9 70.7 100.0 Total 66.4 100.0
Missing
Qs25_Combined
Valid I do not 30.1 45.3 45.3
I do
36.3 54.7 100.0 Total 66.4 100.0
Missing
A significant associationwas found betweenOSN users and support of the design phase (Question 24). By usingOSN for the decision-making pro-cess, users support the process of development of alternatives in order to resolve a problem. The frequency of supporters of the design phase−71%was found to be more than non-supporters −29%. The result of the Z-test shows statistical significance (3.579; p b 0.000).

X2 (1) = 12.813, p b 0.05. Since p-value = 0.000 b 0.005 = α, the null hypothesis is rejected.The second part of the design phase is the determination of consequences of already developed alternatives/courses of action (Question 25). The

frequencies of OSNusers that support the phase of determination of consequences (54%)were found to be insignificant to users of OSNwhodo not gothrough the same phase (46%). The result of the Z-test was found to be insignificant (0.808; p= 0.2095 N 0.000). X2 (1)= 0.653, p N 0.05. Sincep-value 0.2095 N 0.005 = α, we fail to reject the null hypothesis.

At theα=0.05 level of significance, there is not enough evidence to conclude that OSN users support the determination of consequences of var-ious courses-of-action in order to solve a problem. Thus, the results do not support the general hypothesis and OSN does not support the second stepof the design phase.

3.4.2.4. Testing Hypothesis 1cDecisionmakers, by use of OSN, find support in the selection of an option/alternative/course-of-action in order to solve aproblem (Question 26).

This hypothesis tests the relationship between OSN users and the choice phase of the DM process. We claim the existence of a relationship betweenOSN users and the choice phase.

Qs26_Combined

Percent
Valid
I do not 19.5 29.3 29.3 I do use 46.9 70.7 100.0 Total 66.4 100.0
Missing
X2 (1)=12.813, p b 0.05. The frequencies of OSN users who support the choice phase (71%)were found to bemore than the users of OSNwho donot support the choice phase (29%). The result of the Z-testwas found to be significant (3.5795; p b 0.000). Under these terms the null hypothesis canbe rejected and a conclusion can be drawn that there exists a relationship between OSN users and the choice phase of the decision-making process.Thus, the results support the general hypothesis that decision-makers use OSN to support the decision-making process.

3.4.2.5. Testing Hypothesis 1d


The implementation phase goes through two stages, first when the decision-maker uses OSN in order to find resources to implement an option/alternative/course-of-action (Question 27) and the second when the decision-maker goes through an actual implementation of option/alternative/course-of-action (Question 28).

Hypothesis 1e — Onphase of the decisioHypothesis 0e—Onlphase of the decisio

Percent

line social networks supportn-making process.ine social networks do not supn-making process.

Valid percent

the Monitoring

portMonitoring

Cumulative percent

Qs27_Combined
Valid I do not 18.6 28.0 28.0
I do use
47.8 72.0 100.0 Total 66.4 100.0
Missing
Qs28_Combined
Valid I do not 33.6 50.7 50.7
I do use
32.7 49.3 100.0 Total 66.4 100.0
Missing
The frequencies of OSN users that support the identification of resources step (72%) were found to be more than the users of OSN who do notsupport it (28%). The result of the Z-testwas found to be significant (3.810; p b 0.000). X2 (1)= 14.520, p b 0.05. At theα=0.05 level of significance,there is enough evidence to conclude that OSN support the phase of the decision-making process where users can identify resources in order toimplement an option/alternative/course-of-action to make a decision.

The second part of the implementation phase is the actual implementation of an option/alternative/course-of-action with identified resources(Question 28). The frequencies of OSN users who support the second step of the implementation phase (49%) were found to not be different tothe users of OSN who do not support it (51%). The result of the Z-test was found to be insignificant (0.11401; p = 0.454 N 0.000). X2(1) = 0.013,p = 0.9087 N 0.05. The hypothesis is not supported by this analysis and it can be claimed that OSN do not support an implementation of anoption/course of action in order to make a decision.

3.4.2.6. Testing Hypothesis 1e

Qs29_Combined

Percent
Valid percent Cumulativepercent
Valid
I do not use 32.7 49.3 49.3 I do use 33.6 50.7 100.0 Total 66.4 100.0
Missing
Test statistics

Determination

Chi-Square
.013 df 1 Asymp. Sig. .908
OSN users who support the monitoring phase are compared to OSN users who do not monitor an implemented decision (Question 28). The fre-quencies of OSN users that support themonitoring phase (51%)were found to not be different to the users of OSNwhodonot support themonitoringphase (49%).

X2 (1)=0.013, p N 0.05. The results do not support the general hypothesis. The result of the Z-test was found to be significant (0.11401; p=0.454 N 0.000). At the α= 0.05 level of significance, there is not enough evidence to conclude that participants use OSN for monitoring an im-plemented decision.


Four out of seven chi-square tests showed statistically significant results. Statistical significance has been found in the intelligence phase, partiallyin the design phase, choice phase and partially in the implementation phase. Table 8 shows the outcome for Hypothesis 1.

1. Decisionmakers participate inOSN to gain an understanding of a problem in order tomake a decision, and therefore OSN supports the intelligencephase of the DM process.

2. OSN support the first step of the design phase; where a decision maker can develop alternatives to resolve a problem.3. OSN support the choice phase of the DMprocess. Decisionmakers are able to select an option/alternative/course-of-action to resolve the problem

by using online social networks.4. OSN support the first step of the implementation phase. OSN users are able to identify resources to implement an option/alternative/course-of-

action in order to make a decision.5. OSN are not necessarily used for the monitoring phase of the decision-making process.

Results from our statistical analysis show that OSN supportmost of the phases of the decision-making process. Therefore, hypothesis 1 cannot berejected since decisionmakersmake use of OSN to support certain phases of DM. It should be noted that somephases of the decision-making processfail to be supported by OSN. In particular, the second steps of the design and implementation phases are found to be insignificant (p-value N0.05).

A possible explanation for this is that the second step of the implementation phase is a physical-based action that cannot be replaced withuse of online social networks. When the decision is made and the human mind is set, the actual process of implementation of the chosen so-lution is done by a human in the offline world. In the example provided below, the basic process of decision-making shows why the implemen-tation and monitoring phases are not supported by OSN.

Intelligence:My2 year old son has had a headache for the last twohours (OSN subject/problem search).Design: Nurofen or Panadol, Kids Cannot take Nurofen (Advicefrom OSN member).Choice: Take Panadol (based on OSN information and providedalternatives).Implementation: Physical action of taking Panadol (actual doing).Monitoring Results: Any symptoms of headache after taking aPanadol? (Physical state)

3.4.2.7. Testing Hypothesis 2 Hypothesis 2 tests the relationship between decision-maker style (rational, dependent, intuitive and spontaneous) andphases of the decision-making process (intelligence, design, choice, implementation and monitoring). Decision makers who tend to follow a partic-ular style can use OSN in a differentmanner that can reflect the support function of OSN for the decision-making process. Decision-making style is animportant indicator of decision-maker behavior that uses OSN in order to solve a problem.

Hypothesis 2: Decision-making (DM) style of OSNusers affects thedecision-making process.Hypothesis 0: Decision-making (DM) style of OSN users does notaffect the decision-making process.

Hypothesis 2 states that two variables, DMprocess and DM style, are dependent or related. This is true if the observed counts for the categories ofthe variables in the sample are different from the expected counts. In this hypothesis, the chi-square test of independencewas implemented to eval-uate group differences between rational, dependent, spontaneous, intuitive and avoidant decision makers and their behavior towards the DM pro-cess. The null hypothesis is true if the observed counts in the sample are similar to the expected counts.

Hypothesis 2 is divided into two sub-hypotheses, 2a and 2b; null hypothesis stays the same for both.

Hypothesis 2a: Rational and dependent decision makers tend tofind support in most (N2) phases of the decision-making process,whereas spontaneous and avoidant tend to find support in fewer(≤2) phases of the decision-making process by use of onlinesocial networks.Hypothesis 2b: Spontaneous/intuitive and avoidant decisionmakerswill use OSN to support the intelligence and choice phase only.Hypothesis 0: Decision makers of any style tend to find similarsupport in phases of the decision-making process through use ofonline social networks. There is no relationship betweendecision-making style and support for phases of the DM processthrough use of OSN

The data for this hypothesis testing are from the responses to Question 37, which identifies the decision-making style of OSN users and Questions23–29 that identify how OSN support phases of the DM process.


In Question 37, the participant could choose only one option from five decision-making styles. In order to perform a two-by-two chi-square test,the decision-making style variables were grouped into two categories: Rational and dependent decision-makers are grouped under statisticalvalue = 1 and intuitive, spontaneous, avoidant decision-makers were grouped under statistical value = 0. The statistical operation in SPSS is:Qs37 = Qs37_Grouped (1 — Dependent & Rational, 0 — Spontaneous, Intuitive & Avoidant).

For Questions 23–29, the five variables that represent phases of the DM process were combined, and the expression used for SPSS analysis is:Qs23–29_Combined = Qs23 + qs24 + qs26 + qs28 + qs29.

Percent
Qs23_Qs24_Qs26_Qs28_Qs29_Combined
Valid 0 8.8 13.3 13.3
1
4.4 6.7 20.0 2 4.4 6.7 26.7 3 18.6 28.0 54.7 4 11.5 17.3 72.0 5 18.6 28.0 100.0 Total 66.4 100.0
Missing
Qs23_Qs24_Qs26_Qs28_Qs29_Combined_Grouped
Valid Passive supporters 17.7 26.7 26.7
Active supporters
48.7 73.3 100.0 Total 66.4 100.0
Missing
Questions 25 and 27 were not included for this equation, as they could cause a doubling up of values. These questions present the samephases of the DM process as Question 24 and Question 28 respectively.

It is important to note that the decision-making process itself might not have a sequence; OSN users can follow their own sequence,but the more phases they support, the higher their overall support value for the decision-making process. Decision makers who gothrough one or two phases of the DM process during the use of OSN are ‘passive’ supporters of the DM phases for problem solving(value for statistical analysis = 0) and users who find support in more than 2 phases of the DM process are ‘active’ supporters(value for statistical analysis = 1). The highest value of supported phases can be 5, and only in the case of the decision maker followingall the phases of the DM process (from intelligence to implementation). The tables below show the statistical frequencies and operationsfor the discussed questions.

Percent
Select the statement that best reflects your decision-making style.
Valid I make decisions in a logical and systematic way 35.4 51.3 51.3
I avoid making decisions until the pressure is on
5.3 7.7 59.0 I rarely make decisions without consulting other people 8.8 12.8 71.8 When I make decisions, I tend to rely on my intuition 14.2 20.5 92.3 I generally make snap/impulsive decisions 5.3 7.7 100.0 Total 69.0 100.0
Missing
Qs37_Grouped
Valid Avoidant, spontaneous & intuitive 24.8 35.9 35.9
Rational & dependent
44.2 64.1 100.0 Total 69.0 100.0
Missing
The probability of the chi-square test statistic (p = 0.426) is greater than the alpha level of significance of 0.05. The null hypothesis that “Thedecision-making (DM) style of OSN users does not affect the decision-making process” is not rejected. The hypothesis that “The decision-makingstyle of OSN users tends to affect the decision-making process” is rejected. We conclude that there is no relationship between DM style and DMprocess.

3.4.2.8. Testing Hypothesis 3 Hypothesis 3 tests OSN participation role with five phases of the decision-making process that a decision maker goesthrough in order to solve a problem (Question 18).

Hypothesis 3: OSNparticipation roles tend to affect theDMprocess.Hypothesis 3a: Seeker and Adviser find support in most (N3) of thephases of the DM process.Hypothesis 3b: Observers find support in b2 phases of the DMprocess.Hypothesis 0: OSNparticipation role does not affect theDMprocess.


The hypothesis states that the two variables (OSN participation role and DMprocess) are dependent or related. This is true if the observed countsfor the categories of the variables in the sample are different from the expected counts. The null hypothesis is that the two variables are independent.

This hypothesis is tested by three statistical tests: Chi-square test, t-test for independent samples and ANOVA; Chi-square is used to identifyp-value of significance between the two variables, t-test for independence shows the mean difference between the two groups of participationroles and phases of DM processes, and ANOVA is used to justify the results for Hypotheses 3a and 3b.

The Chi-square test examines the relationship between the OSN participation role variables (Seeker, Adviser and Observer) and the phases of thedecision-making process (decisionmakers who followmore than 3 phases of the DMprocess are active supporters – statistical value-1, and decisionmakers who follow fewer than 2 phases of the DM process are passive supporters – statistical value-0).

Seeker and Adviser are grouped together because they are identified to be frequent users of OSN,while observers tend to observe OSNwithout anactual contribution. Seeker & Adviser are recorded as ‘Adviser & Seekers’ (statistical value-1) Observers are identified as a separate group (statisticalvalue-0) and recorded as ‘Observers’. Questions 23–29 will follow identical grouping combinations as for Hypothesis 2.

3.4.2.9. Statistical analysis— results The probability of the Chi-square test (p = 0.001) is less than the alpha level of significance of 0.005. The nullhypothesis that differences in ‘OSN participation role’ are independent of differences in ‘phases of DM process’ is rejected. Therefore, theresearch hypothesis that OSN participation role tends to affect the DM process in order to solve a problem is supported by this analysis.

Chi-square test between two variables: participation role and decision-making process.

Qs_18_Recorded

Percent
Valid
Observer 38.9 53.0 53.0 Adviser & Seeker 34.5 47.0 100.0 Total 73.5 100.0
Missing
To test Hypotheses 3a and 3b, we used the Independent Sample Test and ANOVA. First, we discuss the process of Independent t-test andthen ANOVA will complete the testing for Hypothesis 3. The Independent Sample t-test allows the comparison of means between OSN partic-ipation roles, and to identify whether there is a significant difference between two groups of participation roles in relation to support of phasesof the DMprocess. The Independent Sample t-test is used to compare one categorical variable with two levels and one continuous variable. OSN par-ticipation role is a categorical variable with two levels— Advisers & Seekers and Observers (Qs18 _Recorded). The continuous variable is five phasesof the DM process (Qs23_Qs24_Qs26_Qs28_Qs29_Combined). Two groups of participation role were tested on a scale of 5 phases of the DM process.

3.4.2.10. Statistical output

Group statistics

Qs_18_Recorded
N Mean Std. deviation Std. error mean

Observer
40 2.48 1.724 .273 Adviser & Seeker 35 3.89 1.301 .220
Independent samples test

Levene's testfor equalityof variances

t-Test for equality of means

F
Sig. t df Sig.2-tailed
Meandifference

Std. errordifference

95% confidenceinterval of thedifference

Lower
Upper

Equal variances assumed
5.079 .027 −3.95 73 .000 −1.411 .357 −2.122 − .700 Equal variances not assumed −4.02 71 .000 −1.411 .350 −2.109 − .712
Significance level = 0.00, p-value b 0.05 reject the null hypothesis.


Advisers & Seekers (mean=3.89, SD=1.301) scored higher (t [73]=−4.207, p b 0.01) than Observers (mean=2.48, SD=1.724) on themea-sure of support in phases of the decision-making process.Mean score for Advisers & Seekers is higher than that for Observers, indicating that Advisersand Seekers support more phases of the decision-making process than Observers. The analysis supported our Hypotheses 3a and 3b.

ANOVA test is used to compare scores on a continuous variable that is treated as parametric data (Participation Style) by the level of categoricaldata (phases of the DM process). The first variable is Participation Style. This is a categorical variable that indicates whether people are Observers,Seekers or Advisers. The second variable comprises scores from phases of the decision-making process. This inventory contains a measure of howmany phases of the decision-making process are used by different styles of decision makers. Therefore the analysis of variances used here examineswhether the three categories of participation role differ significantly from one another in their level of support of phases of the DM process.

This test uses already organized variables that were used in Hypothesis 2, the phases of the decision-making process (Qs23_Qs24_Qs26_Qs28_Qs29_Combined) and original variables from Question 18 (Participation Style).

Descriptive


N
Mean Std.deviation
Std.error

95% confidenceinterval for mean

Minimum
Maximum
Lower bound
Upper bound
Adviser (I like to give advice on online social networks)
13 3.85 .899 .249 3.30 4.39 3 5 Seeker (I like to seek information from online social network) 22 3.91 1.509 .322 3.24 4.58 0 5 Observer (I like to observe online social networks,but I do)
40
2.48 1.724 .273 1.92 3.03 0 5
Total
75 3.13 1.687 .195 2.75 3.52 0 5
Test of homogeneity of variances


Levene statistic
Df1 df2 Sig. 3.286 2 72 .043
ANOVA


Sum of squares
df Mean square F Sig.
Between groups
37.181 2 18.591 7.715 .001 Within groups 173.485 72 2.410 Total 210.667 74
Multiple comparisons


Dunnett T3

(I) How would you primarilydescribe yourself in terms of useof online social networks?

(J) How would you primarily describeyourself in terms of use of onlinesocial networks?

Meandifference(I-J) 7.715

Std.error

Sig.
95%confidenceinterval
Lowerbound

Upperbound

Adviser (I like to give advice ononline social networks)

Seeker (I like to seek information from onlinesocial networks)

− .063
.407 .998 −1.08 .96
Observer (I like to observe online socialnetworks, but I don't ask questions or posts advice)

1.371*
.369 .002 .45 2.29
Seeker (I like to seek informationfrom online social networks)

.063 .407 .998 − .96 1.08 Observer (I like to observe online socialnetworks, but I don't ask questions or posts advice)
1.434*
.422 .004 .39 2.48
Observer (I like to observe onlinesocial networks, but I don'task questions or posts advice)

−1.371* .369 .002 −2.29 − .45 Seeker (I like to seek information from online social networks) −1.434* .422 .004 −2.48 − .39
*The mean difference is significant at the 0.05 level.

The average mean score for respondents who are Advisers is 3.85 (with a standard deviation of 0.899). The average mean score for respondentswho display Seeker participation role is 3.91 (with a standard deviation of 1.509), and the averagemean score for respondents who display Observerparticipation role is 2.48 (with a standard deviation of 1.724). Here we note that there is little difference in mean score between Advisers and Seeker


and the respondents score higher than the Observers. The F-value statistic is 7.715. The significance level is p = 0.001. Since this is smaller than .01,we conclude that there is a significant difference for mean scores of phases of the decision-making process.

3.4.2.11. Analysis between subjects The Dunnett T3 tests for significance differences between mean scores of each of the possible pairs of categoricalvariables (e.g., Seeker and Observer is a pairing, as are Adviser and Observer, and Adviser and Seeker) to provide an overall picture of where thedifferences lie. In each row of the multiple comparison table, the possible pairings are presented with each level of the categorical variable.

In the first row (Adviser): No significant difference between Adviser and Seeker respondents (p = 0.998, p N 0.05) and a significant differencebetween Adviser and Observer respondents (p = 0.002, p b 0.05).

In the second row (Seeker): No significant difference between Seeker and Adviser respondents (p = 0.998, p N 0.05) (this information repeatsinformation gained from the first row), and a significant difference between Seeker and Observer (p = 0.004, p b 0.05).

In the third row (Observer): A significant difference between Observer and Adviser respondents (p=0.002, p b 0.05), and a significant differencebetween Observer and Seeker (p = 0.004, p b 0.05) (this information repeats information from the first two rows).

Therefore, using this information,we can conclude that Observer participation role respondents score significantly lower than Adviser and Seekerrespondents on themeasure of support for phases of the decision-making process. Phases of DM scoreswere calculated for theAdviser (mean=3.85,SD=0.899), Seeker (mean=3.91, SD=1.509), and the Observer group (mean=2.48, SD=1.687). The visual difference is presented on the graphthat records the mean measure for each participation role.

An analysis of variances indicated a significant difference between the groups on themeasure of support/influence of the decision-making processF (2, 72)= 7.715, p b 0.01. A Dunnett T3 revealed that Observers scored significantly less than both Advisers (p b 0.05) and Seekers (p b 0.05) on themeasure of support phases of the decision-making process. However, there were no significant differences between the Adviser and Seeker respon-dents (p N 0.05).

Therefore this analysis supports the Hypotheses 3a and 3b and the null hypothesis is rejected.

1. Participation role tends to affect the decision-making process. (Hypothesis 3)

2. Advisers and Seekers use OSN in more phases of the DM process in comparison to Observers. (Hypothesis 3a)3. Observers tend to skip most of the phases of the DM process. (Hypothesis 3b)

Overall, Advisers and Seekers tend to support approximately four phases of the decision-making process, whereas Observers use OSN to supportonly two phases.

4. Discussion and conclusion

The primary focus of this study is to understand howOSN is used as asupport tool for DM andwhich of its phases aremost used by OSN usersfor DM. Our survey results show that most OSN users consider them-selves as following the rational DMstyle. Themain thoughts, discussionsand considerations that took place in this study were bounded aroundthe subject of the DM process and how it can be supported by the useof OSN. We now highlight the contribution of this research to thetheoretical and practical world of DM and OSN.

OSN support decision makers in the most well accepted phases ofthe DM process. Our results provide evidence that OSN providesupport for intelligence (information search), design (model of alter-natives and options), and choice phases. This finding is aligned withthe original Simon [26] model of the DM process. Table 9 shows thesupport for DM in OSN based on our online survey in response tothe research question: “What are the phases of DM process that aresupported by use of OSN?”

An explanation on the observation of partial support for phasesuch as implementation can be derived based on the theory of thecognitive process of the decision maker. In the real-world, decisionsare made unconsciously in the mind of the decision maker. Fromthe perspective of the decision maker there is no intention to admitthe decision implementation, for example, by the textual impressionsin OSN. Survey results showed partial statistical support that the imple-mentation phase is supported by use of OSN.While the actual ‘objective’of implementation was not supported, the understanding and identifica-tion of resources that are required for implementation of the decisionshowed statistical significance. From the discussion above, it is apparentthat OSN are used as a support tool, which helps find relevant informa-tion, understand alternatives, options, choices and consequences;observe and share the DM process experience, identify necessary re-sources for implementation and evaluation of outcomes from takendecisions.

Our analysis has not only provided evidence for OSN support in theDM process, but has also generated insights on how different participa-tion styles of stakeholders influence the support of DM phases throughuse of OSN. The mean score of seekers and advisers on phases of DMare much higher than that of observers. Advisers (mean score 3.85)and seekers (mean score 3.91) find support in approximately four outof five phases of the DM process through use of OSN. Observers useonly two out of five phases of the DM process (mean score 2.48). Froma general understanding about types of DM style that have been provid-ed in the literature, our results do not provide conclusive evidence thatDM style has an influence on all phases of the decision-making process.However, the constantly changing world environment and speed oftechnology innovation have made access to information uncomplicatedand undemanding; therefore the decision maker is exposed to differentsources of information without an understanding of their effects. Re-gardless of their spontaneous, intuitive or avoidant style, the informa-tion database is readily available to the users, whereby through aneasy search of the Internet they are able tomake a decisionwhile payinglittle attention to the phase of the DM process. The action of followingthe process might take as little as a minute to even years. OSN provideus the opportunity to find answers, interpretations, models and choices.The percentage allocation from the survey showed that dependent andrational decision makers are more willing to admit the fact that theyuse OSN in most of the phases of the DM process. As for avoidant, spon-taneous and intuitive decision makers, when OSN users face a problem,they behave differently without following any particular sequence ofDM phases.

Little is known on howOSN should be designed in order to influenceor support the decision-making process. Several research areas provideinsights towards accomplishing a successful design that can generaterevenue. However, the absence of theoretical guidance on how to con-tract a design for this specific purpose has resulted in the developmentof many features and tools that can irritate online users [11]. This isespecially salient in the case of DM where people are searching for

Table 7Summary of the research questions, hypotheses, data analysis and variables.

Research questions Objectives Hypothesis Data analysis Variables

What are the phases that are mostsupported by OSN users?

To compare each of the five phasesof DM process and its relationshipwith the use of OSN

H.1 Online social networks support thedecision-making processH.0 Online social networks do not support thedecision-making process

Chi-square test IntelligenceDesignChoiceImplementa-tionMonitoring—

Qs23–29How can different decision-making styleinfluence the support function of OSNfor DM process?

To explore how differentdecision-maker styles influence thedifferent phases of thedecision-making process.

H.2 Decision-making Style affects the DM processH.0 Decision-making style does not affect the DM process

Chi-square test IntuitiveDependentSpontaneousAvoidantRational—

Qs17Are there any differences in OSNparticipation roles towards the use ofOSN as the support mechanism for theDM process?

Compare OSN participation rolewith five phases of the DM processthat a decision-maker goes throughin order to solve a problem.

H.3 Participation role affects the DM processH.0 Participation role does not affect the DM process

Chi-square test,independent samplet-Test, ANOVA

ObserverSeekerAdviser—

Qs 18


concrete answers, where they can be easily irritated by useless websitefeatures.

Rather than creating a virtual space without understanding or copy-ing the design from successful sites, the hosted organization shouldappraise the decision-maker experience that supports the decision-making process through OSN and mirrors this requirements throughthe design of the website [11]. Yaping et al. [30] argue that with the de-velopment of OSN infrastructure, some website features become morecomplex because the websites are aiming for complete advantage andways to differentiate themselves from others. But complex website fea-tures do not always attract more users, and especially will not keepthem for a long time; the goal of an OSN site is to ensure that usersspendmore time browsing around the topics, asking questions and par-ticipating in the discussions [30]. OSN face a constant battle of attractingnew visitors to their websites andmaintaining or increasing the interestlevel of its members [5]. In the case of OSN for DM, it is vital to attractexperts who can provide quality and peer-reviewed information forDM.

Hausman and Siekpe [11] suggest that successful websites shouldconsider the use of 3D graphics, the use of humor and innovative‘techno-savvy’ tools. In the case of OSN for DM, the priority is to provideuseful and instructive information by enabling the decision-maker touse it for all phases of the DM process. In addition to the identified fea-tures in and responses on relevance of these features from our onlinesurvey study, results show that decisionmakers are especially attractedto the search engines that help with information retrieval. Search en-gines are found to be one of the essential decision-support tools thatcan accommodate the decision maker's query.

Although the factor of irritation has beenmentioned before, some ofthe OSN participants demand the innovativeness that eventually helpswebsite providers to keep their members. For example, in health OSN(HOSN), the body mass indicator (BMI) or symptom checker has its

Table 8Outcome for Hypothesis 1.

Decision-making phase

Hypothesis 1a Intelligence phase — understanding of probHypothesis 1b Design phase — development of alternativesHypothesis1b-2 Design phase — determination of consequenceHypothesis 1c Choice phase — select an option/alternativeHypothesis 1d Implementation phase — identify resourcesHypothesis 1d-2 Implementation phase-implement an option —

Hypothesis 1e Monitoring phase — monitor implemented de

niche among health decision-makers. Most OSN users are familiarwith the generic website functions for DM; the user behavioral inten-tion to share or contribute to the networks is based on this environmen-tal aspects. Results from our study indicate that users appreciate theopportunity to create personal profiles and the possibility of sharingphotos or informational links.

We now discuss some of the challenges and limitations that weencountered during this study. Use of online survey interface for dis-tributing a questionnaire offers many advantages as well as somedisadvantages that we had to face. The main limitations that havebeen exposed in this study are associated with sampling, responserate, and demographics.

First is the sampling issue, with the main goal to target the users ofOSN for DM. The responses from the online survey represented onlythe general public who use OSN in their everyday lives. The questionthat can be asked is whether these OSN participants are aware of theirDM process by use of OSN and how they are different from experiencedusers of OSN for DM. The respondents were asked if they use OSN forDM, but there is noway to ensure that the participants actually supporttheir DM by use of OSN or that they follow a rational decision makingprocess.

Second, there were two related problems, namely insufficient re-sponses and a reasonably high drop-out rate for different questions.These two problems led to some missing data and thus reduced thequalified responses for analysis. This can be explained by the question-naire taking approximately 30 min to complete, and the respondentsmay not have felt encouraged to complete it. There is a possibility thatproviding a reward for participants to participate could boost the re-sponse rate and prevent users from quitting the survey before its com-pletion. The chance to win a prize or gift certificate, as reward forparticipation, however, could result in multiple responses by the sameindividuals and misrepresentation of background information.

Outcome

lem — Qs23 Reject the null hypothesis— Qs24 Reject the null hypothesiss — Qs25 Fail to reject the null hypothesis/course of action — Qs26 Reject the null hypothesis— Qs27 Reject the null hypothesisQs28 Fail to reject the null hypothesis

cision — Qs29 Fail to reject the null hypothesis

Table 9Support of DM process in OSN.

Decision making phases OSN (survey)

Intelligence SupportedDesign SupportedChoice SupportedImplementation Identification of resources — supported

Implementation of an option/alternative/course of action — not supported

Monitoring Not supported


Another drawback of this online survey is the participant age; mostof the respondents were between 18 and 35 years old. In this case, theexplanation can be that professional people are busier and less likelyto participate in a survey.

Appendix A. GDMS instrument

Decision-makingstyle

Item

Rational
1. I double-check my information sources to be sure I havethe right facts before making decisions
2. I make decisions in a logical and systematic way3. My decision-making requires careful thought4. When making a decision, I consider various options in

terms of a specified goal5. I usually have a rational basis for making decisions

Intuitive
1. When I make decisions, I tend to rely on my intuition2. When I make a decision, it is more important for me to
feel the decision is right than to have a rational reasonfor it

3. When making a decision, I trust my inner feelings andreactions

4. When making decisions, I rely upon my instincts5. I generally make decisions that feel right to me

Dependent
1. I rarely make important decisions without consultingother people
2. I use the advice of other people in making importantdecisions

3. I like to have someone steer me in the right directionwhen I am faced with important decisions

4. I often need the assistance of other people when makingimportant decisions

5. If I have the support of others, it is easier for me to makeimportant decisions

Avoidant
1. I put off making decisions because thinking about themmakes me uneasy
2. I avoid making important decisions until the pressure is on3. I postpone decision-making whenever possible4. I often put off making important decisions5. I generally make important decisions at the last minute

Spontaneous
1. I make quick decisions2. I often make decisions on the spur of the moment3. I often make impulsive decisions4. I generally make snap decisions5. When making decisions I do what feels natural at the
moment

GDMS instrument (Adapted from: Scott and Bruce [25, p. 825–826])).

Appendix B. Supplementary data

Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.dss.2014.11.011.

References

[1] D.M. Boyd, N.B. Ellison, Social network sites: definition, history, and scholarship,Journal of Computer-Mediated Communication 13 (1) (2008) 210–230.

[2] T.J. Chermack, K. Nimon, The effects of scenario planning on participant decision-making style, Human Resource Development Quarterly 19 (4) (2008) 351–372.

[3] M.D. Cohen, J.G. March, J.P. Olsen, A garbage can model of organizational choice,Administrative Science Quarterly 17 (1) (1972) 1–25.

[4] E. Deutskens, K. de Ruyter, M. Wetzels, P. Oosterveld, Response rate and responsequality of internet-based surveys: an experimental study, Marketing Letters 15 (1)(2004) 21–36.

[5] V. DiMauro, D. Bulmer, Social media's role in decision making by businessprofessionalsRetrieved from http://www.sdn.sap.com/irj/scn/go/portal/prtroot/docs/library/uuid/e0c2a4fc-0ceb-2c10-0785-f40bb184cd2f?QuickLink=index&overridelayout=true 2010.

[6] M.J. Driver, K.R. Brousseau, P.L. Hunsaker, The Dynamic DecisionMaker: Five DecisionStyles for Executive and Business Success, Jossey-Bass, San Francisco, Calif, 1993.

[7] K.M. Galotti, Decision structuring in important real-life choices, PsychologicalScience 18 (4) (2007) 320–325.

[8] K.M. Galotti, E. Ciner, H.E. Altenbaumer, H.J. Geerts, A. Rupp, J. Woulfe, Decision-making styles in a real-life decision: choosing a college major, Personality and Indi-vidual Differences 41 (4) (2006) 629–639.

[9] J.F. Hair, R.E. Anderson, R.L. Tatham, W.C. Black, Multivariate Data Analysis withReadings, 4th ed. Prentice Hall, Upper Saddle River, NJ, 1998.

[10] S. Harridge-March, S. Quinton, Virtual snakes and ladders: social networks and therelationship marketing loyalty ladder, The Marketing Review 9 (2) (2009) 171–181.

[11] A.V. Hausman, J.S. Siekpe, The effect of web interface features on consumer onlinepurchase intentions, Journal of Business Research 62 (1) (2009) 5–13.

[12] J.C. Henderson, P.C. Nutt, The influence of decision style on decision making behav-ior, Management Science 26 (4) (1980) 371–386.

[13] G.P. Huber, The nature of organizational decision making and the design of decisionsupport systems, MIS Quarterly 5 (2) (1981) 1–10.

[14] G.P. Huber, R.R. McDaniel, The decision-making paradigm of organizational design,Management Science 32 (5) (1986) 572–589.

[15] R. Kumar, J. Novak, A. Tomkins, Structure and Evolution of Online Social NetworksLink Mining: Models, Algorithms, and Applications, in: P.S.S. Yu, J. Han, C.Faloutsos (Eds.), Springer, New York, 2010.

[16] J.Malczewski, Spatial decision support systems, NCGIA Core CurriculumGIScience, 1997.[17] J. Maltby, L. Day, Early Success in Statistics, Prentice Hall, 2001.[18] J.G. March, Bounded rationality, ambiguity, and the engineering of choice, The Bell

Journal of Economics 9 (2) (1978) 587–608.[19] H. Mintzberg, D. Raisinghani, A. Théorêt, The structure of “unstructured” decision

processes, Administrative Science Quarterly 21 (2) (1976) 246–275.[20] C.M. Musil, C.B. Warner, P.K. Yobas, S.L. Jones, A comparison of imputation techniques

for handlingmissing data,Western Journal ofNursing Research24 (7) (2002) 815–829.[21] J. Preece, B. Nonnecke, D. Andrews, The topfive reasons for lurking: improving commu-

nity experiences for everyone, Computers in Human Behavior 20 (2) (2004) 201–223.[22] D. Rodan, L. Uridge, L. Green, Using nicknames, pseudonyms and avatars on

HeartNET: a snapshot of an online health support community, Paper presented atthe ANZCA Conference, Canberra, Australia, 2010.

[23] A.J. Rowe, J.D. Boulgarides, Managerial Decision Making: A Guide to SuccessfulBusiness Decisions, McMillan Publishing Co., New York, 1992.

[24] A.J. Rowe, J.D. Boulgarides, Managerial Decision Making, Prentice Hall, Upper SaddleRiver, NJ, 1994.

[25] S.G. Scott, R.A. Bruce, Decision-making style: the development and assessment of anewmeasure, Educational and Psychological Measurement 55 (5) (1995) 818–831.

[26] H.A. Simon, Theories of decision-making in economics and behavioral science, TheAmerican Economic Review 49 (3) (1959) 253–283.

[27] P. Thunholm, Decision-making style: habit, style or both? Personality and IndividualDifferences 36 (4) (2004) 931–944.

[28] T. Verbraken, F. Goethals, W. Verbeke, B. Baesens, Predicting online channel accep-tance with social network data, Decision Support Systems 3 (2014) 104–114.

[29] S. Wasserman, K. Faust, Social Network Analysis: Methods and Applications,Cambridge University Press, New York, 1994.

[30] C. Yaping, H. Dan, Y. Jun, The impact of B2C website design features on consumers'purchase intention, Paper Presented at the Proceedings of the 2009 InternationalConference on Networks Security,Wireless Communications and Trusted Computing,IEEE Computer Society, Wuhan, China, 2009.

[31] Y. Yu,W. Duan, Q. Cao, The impact of social and conventionalmedia on firm equity value:a sentiment analysis approach, Decision Support Systems 55 (4) (2013) 919–926.

[32] B. Zhou, Comparison of Decision Styles of Business Managers in Two InternationalCompanies within a Rational Decision-Making Context, Cape Peninsula Universityof Technology, 2006.

Valeria Sadovykh is a PhD student in the Department of Information Systems and Oper-ations Management, Business School, The University of Auckland. Her research interestsinclude use of Online Social Networks as a support tool for Decision Making Process, On-line Decision Support Systems, Netnography and Content Analysis of Online Social Media.

David Sundaram has a varied academic (Electronics & Communications, Industrial Engi-neering, and Information Systems) as well as work (systems analysis and design, consulting,teaching, and research) background. His primary research interests are in the design and im-plementation of adaptive systems that are flexible and evolvable and support informational,decisional, knowledge and collaboration needs of organisations and individuals. Related re-search interests that support the above include (1) adaptive organisations and supportingthem through the interweaving of deliberate and emergent strategies (2) process, informa-tion, decision, and visualisation modelling (3) sustainability modelling and reporting (4)ubiquitous information systems (5) enterprise systems and their implementation.

SelwynPiramuthu is a Professor in the Information Systems andOperationsManagementDepartment at the University of Florida. His research interests include online social net-works, RFID systems, pattern recognition and its application in supply chainmanagement,computer-aided manufacturing, and financial credit-risk analysis.



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