A K M Najmul Islam
email: najmul.islam@utu.fi

Matti Mäntymäki
email: matti.mantymaki@utu.fi

Aaron W Baur
email: abaur@escpeurope.eu

Markus Bick
email: mbick@escpeurope.eu

Investigating Dual Effects of Social Networking Sites

Keywords: Social networking sites, Addiction, SNS Addiction, Subjective Vitality, Voyeurism, Exhibitionism, Enjoyment

This paper investigates the dual effects (i.e. benefits and dangers) of a social networking site (SNS). Our dependent variables are subjective vitality and addiction. We employ self-determination theory (SDT), specifically the concepts of autonomous and controlled motivation and hypothesize that social enhancement and enjoyment represent autonomous motivation and contributes to subjective vitality while voyeurism and exhibitionism represent controlled motivation and contribute to SNS addiction. We test our hypotheses with data from 289 student Facebook users using Partial Least Squares (PLS). The results suggest that both social enhancement and enjoyment predict subjective vitality. In contrast, exhibitionism is the sole predictor of SNS addiction while voyeurism has no effect.

Introduction

Social networking sites (SNSs) have become a prominent venue for social interaction.

Nearly 80% of the Internet users use social networking sites such as Facebook, the Russian vKontakte or the Chinese equivalent Renren. The most popular SNS, Facebook, has more than 2 billion users globally. SNSs offer their users various ways for communication, social interaction and selfpresentation [START_REF] Mäntymäki | Voyeurism and Exhibitionism as gratifications from Prosuming Social Networking sites[END_REF]2016). Good social relationships and interaction with other people support one's psychological well-being. Considering the number of SNS users globally, it is particularly interesting to investigate to what extent SNS use supports user's psychological well-being (Islam et al., In Press). For example, receiving positive feedbacks on posted content may increase an individual's self-esteem and well-being [START_REF] Valkenburg | Friend networking sites and their relationship to adolescents' well-being and social self-esteem[END_REF].

The IS research is traditionally emphasized performance improvement as the main benefit of IT use [START_REF] Davis | Perceived usefulness, perceived ease of use, and user acceptance of information technology[END_REF][START_REF] Delone | The DeLone and McLean model of information systems success: A ten year update[END_REF][START_REF] Venkatesh | Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology[END_REF]. The performance improvement has traditionally been conceptualized as (work) efficiency gains. In addition, due to the proliferation of hedonic IS and consumer use of IT the hedonic outcomes have gained increasing interest [START_REF] Turel | The benefits and dangers of enjoyment with social networking websites[END_REF]; [START_REF] Van Der Heijden | Acceptance of hedonic Information Systems[END_REF][START_REF] Venkatesh | Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology[END_REF].

However, individual's psychological well-being as an outcome of IT use, particularly in the SNS context, has thus far received only limited attention in prior IS research.

To fill this research gap, we explore what increases individuals' subjective vitality (i.e., an aspect of well-being) in an SNS use context. In practice, we argue that social enhancement and enjoyment are the two drivers of subjective vitality.

At the same time, there is an increasing awareness that the accelerated sharing caused by the pervasive use of social networking sites is not entirely positive but is also related to adverse phenomena such as voyeurism, exhibitionism [START_REF] Munar | Digital exhibitionism: The age of exposure[END_REF], and addiction [START_REF] Andreassen | Development of a Facebook addiction scale[END_REF]. Prior IS research suggested that hedonic systems like SNSs, games, etc. would be more addictive than utilitarian system [START_REF] Turel | The benefits and dangers of enjoyment with social networking websites[END_REF]. In general, technology producing thrills or filling socio-psychological voids in users' lives are more addictive than others [START_REF] Young | Internet addiction over the decade: A personal look back[END_REF]. By participating in an SNS, an individual fills his/her social voids and experiences thrills, and thus he/she becomes addicted to the SNS [START_REF] Turel | The benefits and dangers of enjoyment with social networking websites[END_REF].

Turel & Serenko (2012) found that majority of prior technology addiction research investigated the consequences of technology addiction. Technology addiction can affect adversely on individual, organizational, and societal levels. Possible technology addiction outcomes include productivity losses (Yellowless & [START_REF] Yellowlees | Problematic Internet Use or Internet Addiction?[END_REF], health problems [START_REF] Block | Issues for DSM-V: Internet Addiction[END_REF], worsening academic performance [START_REF] Kuss | Addiction to social networks on the Internet: A literature review of empirical research[END_REF], and additional treatment costs [START_REF] Xu | Online game addiction among adolescents: Motivation and prevention factors[END_REF]. There is a lack of research on understanding antecedents/determinants of technology addiction [START_REF] Turel | The benefits and dangers of enjoyment with social networking websites[END_REF]. To fill this second research gap, we explore factors that might contribute to SNS addiction. As a result, we place voyeurism and exhibitionism motivations as factors driving SNS addiction.

To recap, SNS use can bring both benefits and dangers to the users. Therefore, this paper examines the dual effect of SNS use. In doing so, we extend the scope of IS research from examining only either benefits [START_REF] Delone | The DeLone and McLean model of information systems success: A ten year update[END_REF][START_REF] Venkatesh | Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology[END_REF] or dangers [START_REF] Turel | Integrating Technology Addiction and Use: An Empirical Investigation of Online Auction Users[END_REF][START_REF] Xu | Online game addiction among adolescents: Motivation and prevention factors[END_REF]) towards a parallel investigation of both positive and negative side of IT use (Mäntymäki & Islam, 2016).

The remainder of this paper is organized as follows. In section 2 we present the theoretical foundations. Section 3 is dedicated to hypotheses development and section 4 presents the research method. Section 5 presents data analysis results and discussions. Finally, section 6 discusses limitations and future research.

2

Theoretical Foundations

Subjective Vitality

The concept of vitality has been theorized as an important idea within diverse theoretical frameworks [START_REF] Ryan | From ego depletion to vitality: Theory and energy findings concerning the facilitation of energy available to the shelf[END_REF]. According to the Oxford English Dictionary, an individual with vitality has vigour and liveliness, a general energy for life. When vital, individuals experience a sense of enthusiasm, aliveness, and energy available to the self (Ryan & Fredrick, 1997).

As one cannot, in principle, directly measure the energy available to the self, Ryan & Fredrick (1997) suggested exploring vitality as a subjective variable. In relation to this, Akin (2012) defined subjective vitality as the subjective experience of being full of energy and alive. Furthermore, subjective vitality is considered as an aspect of eudaimonic well-being, as being vital and energetic is part of what it means to be fully functioning and psychologically well [START_REF] Ryan | To be happy or to be self-fulfilled: A review of research on hedonic and eudaimonic well-being[END_REF].

Prior research on psychology and medicine has empirically found that subjective vitality is robustly associated with both behavioural and health outcomes (e.g., Pennix et al., 2000;[START_REF] Ryan | From ego depletion to vitality: Theory and energy findings concerning the facilitation of energy available to the shelf[END_REF][START_REF] Thayer | The Origin of Everyday Moods[END_REF]. This assertion has been supported by neuroscientific research that has shown that when experiencing vitality, specific configurations in the brain are activated [START_REF] Barrett | Mechanisms of action underlying the effect of repetitive transcranial magnetic stimulation on mood: Behavioral and brain imaging studies[END_REF]. Consequently, subjective vitality has been linked to other positively toned and energized states such as feelings of vigour [START_REF] Mcnair | Profile of Mood States[END_REF], calm energy [START_REF] Thayer | The Origin of Everyday Moods[END_REF], and activated positive affect [START_REF] Watson | Toward a consensual structure of mood[END_REF]. Vital individuals have been found more active and productive and possess better abilities to cope with stress and challenges [START_REF] Ryan | From ego depletion to vitality: Theory and energy findings concerning the facilitation of energy available to the shelf[END_REF]. In addition, vital individuals are less vulnerable to illness, have better mental health as well as are more resilient to physical and viral stressors (Pennix et al., 2000;[START_REF] Ryan | From ego depletion to vitality: Theory and energy findings concerning the facilitation of energy available to the shelf[END_REF].

Ryan & Deci (2008) argued subjective vitality as a complex and dynamic outcome that is influenced by social and psychological factors. They pointed that social events could leave even a well-nourished person feeling excited and energized, or alternatively stressed, angelic, and drained. As SNSs have become an integral part of people's social life, it is important for IS researchers to investigate what factors influence individual's subjective vitality in the SNS use context.

Prior psychology literature conceptualized subjective vitality in two ways: individual difference level [START_REF] Ryan | On energy, personality, and health: Subjective vitality as a dynamic reflection of well-being[END_REF]) and state level [START_REF] Nix | Revitalization through self-regulation: The effects of auton omous and controlled motivation on happiness and vitality[END_REF]. In this paper, we have adopted the state level conceptualization of subjective vitality (i.e., the energy felt during SNS use). Consequently, we define subjective vitality as the subjective experience of being full of energy and alive during SNS use.

Although the concept of subjective vitality has never been explored in IS research, we argue that it can be linked to several IS constructs. We suggest two research directions for IS researchers related to subjective vitality. First, IS researchers may explore the outcomes of subjective vitality. For example, subjective vitality may improve individuals' productivity in an organization, and ability to innovate with IT. Second, examining the antecedents of subjective vitality such as design features of the IT artifact or user motivation would be meaningful for IS researchers.

Addiction

The concept of addiction is frequently used in clinical and behavioural science [START_REF] Marlatt | Addictive behaviors: Etiology and treatment[END_REF]. Addiction is defined as a repetitive habit pattern that increases the risk of disease and/or associated personal and social problems, often experienced subjectively as "loss of control" [that] continues despite volitional attempts to abstain or moderate use (Marlatt et al., 1988, p. 224).

Addiction has six symptoms as suggested by prior literature [START_REF] Brown | Some contributions of the study of gambling to the study of other addictions[END_REF][START_REF] Griffiths | A "components" model of addiction within a bio-psychosocial framework[END_REF][START_REF] Andreassen | Development of a Facebook addiction scale[END_REF]. These are: (1) Salience: meaning that the activity dominates thinking and behaviour; (2) Mood modification: meaning that the activity modifies/improves mood; (3) Tolerance: meaning that an increasing amount of activity is required for the purpose of remaining satisfied; (4) Withdrawal: meaning that when the activity is discontinued or suddenly reduced, the individual experiences panic, anxiety, agitation, or other unpleasant feelings; [START_REF] Armstrong | Estimating nonresponse bias in mail surveys[END_REF] Conflict: meaning that the activity causes conflicts in relationships, in work/studies, and other activities; [START_REF] Bagdasarov | I am what I watch: Voyeurism, sensation seeking, and television viewing patterns[END_REF] Relapse: meaning that the tendency to revert to earlier patterns of the activity after abstinence or control.

Since the early 1990s, psychology researchers have been investigating the phenomenon of technology dependencies or technology addictions [START_REF] Kubey | Television addiction is no mere metaphor[END_REF]. Technology addictions are a type of behavioural addiction that does not involve any external chemical substance [START_REF] Marks | Behavioural (non-chemical) addictions[END_REF]. These addictions mainly occur when human interacts with machines [START_REF] Widyanto | Internet addiction: A critical review[END_REF]. They can be classified either as passive (e.g., radios, televisions, etc.) or active (e.g., computer games, Internet, online forums, SNSs, etc.), and include inducing and reinforcing features which may contribute to promote addictive tendencies [START_REF] Griffiths | Technological addictions[END_REF]. This implies that Internet addiction or problematic use of the Internet can be viewed as a type of active technology addictions. Internet addictions like other behavioural addictions are often manifested through the six symptoms (salience, mood modification, tolerance, withdrawal, conflict, and relapse) that have been described above.

In psychology and clinical psychology [START_REF] Andreassen | Development of a Facebook addiction scale[END_REF]Cheung et al., 2013), SNS addiction falls under the broad category of problematic use of the Internet. Diverse terminologies have been used in prior literature in order to refer problematic use of the Internet such as "Internet Addiction Disorder", "Pathological Internet Use", "Problematic Internet Use", "Excessive Internet Use", "Compulsive Internet Use", Cyberspace Addiction, Online Addiction, Net Addiction, Internet Addicted Disorder, and High Internet Dependency [START_REF] Widyanto | Internet addiction: A critical review[END_REF][START_REF] Douglas | Internet addiction: Meta-synthesis of qualitative research for the decade 19996-2006[END_REF], Cheung et al., 2013). Altogether, as pointed out by [START_REF] Turel | Integrating Technology Addiction and Use: An Empirical Investigation of Online Auction Users[END_REF], the issue of problematic use of IT is relatively new in the IS literature, and scientific understanding of this issue is still evolving. [START_REF] Young | Internet addiction: The emergence of a new clinical disorder[END_REF] conducted the first ground-breaking research study on the problematic use of Internet. Following this, several researchers defined Internet addiction. For example, Mitchell (2000, p. 632) defines Internet Addiction Disorder as "the compulsive overuse of the Internet and the irritable or moody behaviour when deprived of it". [START_REF] Rice | Online addiction[END_REF] defined Internet Addiction as a proclivity toward compulsive use of the Internet that interferes with one's ability to lead a normal life. [START_REF] Shapira | Problematic Internet use: Proposed classification and diagnostic criteria[END_REF] conceptualized problematic Internet use as an impulse control disorder in which an individual experiences rising tension or arousal before Internet use and a sense of relief or pleasure after completion of the behaviour. Internet addiction is characterized by one's inability to control his/her dependence on the Internet, leading to feelings of distress and functional impairments of daily activities [START_REF] Shapira | Psychiatric features of individuals with problematic internet use[END_REF]. [START_REF] Beard | Internet addiction: A review of current assessment techniques and potential assessment questions[END_REF] defined it as an individual's psychological state, which includes both mental and emotional states, as well as their scholastic, occupational and social interactions, is impaired by the overuse of the medium.

To sum up, all these definitions capture one or more symptoms of behavioural addictions. This accords with the suggestions to conceptualize addiction using six core symptoms of behavioural addictions (e.g., [START_REF] Charlton | Distinguishing addiction and high engagement in the context of online game playing[END_REF][START_REF] Davis | Validation for a new scale for measuring problematic internet use: Implications for pre-employment screening[END_REF][START_REF] Young | Internet addiction: The emergence of a new clinical disorder[END_REF]. Following this, we conceptualize SNS addiction as the degree of a user's psychological dependency on the SNS, and the magnitude of the associated behavioural addiction symptoms.

Motivations

Social enhancement

Social enhancement is the value that an individual derives from gaining acceptance and approval from other SNS members, and the enhancement of one's social status within his/her community [START_REF] Dholakia | A social influence model of consumer participation in network-and small-group-based virtual communities[END_REF]. Building one's accurate perceptions of the self is important for his future mental health [START_REF] Taylor | Illusion and well-being: A social psychological perspective on mental health[END_REF]. Individuals generally want to see themselves and be seen by others in a positive light, and thus they employ different strategies to enhance their social image [START_REF] Mccaslin | Self-enhancement and theory-based correction processes[END_REF]. Recognition from others has been found as one of the driving forces behind joining virtual communities [START_REF] Hars | Working for free? Motivations for participating in open-source projects[END_REF].

Horvath & Morf (2010) argued that narcissists and genuine self-esteemed individuals follow different strategies for social enhancement. Narcissists exploit self-enhancement opportunities primarily by augmenting self-ratings on positive traits while nonnarcissists self-present themselves more moderately and use more socially accepted discounting of negative traits. In this paper, we conceptualize social enhancement as a non-narcissistic tendency to present accurate image of oneself in an SNS platform.

Prior IS research has successfully used the concept of image, a variable similar to social enhancement [START_REF] Moore | Development of an instrument to measure the perception of adopting an Information Technology innovation[END_REF][START_REF] Venkatesh | A theoretical extension of the technology acceptance model: Four longitudinal field studies[END_REF]. It has been found that individuals' technology usage behaviour is motivated by image (e.g., Venkatesh & [START_REF] Venkatesh | A theoretical extension of the technology acceptance model: Four longitudinal field studies[END_REF].

Hedonic motivation

Due to the advent of systems such as computer and console games, virtual worlds and SNSs, hedonic uses of IS have become increasingly important. Hedonic systems aim to provide self-fulfilling values such as fun, and pleasure to the users. Hence, the focus on motivational drivers of system use has shifted from extrinsic to intrinsic reflections, such as perceived enjoyment (Van der Heijden, 2004; Turel & Serenko, 2012). Intrinsic motivation refers to the activity spawned by the pleasure of action [START_REF] Ryan | On energy, personality, and health: Subjective vitality as a dynamic reflection of well-being[END_REF]) and drives voluntary activity done for no apparent reinforcement other than the process of performing the activity per se (de Charms, 1968).

Prior IS literature has successfully used the concept of intrinsic motivation. Venkatesh (2000) found intrinsic motives are important for system use decisions. Van der Heijden (2004) even found perceived enjoyment (i.e., an intrinsic motivation) as more important than perceived usefulness in predicting intention to use hedonic systems. Perceived enjoyment was also found important predicting habit and engagement in SNS usage context [START_REF] Turel | The benefits and dangers of enjoyment with social networking websites[END_REF]. Based on these findings, we decided to use perceived enjoyment as the proxy of hedonic motivation. We conceptualize perceived enjoyment as the extent to which the activity of using an SNS is perceived to be enjoyable in its own right [START_REF] Davis | Extrinsic and intrinsic motivation to use computers in workplace[END_REF].

Voyeurism

Traditionally, the concept of voyeurism has referred to the sexual interest in or practice of spying on people who are naked or who are engaged in sexual activities, or other actions that are usually considered private in nature [START_REF] Hirschfeld | Sexual anomalies and perversions: Physical and psychological development, diagnosis and treatment (new and revised edition)[END_REF].

The most extreme behavioural manifestations of voyeurism are clinically regarded as abnormal sexual behaviour. American Psychiatric Association's (APA) Diagnostic and Statistical Manual of Mental Disorders (DSM-IV, 1994) offers the following psychiatric diagnostic criteria for voyeurism:

A. Over a period of at least 6 months, recurrent, intense sexually arousing fantasies, sexual urges, or behaviors involving the act of observing an unsuspecting person who is naked, in the process of disrobing, or engaging in sexual activity. B. The person has acted on these sexual urges, or the sexual urges or fantasies cause marked distress or interpersonal difficulty.

Today, as pointed out by Calvert (2009), voyeurism does not necessarily include sexuality. In the context of media use, Calvert (2009, p. 2) defines voyeurism as "the consumption of revealing images of and information about other's apparently real and unguarded lives, often yet not always for purposes of entertainment but frequently at the expense of privacy and disclosure, through the mass media and Internet." Many everyday activities such as watching reality TV can be driven by voyeuristic motives without any sexual connotation. In this paper, we conceptualize voyeurism as pleasure derived by accessing others' private content in SNSs [START_REF] Metzl | Voyeur Nation? Changing Definitions of Voyeurism, 1950-2004[END_REF].

Exhibitionism

Exhibitionism refers to self-disclosure "process of making the self, known to others" (Jourard & Lasakow, 1958, p. 91). With regard to exhibitionism, the self-disclosure is not driven by building trust to develop interpersonal relationships (see e.g. [START_REF] Joinson | Measuring self-disclosure online: Blurring and non-response to sensitive items in web-based surveys[END_REF]. Rather, the self-disclosure is driven by one's narcissistic tendency [START_REF] Ames | The NPI-16 as a short measure of narcissism[END_REF][START_REF] Brunell | Narcissism and academic dishonesty: The exhibitionism dimension and the lack of guilt[END_REF][START_REF] Carpenter | Narcissism on Facebook: Self-promotional and anti-social behaviour[END_REF] to demonstrate superiority and to seek for admiration from other people in order to build a desirable self-image. To this end, Oxford English Dictionary defines exhibitionism as an extravagant behaviour that is intended to attract attention to oneself and Webster's Ninth Collegiate Dictionary defines it as the act of practice of behaving so as to attract attention to oneself.

As a psychological trait in the general population, exhibitionism is not overtly negative or harmful like for example manipulation of others or hostility. Rather, it indicates extraversion and low impulse control [START_REF] Panek | Mirror or megaphone?: How relationships between narcissism and social networking site use differ on Facebook and twitter[END_REF]. Furthermore, exhibitionism is considered as a first order-component of narcissistic personality [START_REF] Ames | The NPI-16 as a short measure of narcissism[END_REF][START_REF] Raskin | A principal-components analysis of the narcissistic personality inventory and further evidence of its construct validity[END_REF]. A combination of self-absorption, vanity, superiority, and exhibitionistic tendencies that illustrates the features of self-love and theatrical selfpresentation is labeled grandiose exhibitionism [START_REF] Ackerman | What does the narcissistic personality inventory really measure?[END_REF].

Exhibitionism is diagnosed with the following criteria (DSM-IV, 1994): A. Over a period of at least 6 months, recurrent, intense sexually arousing fantasies, sexual urges, or behaviours involving the exposure of one's genitals to an unsuspecting stranger. B. The fantasies, sexual urges, or behaviours cause clinically significant distress or impairment in social, occupational, or other important areas of functioning.

People's self-disclosure in SNSs can be driven by exhibitionism as a narcissistic trait [START_REF] Carpenter | Narcissism on Facebook: Self-promotional and anti-social behaviour[END_REF]. In this paper, we do not consider exhibitionism as a psychopathological condition but conceptualize it as a pleasure derived from disclosing private information online in order to gain superiority.

3

Hypotheses Development

The Bright Side: Social enhancement and perceived enjoyment as predictors of subjective vitality

Prior psychology research differentiated between autonomous and controlled motivations [START_REF] Ryan | On energy, personality, and health: Subjective vitality as a dynamic reflection of well-being[END_REF]). An individual can be motivated to act in a particular way by external compulsion (i.e., controlled motivations) or out of inwardly endorsed motives such as personal interests or values (i.e., autonomous motivations). Intrinsic motivations have been regarded as autonomous by definition, which means that they are experienced as emanating from one's self, whereas extrinsic motivations vary in the degree to which they are autonomous versus controlled [START_REF] Deci | A motivational approach to self: Integration in personality[END_REF]. We argue that social enhancement and hedonic motivations are autonomous. Actions for enhancing social status and enjoyment in SNSs are self-determined. Koopmans & Sremac (2011) pointed that presenting authentic self is autonomous. Based on this, it is logical to assume that the act of enhancing self or building one's appropriate image is autonomous. Prior IS research also suggested enjoyment as an intrinsic motivation (Van der Heijden, 2004) and hence it is autonomous by definition [START_REF] Ryan | On energy, personality, and health: Subjective vitality as a dynamic reflection of well-being[END_REF]. Individuals act with an internal perceived locus of causality in SNSs in order to gain social enhancement, and enjoyment [START_REF] Deci | A motivational approach to self: Integration in personality[END_REF].

When people are intrinsically motivated or autonomously extrinsically motivated, they will feel their energy enhanced [START_REF] Nix | Revitalization through self-regulation: The effects of auton omous and controlled motivation on happiness and vitality[END_REF]. Consequently, it is logical to assume that social enhancement and enjoyment may enhance an individual's subjective vitality. Based on this we propose the following two hypotheses.

H1. Social enhancement positively influences subjective vitality H2. Enjoyment positively influences subjective vitality

We also argue that the other two motivations: exhibitionism and voyeurism are controlled motivations and hence these would not have any influence on subjective vitality. Voyeuristic people look for content that they cannot otherwise see in an SNS. These people do so due to their curiosity or comparing themselves with others to maintain self-esteem. Exhibitionist people look for opportunities to show that they are superior to others by presenting a possible self-image in an SNS. They do such activities because of a pressure to maintain self-esteem. Both voyeuristic and exhibitionistic behaviours are governed by a regulatory process within an individual that is experienced as a pressure or demand. Prior psychology research suggests that behaviours that are executed because of pressure or demand are controlled in nature [START_REF] Nix | Revitalization through self-regulation: The effects of auton omous and controlled motivation on happiness and vitality[END_REF]. Thus, exhibitionism and voyeurism are controlled form of behaving and are not likely to enhance subjective vitality [START_REF] Deci | A motivational approach to self: Integration in personality[END_REF].

3.2

The Dark Side: Voyeurism and exhibitionism as predictors of addiction

Prior psychiatric literature suggests that exhibitionism and voyeurism are addictive sexual behaviours (Coleman-Kennedy & Pendley, 2002; [START_REF] Roller | Sex addiction and women: A nursing issue[END_REF]. Following this, we argue that the exhibitionistic and voyeuristic motivations would make individuals addicted to the SNS.

Prior IS research on addiction suggests that excessive use of IS may develop habit which eventually lead to addiction [START_REF] Turel | The benefits and dangers of enjoyment with social networking websites[END_REF]. Following this finding, we argue that voyeuristic and exhibitionistic individuals use SNSs heavily and thus these people become addicted. The logical basis of this argument is that voyeurism cannot exist without disclosure whereas exhibitionists require an audience. Both types of individuals depend on each other (i.e., control each other) in an SNS platform. Both types of individuals require acceptance from each other to raise their self-esteem [START_REF] Silverstein | Exhibitionism as countershame[END_REF]. In the process of gaining acceptance from others, exhibitionistic people spend huge amount of time to constantly post contents while voyeuristic people spend huge amount of time to constantly consume contents posted by others. Eventually, many people start to post contents publicly over real friends [START_REF] Kuss | Social networking on the Internet: From pastime to excess and addiction[END_REF]. Similarly, many individuals start to consume contents of unknown people. Spending hours in an SNS can lead to significant behavioural and psychological problems. For instance, they might feel a compulsion to use the SNS to meet their exhibitionistic and voyeuristic needs. Consequently, we propose the following two hypotheses.

H3. Voyeurism positively influences addiction to SNS H4. Exhibitionism positively influences addiction to SNS

We also argue that the autonomous motivations: social enhancement and enjoyment are not expected to influence addiction. Addictive behaviours are compulsive. It means that an addict performs his addictive behaviour because he experiences an irresistible urge to do it. The addict performs the act repeatedly and unable to stop it. The act is not something that the addict does out of his/her self-choice. This implies that autonomous motivations are not expected to influence addiction. Koopmans & Sremac (2011) pointed that addiction might be incompatible with autonomy as addiction is often been equated with loss of control, powerlessness, and unmanageability.

Controlling for habit

Prior IS literature found habit (i.e., the extent to which people tend to perform behaviours automatically because of learning) as a significant determinant of addiction [START_REF] Turel | The benefits and dangers of enjoyment with social networking websites[END_REF]. Consequently, we decided to control the effect of habit on addiction and subjective vitality in this paper. Figure 1 below summarizes the hypothesized relationships. After the questionnaire was drafted, it was first sent to two senior scholars for a review.

Based on their comments, some items were adjusted to make the wordings more precise. The final questionnaire items are shown in Table 1.

Social

Data collection

The data was collected with an online survey among student of a Finnish university. The respondents were randomly selected from a sample that in turn was randomly selected from student database. The survey was sent to altogether 1,500 respondents. Altogether 289 usable responses were received. This yielded to a response rate of 19.27 per cent. 68% of the respondents were female.

To investigate the possible non-respondent bias, we ran a series of independent samples t-tests. The respondents were first divided into two groups based on the median date of completion of the survey and then compared their responses on subjective vitality, addiction, habit, social enhancement, voyeurism, exhibitionism and enjoyment [START_REF] Armstrong | Estimating nonresponse bias in mail surveys[END_REF]. The test did not reveal any statistically significant differences between the early and late responses, supporting the notion that non-response bias was not a major concern in our study.

Data analysis

Partial least squares (PLS) approach with WarpPLS was utilized in the analysis. PLS is a second-generation regression method that combines confirmatory factor analysis with linear regression, and this makes it possible to run the measurement and structural models simultaneously. A rule of thumb for the required sample size in PLS is that the sample should be at least ten times the most complicated multiple regressions in the model [START_REF] Barclay | The partial least squares (PLS) approach to causal modeling: Personal computer adoption and use as an illustration[END_REF]. The sample size in this paper fulfils this criterion well. We followed Gefen and Straub's (2005) procedure to test convergent and discriminant validity. Convergent validity indicates the extent to which the items of a scale that are theoretically related are also related in reality. Convergent validity is ensured by comparing the item loadings, composite reliability (CR), and average variance extracted (AVE) values. [START_REF] Fornell | Evaluating structural equation models with unobservable variables and measurement error[END_REF] recommended path-loading values at least 0.7 to be acceptable. Based on this criterion, SV2 from subjective vitality construct was removed. Similarly, ADDICT3, and ADDICT4 have been removed from addiction construct. From Table 2, we observe that all the constructs have composite reliability values between 0.855 and 0.920, fulfilling the recommended value proposed by [START_REF] Nunnally | Psychometric theory[END_REF]. Finally, all AVE values exceed the threshold of 0.5 as recommended by [START_REF] Fornell | Evaluating structural equation models with unobservable variables and measurement error[END_REF].

Note: Items in Italic were removed due to low loadings

Discriminant validity refers to whether the items measure the construct in question or other (related) constructs [START_REF] Gefen | A practical guide to factorial validity using PLS-Graph: Tutorial and annotated example[END_REF]. We verified discriminant validity using correlation matrix and factor analysis. Table 2 shows the correlation matrix with the square root of AVE values presented diagonally. The square root of the AVE value for the variables is consistently greater than the off-diagonal correlation values, suggesting satisfactory discriminant validity between the variables [START_REF] Fornell | Evaluating structural equation models with unobservable variables and measurement error[END_REF]. The "item to latent variable correlation" values obtained from the confirmatory factor analysis are shown in Table 3. It shows that all items have cross-loadings coefficients lower than the factor loading on their respective assigned latent variable, suggesting that discriminant validity on the item level is met for all the constructs [START_REF] Gefen | A practical guide to factorial validity using PLS-Graph: Tutorial and annotated example[END_REF].

Overall, these tests of validity and reliability provided us with a high degree of confidence with the measures used in testing our research model.

Table 2. Correlations between latent variables and squared root of average variance extracted

5

Results and discussions

PLS results

The test of the structural model includes estimates of the path coefficients, which indicate the strengths of the relationships between the dependent and independent variables, and the R-square values, which represent the amount of variance explained by the independent variables. Figure 2 shows the results of the hypothesized structural model. As hypothesized, social enhancement (β=0.20, p<0.001) and perceived enjoyment (β=0.31, p<0.001) had significant effect on subjective vitality after controlling the effect of habit on it. The explained amount variance of subjective vitality by its predictors was 23 per cent Exhibitionism (β=0.41, p<0.001) had significant influence on addiction after controlling the influence of habit on it. Interestingly, voyeurism (β=0.08, ns) had no-significant influence on addiction. Taken together, the determinants explained 34 per cent of the variance in addiction.

Key findings

We point out to two observations that we think are the most interesting. First, we found that both social enhancement and enjoyment influence subjective vitality. This finding implies that an accurate presentation of one's image and enjoyment experienced by communicating and interacting with peers in an SNS improves one's subjective vitality.

For example, when one receives positive feedback for the content he/she has posted (e.g., pictures, profile information, etc.), it may influence his/her subjective vitality. It also provides him/her pleasure. Additionally, surfing around in SNS for a while may offer a refreshing break and help one to feel more energetic. Second, we found that exhibitionism significantly predicted addiction to SNS while controlling for the influence of habit. This may imply that exhibitionists frequently post content and check how many people comment or 'like' in their updates. In doing so, they may feel an urge to use the SNS more and more and eventually develop addiction. Interestingly, we also found that individuals' voyeuristic behaviours had non-significant influence on addiction to SNS. The reason behind the non-significant relationship can be explained by the fact that the SNS under investigation (i.e. Facebook) contains more features to support exhibitionistic behaviours and fewer features to support voyeuristic behaviours. Features like updating status, check-in, posting photos, etc. that supports exhibitionistic behaviours can be accessed easily in both desktop and mobile versions of Facebook. These features are integrated in mobile devices in such a way that individuals can execute these features with just few taps. In contrast, voyeuristic activities like finding a person from outside of one's friend list and viewing his/her content require much effort. Recently, Facebook has introduced the Graph Search feature that has eased the process of finding and accessing the content on Facebook. However, the Graph Search feature has not been included yet in the Facebook applications for mobile devices. Additionally, it is still not available for all (e.g. Finnish) Facebook users. Nevertheless, the Graph Search will most likely considerably increase the voyeuristic value of Facebook.

Figure . 2. PLS results

Theoretical implications

Our study has three theoretical implications for IS research. First, we advance the understanding of dual effect of IT. Prior IS research has mostly investigated either the positive (e.g. DeLone & MacLean, 2003) or negative (e.g. Turel et al., 2011) effect of IT use. However, in this study we have shown that hedonic systems such as SNSs may lead to both positive and negative individual-level consequences. To this end, we included a benefit related variable (i.e., subjective vitality), and a danger related variable (i.e., addiction) in our research model as the dependent variables and examined their predictors. Second, we accumulate the understanding of addiction in the SNS context. Prior IS research mostly implied that SNS enjoyment shapes habitual behaviour, which in turn leads to addiction [START_REF] Turel | The benefits and dangers of enjoyment with social networking websites[END_REF]. We found that exhibitionism is a significant predictor of addiction to SNSs after controlling the influence of habit. In fact, we found that the influence of exhibitionism on addiction was much stronger than that of habit.

Finally, drawing the concept of autonomous motivation [START_REF] Deci | A motivational approach to self: Integration in personality[END_REF], we hypothesized and empirically found that both social enhancement and enjoyment are significant predictors of subjective vitality. Despite its importance, prior IS research did not put enough effort investigating the concept of subjective vitality. We suggest future research investigating other autonomous motivations that influence subjective vitality.

Practical implications

Our study has three practical implications. First, it should be remembered that IT may have dual effects on individuals. Using an SNS may both improve user's subjective vitality but also create addiction. In addition, to identifying the psycho-social predictors such as the variables employed in this study, understanding the dual effect of SNS's use requires also understanding the use patterns and use context. For example, taking short breaks from work and surfing SNSs may improve individual's subjective vitality, however, spending hours or frequent SNS surfing might lead to addiction. We hope that organizational and governmental decision-makers pay increased attention to the potential dual effects of IT in general and SNS in particular. We hope that the decisionmakers consider the possibility that the increased IT use is not mechanistically positive, and that all uses are not equally beneficial, or harmful. Second, we found that individual's exhibitionistic behaviours cause addiction. SNSs contain many features for performing exhibitionistic behaviours. For example, status updates, pictures, likes and comments are some of the features that exhibitionists might utilize to show off. (Mäntymäki & Islam, 2016;Islam et al., In Press). To reduce the risk of addiction, we slightly provocatively suggest that SNS providers and their legislative regulators would consider whether the exhibitionistic appeal of SNSs could be constrained. For example, the number of status updates or photos per day or month could be limited to a certain maximum to reduce the adverse effects and time spent on SNSs.

Finally, we found that an SNS use can be a source of subjective vitality improvement as it affords portraying one's appropriate image and offers hedonic value. Based on this finding, we suggest organizations that have categorically banned, and occasionally even tried to prevent SNS use in the workplace to reconsider this decision.

6

Limitations and future research

Our study has a number of limitations that may serve as the avenue for future research. First, the research was cross-sectional. The beliefs of the users regarding a system will change as the users gain experience of a target system but such changes cannot be captured with the type of cross-sectional study undertaken. Thus, longitudinal research is necessary to overcome the issue. Second, the study has been conducted using a single SNS (Facebook) in a single Finnish university. Hence, generalizing the findings directly to other user groups or other SNSs is not an appropriate course of action. Third, we included only a few drivers of subjective vitality and addiction. Future studies could incorporate a more extensive set of drivers of subjective vitality and addiction. Finally, yet we focused on subjective vitality and addiction to capture the dual effect of SNS use, these two constructs are by no means an exhaustive presentation of the possible positive and negative outcomes. Thus, future research could aim at identifying additional positive and negative outcomes of SNS use.

Fig. 1 .

 1 Fig. 1. The research model

(

  I use Facebook, sometimes I feel so alive I just want to burst 1/0.843 <0.001 Addiction to SNS (Andreassen et al., 2012) ADDICT1: I spend a lot of time thinking about Facebook or plan use of Facebook 1.51 0.80 0.672/0.706 <0.001 ADDICT2: I feel an urge to use Facebook more and more 1.45 0.76 0.817/0.809 <0.001 ADDICT3: I use Facebook in order to forget about personal problems 1have tried to cut down on the use of Facebook without success 1become restless or troubled if I am prohibited from using Facebook 1.57 1.01 0.772/0.795 <0.001 ADDICT6: I use Facebook so much that it has a negative impact on my studies/work 1Ames et al., 2006) EXHIBIT1: I really like to be the center of attention on Facebook 2am apt to show off on Facebook if I get the chance 2get upset when people don't comment/put a like on my posts in Facebook 2get into a bad mood if people don't comment/put like on my posts on Facebook 1enjoy viewing Facebook because it helps me get a peek into other's private moments 2like Facebook because it provides access to other people'like Facebook because people don't know that I am accessing their information 2like watching people when they don' t know that they are being watched 2get satisfaction out of watching others when they are unaware 1

  variance extracted (AVE), Composite reliability (CR), Enjoyment (ENJ), Social enhancement (SE), Exhibitionism (EXHIBIT), Voyeurism (VOYEUR), Subjective vitality (SV), Addiction (ADDICT), Habit (HABIT)

  Note: ***: p < 0.001, n.s.: non-significant

Table 1 .

 1 Construct items' averages, standard deviations (std.), and loadings

	Item
	Construct

Table 3 .

 3 Item to latent variable correlations

	Habit	0.493	0.535	0.538	0.360	0.460	0.306	0.397	0.365	0.385	0.322	0.301	0.375	0.223	0.328	0.246	0.258	0.248	0.226	0.267	0.288	0.259	0.266	0.341	0.267	0.315	0.312	0.888	0.880	0.806
	Addiction	0.273	0.203	0.198	0.203	0.118	0.397	0.331	0.300	0.318	0.407	0.284	0.416	0.414	0.246	0.208	0.244	0.239	0.300	0.165	0.265	0.226	0.218	0.768	0.839	0.787	0.788	0.379	0.309	0.307
	Subjective vitality	0.391	0.381	0.335	0.352	0.335	0.255	0.341	0.340	0.284	0.240	0.168	0.271	0.163	0.252	0.151	0.088	0.113	0.112	0.822	0.843	0.854	0.843	0.217	0.208	0.177	0.228	0.268	0.315	0.242
	Voyeurism	0.228	0.188	0.169	0.264	0.211	0.305	0.263	0.271	0.401	0.257	0.243	0.425	0.408	0.744	0.798	0.865	0.858	0.778	0.123	0.154	0.136	0.169	0.240	0.252	0.234	0.244	0.327	0.270	0.226
	Exhibitionism	0.338	0.320	0.302	0.314	0.336	0.552	0.508	0.471	0.488	0.776	0.739	0.851	0.720	0.479	0.353	0.348	0.260	0.333	0.249	0.271	0.243	0.165	0.394	0.373	0.442	0.361	0.342	0.356	0.327
	Social enhancement	0.420	0.366	0.356	0.446	0.358	0.816	0.832	0.844	0.825	0.522	0.481	0.512	0.358	0.364	0.335	0.271	0.239	0.318	0.304	0.332	0.344	0.257	0.363	0.325	0.289	0.314	0.356	0.408	0.366
	Enjoyment	0.864	0.898	0.852	0.717	0.832	0.333	0.476	0.369	0.357	0.331	0.252	0.360	0.237	0.352	0.271	0.146	0.126	0.143	0.359	0.374	0.362	0.348	0.264	0.162	0.203	0.134	0.437	0.599	0.444
	Items	HED1	HED2	HED3	HED4	HED5	SOC1	SOC2	SOC3	SOC4	EXHIBIT1	EXHIBIT2	EXHIBIT3	EXHIBIT4	VOYEUR1	VOYEUR2	VOYEUR3	VOYEUR4	VOYEUR5	WB1	WB3	WB4	WB5	ADDICT1	ADDICT2	ADDICT5	ADDICT6	HABIT1	HABIT2	HABIT3


Matti Mäntymäki
email: matti.mantymaki@utu.fihannu.salmela@utu.fimarja.turunen@utu.fi

Hannu Salmela

Marja Turunen

Do Business Ecosystems Differ from Other Business Networks? The case of an emerging business ecosystem for digital real-estate and facility services

Keywords: business ecosystem, cluster, network, industry, value network, collective consciousness, digitalization, conceptual analysis

The concept of ecosystem emanates from ecology and subsequently has been broadly used in business studies to describe and investigate complex interrelationships between companies and other organizations. However, it is widely known that borrowing constructs from natural sciences to social sciences, or vice versa, can be problematic. For example, the use of the ecosystem concept outside its original domain has received criticism. To better understand the essence, applicability and boundaries of the business ecosystem concept, this study conducts a conceptual analysis of a set of concepts used to describe business networks. To this end, we analytically comparing business ecosystem with other prominent concepts, namely industry, population, cluster, inter-organizational network and value network. We then present an illustrative case of an emerging business ecosystem of digital services for real-estate and facility services and scrutinize the applicability of the focal concepts for our case. The results indicate a need for increased conceptual clarity when describing business networks. The results indicate a need for conceptual clarity when describing business networks. We conclude with a synthesis and discuss under what circumstances using the business ecosystem concept may add value for research and practice. The paper contributes to the business ecosystem literature by clarifying the similarities and differences between business ecosystem and other concepts used to describe business networks.

Introduction

The term ecosystem has diffused outside its original domain in natural sciences [START_REF] Autio | Innovation ecosystems. The Oxford handbook of innovation management[END_REF][START_REF] Mäntymäki | In Search for the Core of the Business Ecosystem Concept: A Conceptual comparison of business ecosystem[END_REF]. In biology, an ecosystem, or ecological system, typically denotes a unit of biological organization made up of all the organisms in a given area, thus forming a "community". Organisms within a community interact with the physical environment so that the flow of energy leads to characteristic trophic structure and material cycles within the system [START_REF] Odum | The strategy of ecosystem development[END_REF]).

The specific focus of this paper is the use of the ecosystem metaphor in describing business networks (cf. [START_REF] Aarikka-Stenroos | Network management in the era of ecosystems: Systematic review and management framework[END_REF]. The literature has coined concepts such as business ecosystems [START_REF] Peltoniemi | Business ecosystem as the new approach to complex adaptive business environments[END_REF], innovation ecosystem [START_REF] Oh | Innovation ecosystems: A critical examination[END_REF]), software ecosystems [START_REF] Hyrynsalmi | The influence of developer multihoming on competition between software ecosystems[END_REF], service ecosystem [START_REF] Vargo | From repeat patronage to value co-creation in service ecosystems: a transcending conceptualization of relationship[END_REF], product ecosystem [START_REF] Frels | The integrated networks model: Explaining resource allocations in network markets[END_REF], and platform ecosystem (Ceccagnoli et al. 2013), to name but a few. The widespread use of the ecosystem metaphor implies that has been viewed to provide some value-added for researchers. At the same time, however, the use of ecosystem metaphor has also been criticized and the accuracy of the metaphor questioned [START_REF] Oh | Innovation ecosystems: A critical examination[END_REF][START_REF] Hyrynsalmi | Letters from the war of ecosystems[END_REF]. [START_REF] Boulding | General system theory -The skeleton of science[END_REF] holds that analysing tools, such as conceptualizations of organization need to be at the same level of the complexity with the phenomena in question. To foster this element, we apply Turunen's (2015) view of organizing which maintains that all conceptualization of are embedded in organizational consciousness which we take along in this paper.

The purpose of this paper is to understand [START_REF] Aarikka-Stenroos | Network management in the era of ecosystems: Systematic review and management framework[END_REF] what is a business ecosystem and (2) how does the concept of business ecosystem relate to other similar concepts, including what role cross-organizational collective consciousness plays in the ecosystem concept. In this paper, we focus on five widely discussed concepts that have been used to describe business networks, i.e. groups of inter-connected organizations, namely industry, population, inter-organizational network, cluster and value network.

Based on our analysis we argue that concepts industry and population emphasize competitive relationships between firms, whereas an inter-organizational network and a cluster place more emphasis on collaboration. In this respect, an ecosystem is a more diverse concept, presuming both collaborative and competitive relationships, which generate intertwined awareness processes such as collective consciousness [START_REF] Turunen | Toward a Consciousness-Based View of Organizing[END_REF], which draws on social sciences, particularly from psychology and sociology.

This study contributes to the literature by demonstrating that there is an overlap between the business ecosystem concept and other similar concepts. This is particularly so in the use of these concepts by practitioners. For researchers, our study shows a clear need for more fine-grained conceptual and theoretical analyses of the business ecosystem concept. We further conclude that additional scrutiny of the ecosystem metaphor and its value-added for theorizing and for managerial communication is needed.

The paper proceeds as follows: after the introductory section, we present a discussion of the business ecosystem concept. Thereafter, the present a set of related constructs used to depict business networks and analyse how they converge with, and diverge from, the business ecosystem construct. The paper concludes with a synthesis of the analysis and suggestion for future research.

2

The Business Ecosystem concept

The business ecosystem concept was coined by [START_REF] Moore | Predators and prey: a new ecology of competition[END_REF] In their business ecosystem conceptualization, Iansiti and Levien (2004, 8-9) put more emphasis on networks. Accordingly, an ecosystem is essentially as an analogy to describe modern business networks. They also acknowledge the using biological analogies in business literature can be a controversial issue and further argue that "the analogy between evolved biological systems and networks of business entities is too often misunderstood." Iansiti and Levien (2004, 5). Authors further lament that their use of the term ecosystem is probably closer to the biological term community but they use the term ecosystem to highlight that they are discussing a complex system and working with a biological analogy.

Based on subsequent literature on business ecosystems, such systems appear to have at least three characteristic features:

1. Members of an ecosystem are highly interconnected. Interconnectedness refers to the fact that the success or failure of a member of an ecosystem affects the other members. 2. A business ecosystem often includes a keystone that "regulates ecosystem health" (Moore 1993 p.8). The keystone is typically an actor that is able to support and orchestrate the activities that take place within the ecosystem. 3. Ecosystems are complex systems [START_REF] Peltoniemi | Business ecosystem as the new approach to complex adaptive business environments[END_REF]. As described by Cowan (1994, 1), complex systems "contain many relatively independent parts which are highly interconnected and interactive." Lewin in turn (1999) further laments that complex systems are systems whose properties are not fully explained by an understanding of its constituent parts. [START_REF] Lewin | Complexity: Life at the edge of chaos[END_REF]. Thus, complex systems can be informed by the process research (James, 1909;[START_REF] Tsoukas | On organizational becoming: Rethinking organizational change[END_REF] of collective interaction (Kimble, 2008).

While the characteristic features of #1 and #3 are somewhat congruent, the second one raises a question: how can a complex, interconnected, system be regulated by one actor? This appears to be one of the internal tensions related to the concept of business ecosystem. On the other hand, co-operation in business ecosystem creates a common awareness that in turn helps to manage diversity and complexity.

In software business, the ecosystem concept has been used to depict business networks built around a key player such as Apple. The core of Apple's ecosystem is the App Store. For customers, the App Store is a software marketplace where Apple acts as a gatekeeper and trust provider. For application developers, such as providers of different mobile games, Apple provides the development tools and a distribution channel via its App Store. For Apple, the App Store is a means to generate additional revenue but also a mechanism to significantly extend its value proposition beyond hardware and the core software that is pre-installed in its products.

Comparison of industry, population, cluster, interorganizational network, value network, and business ecosystem

One way to seek a better understanding of the ecosystem is to compare it with other similar concepts used in prior research. In the following, we shall present and compare an ecosystem with five such concepts: industry, population, inter-organizational network, value network and cluster. While the first two assume relationships between firms as primarily competitive, the last three bring the collaborative relations into the surface.

Industry

Perhaps the most traditional concept used in describing and classifying companies' environment is industry. Generally speaking, an industry consists of companies or networks of companies that provide similar product or service offerings to same markets.

Porter defines the concept industry as follows (Porter, 1980, p. 32):

"Structural analysis, by focusing broadly on competition well beyond existing rivals, should reduce the need for debates on where to draw industry boundaries. Any definition of an industry is essentially a choice of where to draw the line between established competitors and substitute products, between existing firms and potential entrants, and between existing firms and suppliers and buyers."

The underlying theme in the concept is that competitive relations define borders for industry. Industries can be treated as entities, having attributes of their own. For instance, because of structural differences, some industries may be more profitable than others [START_REF] Porter | Competitive Strategy: Techniques for Analyzing Industries and Competitors[END_REF]. The dynamics inside the industry is largely explained by forces of competition.

The idea of an industry as a competitive marketplace does not exclude collaboration completely, but it is seen as an exception, labelled with terms such as strategic alliances or co-opetition [START_REF] Bengtsson | Coopetition" in Business Networks-to Cooperate and Compete Simultaneously[END_REF][START_REF] Hamel | Collaborate with your Competition and Win[END_REF].

Industries are also complex systems, even if no collaborative relations are taken into consideration. For example, in hypercompetitive industries, companies need to rely on complex strategic manoeuvring in order to capitalize on new opportunities in the marketplace faster than their competitors (D'Aveni, 1994). However, as the industry players focus on their own competition, there does not emerge cross-organizational collaboration or collective awareness among industry players.

A company can belong to industries of different levels, for example game developers belong to game industry but on a more generic level also to software industry. When industry concept is applied to computer and mobile game providers, the emphasis is on competitive relations: game providers compete over same customers' (players') time and money. They also compete with other forms of current and future forms of entertainment. In the industry concept, platform providers like Apple are seen as distribution channel firms, whose negotiation power decreases profit margins of game providers. By leaving the collaborative relations behind, industry and industry analysis brings forth the competitive ones -which may be sufficient to explain many complex phenomena in the gaming industry.

Population

Population is a theoretical concept used in analysing variability of organizations over time [START_REF] Hannan | Organizational Ecology[END_REF]). Here the classification of companies is based on a number of attributes, such as the size of the organization, organizational form, and strategy. As an example, small, family owned companies that focus on niche markets can be seen as one population. The purpose of this classification is to explain variance and dynamics between organizations. Hannan and Freeman describe the approach as follows (Hannan &Freeman, 1989, p. 13):

"The population ecology perspective concentrates on the sources of variability and homogeneity of organizational forms. It considers the rise of new organizational forms and the demise of transformation of existing ones. In doing so, it pays considerable attention to population dynamics, especially the processes of competition among diverse organizations for limited resources such as membership, capital, and legitimacy."

A basic assumption underlying the population concept is that competition in markets will favour those populations of companies that have the characteristics needed in new situations. Hence, "there are strong parallels between processes of change in organizational populations and in biotic populations" (Hannan & Freeman, 1989, p. 13).

Population ecology acknowledges that sometimes organizations form communities, i.e. organizations that collaborate with each other. Hence, survival could take place at the level of communities, rather than at the level of populations of similar companies. This idea is not, however, included in the analysis. The power of population ecology is in explaining, why some populations of independent firms succeed in competition while others vanish.

Overall, population ecological models demonstrate, that complex phenomena behind birth and growth of new types of companies and demise of existing ones can be explained with competitive relationships. It can be concluded that those companies who can best leverage the ecosystem reap the best benefits.

In mobile and computer game business, an example of population is the emergence, growth, and typically also decline of a certain types of game developers. While the companies compete with one another, they also share the destiny of their competitors, in particular the ones which are most similar to them. The population ecology model explains, how new types of companies emerge to markets, thus causing existing companies to suffer from shrinking markets.

For example, the rapid growth and success of freemium games from game companies such as Supercell's Clash of Clans or King's Candy Crush Saga took markets from established game companies relying on traditional pricing. This may also have contributed to the birth of a new type of population: small and medium-sized game companies offering freemium games (cf. [START_REF] Koskenvoima | Why do small and medium-size freemium game developers use game analytics[END_REF]. Hence, by using long time frames, population ecology model explains many "ecology" type of phenomenapurely with competitive relations.

Inter-organizational network

Research on business networks or inter-organizational networks takes a completely opposite approach. Research focuses on such entities, where inter-relationships of companies are seen as predominantly collaborative. Because of a wide variety of collaborative forms, giving an exact definition for an inter-organizational network is difficult. Nevertheless, Provan, Fish and Sydow (2007, p. 482) provide the following characterization of an inter-organizational network:

"… we make no effort to try to offer an all-encompassing definition of an interorganizational network. Rather, we focus instead on one specific type of network that has been frequently discussed but only infrequently researched, namely, a whole network consisting of multiple organizations linked through multilateral ties. A whole network is viewed here as a group of three or more organizations connected in ways that facilitate achievement of a common goal. That is, the networks we discuss are often formally established and governed and goal directed rather than occurring serendipitously" [START_REF] Kilduff | Social networks and organizations[END_REF].

A characteristic feature of an inter-organizational network is that it comprises several independent organizations. Like all groupings of organizations, also inter-organizational networks evolve, but such evolution can be treated as conscious and goal-directed. For example, Ring and Van de Ven (1994) have proposed a process framework that focuses on formal, legal, and informal socio-psychological processes by which parties jointly negotiate, commit to, and execute their relationship.

The idea of competition within a network is not completely absent. For instance, the governance processes described by Ring and Van de Ven (1994) need to ensure both efficient and equitable outcomes. They will also need to be able to deal with conflicts as they arise. While the concept of inter-organizational network does not deny conflicts of interest, the primary emphasis is on collaborative ties between individual organizations. This suggests that inter-organizational collaboration does not hit the potential which is available.

It is perhaps surprising; how few are the examples of software companies engaging in genuinely collaborative network relations that would involve three or more organizations. In computer and mobile games, collaboration between game companies and movie producers can, perhaps, be seen as an example of such a collaboration. Collaboration in open-source forums, or digital platforms, can sometimes fulfill some requirements of an inter-organizational network. But traditionally the relationships have been arms-length relations without shared governance or formal contracts.

Cluster

The term cluster emanates from the works of Michael [START_REF] Porter | The competitive advantage of nations[END_REF] on nations' competitive advantage. Cluster has a strong conceptual linkage to industry as a cluster is a part or a representative of an industry [START_REF] Porter | The competitive advantage of nations[END_REF][START_REF] Dayasindhu | Embeddedness, knowledge transfer, industry clusters and global competitiveness: a case study of the Indian software industry[END_REF][START_REF] Tallman | Knowledge, clusters, and competitive advantage[END_REF]). Porter (2000, p. 16) defines cluster as follows:

"a geographically proximate group of interconnected companies and associated institution in a particular field, linked by commonalities and complementarities." (Porter, 2000, p. 16) The concept of cluster offers a vehicle to explain why large numbers of companies operating in a same market are concentrated on certain geographical locations. With the term cluster [START_REF] Porter | The competitive advantage of nations[END_REF] refers to a phenomenon linked to geographic concentrations of national industries which origin from vertical or horizontal relationships between companies. Locality is considered a key characteristic of a cluster as companies in a cluster as companies in a cluster are often located in a single city or region [START_REF] Porter | The competitive advantage of nations[END_REF][START_REF] Scheel | Knowledge clusters of technological innovation systems[END_REF][START_REF] Tallman | Knowledge, clusters, and competitive advantage[END_REF]). Cluster has a strong conceptual linkage to industry as a cluster is a part or a representative of an industry (see e.g. [START_REF] Dayasindhu | Embeddedness, knowledge transfer, industry clusters and global competitiveness: a case study of the Indian software industry[END_REF][START_REF] Tallman | Knowledge, clusters, and competitive advantage[END_REF]). [START_REF] Porter | The competitive advantage of nations[END_REF] sees intense competition within a cluster as its main driving force as competition forces companies to increase the standard of their operations in order to remain competitive. Intense competition can be due to bargaining power of customers who may be interact with several companies within the cluster. These interactions in turn accelerate exchange of information and diffusion of innovations. Collaboration and interaction build up not only cross-organizational awareness but consciousness fields [START_REF] Turunen | Toward a Consciousness-Based View of Organizing[END_REF] in the cluster.

In software business, physical proximity of companies operating in a certain field is almost a norm due to the positive network externalities. Silicon Valley is perhaps the best-known example of a geographical concentration of software companies. But there are also other countries, like India, China, Russia, Ireland and Israel, who have strong concentrations of software development [START_REF] Carmel | Offshoring Information Technology: Sourcing and Outsourcing to a Global Workforce[END_REF]. The emergence of gaming industry in Finland can also be seen as a good example of a cluster: Interest of capital investors, support from the government, and availability of programmers specialized (and interested) in games, are examples of cluster effect. While companies don't necessarily collaborate extensively (as they often are competitors), they still seem to benefit from the mere existence of other similar companies in the same region.

Value network

The value network concept emphasizes the intangible capabilities of the network. Normann and Ramirez (1993) maintain that business should not focus on the positioning the fixed set of activities along the value chain only but focus on the value creating system itself. While Normann and Ramirez addressed the systems view, Christensen and Roosenbloom (1995) set the stage for value network concept from the attacker's position in the competitive arena. According Christensen (2013, p. 54), value network extends beyond the product categories and focuses on the context i.e. the value network of a firm to excel on the competition. Christensen defines value network as a context of competitive arena on immaterial values:

"The collection of upstream suppliers, downstream channels to market, and ancillary providers that support a common business model within an industry. When wouldbe disruptors enter into existing value networks, they must adapt their business models to conform to the value network and therefore fail at disruption because they become co-opted." Christensen (2013, p. 296)

The boundaries of the value network are determined as a unique definition of the product performance and on the cost structure to meet the customer preferences, according Christensen (2013, p. 54).

While both Normann and Ramirez (1993) and Christensen (2013) focused on firms, Verna Allee's (2003) definition of value networks is applicable for any organizations, not only those on the competitive arena. She maintains that the key of the value network is the exchange of intangible assets between individuals, groups and organizations:

"A web of relationships that generates both tangible and intangible value through complex dynamic exchanges between two or more individuals, groups or organizations. Any organization or group of organizations engaged in both tangible and intangible exchanges can be viewed as a value network, whether private industry, government or public sector." (Allee, 2003, p. 268)

Allee emphasized the importance of revealing the hidden network patterns behind business processes with an analytical tool to define, map, and analyze the participants, transactions and tangible and intangible deliverables that together form a value network. As a result, value network can be seen as a conceptual tool to describe the social, technical and intangible resources between individuals, groups and organizations in highlighting the context outside the firm boundaries. It is also a predictive tool to analyze and explain company performance [START_REF] Christensen | The innovator's dilemma: when new technologies cause great firms to fail[END_REF] in competitive landscapes, for instance to explain why well-established, large companies fail. The main contribution to the conceptual analysis is that value network moves away from the linear model, which the other concepts maintain. Individuals, groups and organizations negotiate of value constellations. Thus, they are due to transformations and disruptions which may put one backwards or aside of the linear time that accrues the from the past and the future of multiple members of the network. Thus, value network might face cyclical or other trajectories in addition to linear development.

Collective consciousness is accommodated in the value network. In fact, it becomes fostered in the interrelations between individuals, groups and organizations. Indeed, the contributors of value network concept mention the benefits of collective consciousness explicitly, such as Normann, (2001) and [START_REF] Allee | The future of knowledge: Increasing prosperity through value networks[END_REF]. For instance, Allee (2003:54) maintains that "collective consciousness provides a new transformative shift towards understanding the more complex layers of the system and new avenues for connecting together with other players" i.e. collaboration in the intangible areas of value creation.

For software business, advances in digital technologies, for example in sensor technology and Internet of Things (IoT) (cf. [START_REF] Mian | Social Sensor Web: Towards a Conceptual Framework[END_REF]) enable the creation of new value networks and business models for established, mature businesses and simultaneously challenges the existing logics for value creation.

Synthesis

We scrutinized the results from the literature and business cases and present them into a synthesis of our conceptual analysis of business ecosystem, industry, and population. We derive directly from the definitions described in the above sections. After that we provide a generic description of the applicability of a concept, which is not directly related to the definition. Table 1 below presents a synthesis of our conceptual analysis of business ecosystem, industry, and population. The first and perhaps the clearest difference can be found in the ways of which the concept defines the group of companies that constitute the environment. As a result, the borders for an industry are defined by established and potential competitors [START_REF] Porter | The competitive advantage of nations[END_REF], for population by variability and homogeneity of organizational forms [START_REF] Hannan | Organizational Ecology[END_REF], for inter-organizational network by multilateral ties between organizations [START_REF] Provan | Interorganizational Networks at the Network Level: A Review of the Empirical Literature on Whole Networks[END_REF], and for cluster geographical proximity [START_REF] Porter | Location, competition, and economic development: Local clusters in a global economy[END_REF]. An ecosystem can be seen as a large number of loosely interconnected participants from various industries, who depend on each for their mutual effectiveness and survival [START_REF] Moore | The Death of Competition: Leadership and Strategy in the Age of Business Ecosystems[END_REF].

Concerning to the nature of ties, we find the concepts falling into three groups. In industries and populations, companies are connected primarily through competitive relationships [START_REF] Hannan | Organizational Ecology[END_REF][START_REF] Porter | The competitive advantage of nations[END_REF]. In inter-organizational networks, primary relationships between companies are seen as collaborative [START_REF] Provan | Interorganizational Networks at the Network Level: A Review of the Empirical Literature on Whole Networks[END_REF]. For clusters and ecosystems, ties can be both collaborative and compet-itive. Within a cluster, organizations' competitive and collaborative regional relationships assist them in global competition [START_REF] Porter | Location, competition, and economic development: Local clusters in a global economy[END_REF]. In value networks and in ecosystems, it is an explicit assumption that companies within work cooperatively and competitively [START_REF] Moore | The Death of Competition: Leadership and Strategy in the Age of Business Ecosystems[END_REF]. In terms of sources of transformation, competitive forces are central in the evolution of industry, population and cluster. Industry evolution is directed by several competitive forces [START_REF] Porter | The competitive advantage of nations[END_REF], the growth and demise of populations results from competition over access to limited resources such as membership, capital and legitimacy (Hannan & Freeman 1989), and the destiny of regions is an outcome of global competition [START_REF] Porter | The competitive advantage of nations[END_REF]. At the opposite end, the evolution of inter-organizational networks are seen to result from negotiations that are formally governed and goal directed [START_REF] Provan | Interorganizational Networks at the Network Level: A Review of the Empirical Literature on Whole Networks[END_REF]. In between are ecosystems -and value networks-, where transformation is seen as contingent upon new customer needs and/or new product and service innovations, leading to reforms in ecosystems [START_REF] Moore | The Death of Competition: Leadership and Strategy in the Age of Business Ecosystems[END_REF] with the distinction of value networks where reforms may be initiated in several parts of the network depending on the negotiated value [START_REF] Allee | The future of knowledge: Increasing prosperity through value networks[END_REF].

Overall, a strength of the business ecosystem concept is that it acknowledges both collaborative and competitive relationships. Hence, the concept enables simultaneous analysis transformation, both within networks but also in the markets where they operate. At the same time, however, the concept itself becomes more complex: Defining borders of an ecosystem is more difficult, because the relationships defining an ecosystem are manifold. Hence, the ecosystem lens can also lead to an overly complex view of reality, in particular if collaborative (or competitive) ties between companies are insignificant. Furthermore, business ecosystem concept provides the most opportunities to produce viable collective consciousness fields because many actors try to make sense of the larger system. However, on a conceptual level, an ecosystem draws on biology, as explained earlier, and consequently does not imply a term of collective consciousness. These consciousness fields focus attention and feed in mechanisms and opportunities for leveraging the complexity of the terrain. However, using the vocabulary of and drawing conceptually from natural sciences in social sciences may inhibits tapping the potential of the complexity present in e.g. the business cases presented above.

5

The case of an emerging business ecosystem for digital realestate and facility services

In order to delineate the similarities and differences between the different concepts described above, the present an initiative that is aimed to become a business ecosystem for digital real-estate and facilities services.

The concrete activities within initiative take place under a industry-academia research and development program that is funded by Business Finland -The Finnish Funding Agency for Technology as well as the companies and universities participating the program.

The purpose of the program is to develop end-user services using IoT, sensor technology, face recognition, artificial intelligence etc. as well as to identify potential use cases and develop business models for these services. The activities within the project are divided into four thematic entities, titled well-being, intelligent restaurant, data-as-services and empathetic building. The thematic entities are led by the companies participating the research program. Table 2. below contains a brief description of the network of participants.

Table 2. Description of the emerging business ecosystem

Dimension Description

Definition of group borders

Participating institutions, companies, and research s, and financing institutions.

Primary relationship between firms

Collaborative and competitive ties. In the beginning of the endeavor, the relationships are intense and loose.

Sources of transformation and change

The collective platforms of sharing information, which engage partners of the network to challenge the old models and adopt new ones. High quality collective consciousness fields and working methods.

Applicability

Explaining collaboration of diverse organizations and individuals with partly shared and competitive/diverse motives. Business ecosystem a benchmark and desired end state.

Ecosystem metaphor an important part of the discourse within the program participants.

When scrutinized through the different concepts used to describe business networks discussed in the prior sections, we see that certain concepts are more insightful to describe the emerging ecosystem for digital real-estate and facility services.

First, the industry concept does not apply directly very well, because the industry consortium comprises companies from both real estate and facility as well as ICT sectors. On the other hand, the industry concept has certain relevance in the early state of the program where the participating companies operate mostly based on the dominant logics of their respective industries.

The concepts highlighting the collaborate relationships between the players such as inter-organizational network, cluster and value-network have certain value in describing the relationships between the participants. The population concept in turn builds upon the assumption that the players homogenous which significantly limits the applicability of the population concept in our case.

The business ecosystem concept has certain fit to our case as the relationship between the players is essentially co-opetitive. On the other hand, at least in its current formative stage, there is no clearly dominant player that characterize an ecosystem.

As a result, we conclude that value network appears to have the best fit to our case in its present state. Value network does not assume the existence or emergence of a dominant player. The lack of a clear dominator may on one hand increase the need for additional negotiation and thus slow down the development activities. On the other hand, it forces the participants to articulate their needs and intentions and take responsibility of the course of action. Table 3. below provides a summary of the analysis.

Discussion

Compared to the related concepts investigated here -industry, population, inter-organizational network, cluster, and value network -the concept of business ecosystem appears to enable the analysis of both collaborative and competitive relationships. The need for the concept is often argued on the basis that economy and competition has changed and collaborative arrangements are becoming increasingly significant due to globalization and digitalization. Our analysis of the concepts suggests that all six concepts provide partially distinct perspective to and emphasis on business networks. We further pointed out the system complexity might be a relevant dimension to classify business networks. Using theoretical and conceptual tools that are can explain the research problem with a minimal complexity is generally considered a virtue in research. At the same time, however, overly simple tools and concepts are often insufficient in finding solutions for highly complex problems [START_REF] Boulding | General system theory -The skeleton of science[END_REF]. For example, inter-organizational collaboration generates different levels and qualities of attention [START_REF] Teece | Business models, business strategy and innovation[END_REF]) such as collective awareness and collective consciousness. This in turn, can accommodate dealing with highly complex levels issues and problems, including innovations, sustainability and ethics [START_REF] Turunen | Toward a Consciousness-Based View of Organizing[END_REF]. Therefore, managerial decision makers' tolerance to educate themselves on complexity may be worthwhile.

The same applies also to managers who are making strategic decisions. The key question in selecting a perspective is how significant collaborative arrangements are in a given industry. If collaborative arrangements are business critical, belonging to the right network(s) can make a difference. However, if barriers for leaving and joining ecosystems are low and multi-homing in several ecosystems in parallel is possible, the classical competitive industry perspective can be more valuable in strategic decisionmaking.

Our analysis implies that the ecosystem concept appears to fit particularly well to situations where there is a focal firm or platform leading the network [START_REF] Iansiti | Strategy as ecology[END_REF], Teece 2007[START_REF] Autio | Innovation ecosystems. The Oxford handbook of innovation management[END_REF]. This is the case with global players such as Alibaba, Apple and Amazon where the network consists of a very large number of actors and is being led and coordinated by a single leading firm (cf. [START_REF] Hyrynsalmi | The influence of developer multihoming on competition between software ecosystems[END_REF]). On the other hand, in the absence of a clear dominant player, tools and concepts such as value network that put emphasis on inter-organizational collaboration such as value network may offer more insightful descriptions. For instance, as stated by [START_REF] Allee | The future of knowledge: Increasing prosperity through value networks[END_REF], value network analysis can lead to profound shifts in perception of problem situations and mobilize collective action to implement change.

Like any other piece of research, this study suffers from a number of limitations. First, we focused only a limited set of concepts. Future research should thus incorporate e.g. platform and alliance in the analysis. Second, we have focused on business ecosystems on a general level. However, there are presumably different types of business ecosystems. Future studies could thus identify different types of business ecosystems.

Table 1 .

 1 Concepts depicting business networks

		Industry	Population Inter-	Cluster	Value net-	Business
				organiza-		work	ecosystem
				tional
				network
	Definition	Established	Homogeneity	Multiple or-	A geograph-	Exchange of	Loosely con-
	of group	and poten-	of organiza-	ganizations	ically proxi-	intangible as-	nected firms
	borders	tial competi-	tional forms;	linked	mate group;	sets between	who depend
		tors; firm	firm borders	through mul-	distance bor-	individuals,	on each other
		borders		tilateral ties;	der	groups and or-	for their mu-
				network bor-		ganization;	tual effective-
				ders		borders of ex-	ness and sur-
						perienced	vival; an entity
						value constel-	and a system
						lations of net-	with borders
						work
	Primary	Competi-	Competition;	Collaborative	Loose collab-	Competition	Competitive
	relation-	tion; includ-	among diverse	ties that facil-	orative ties	and collabora-	and collabora-
	ship be-	ing latent	organizations	itate reaching	within a re-	tion in value	tive ties
	tween	competition	forms	a common	gion that as-	creation
	firms			goal	sist in global
					competition
	Sources	Selection	Selection	Formally, es-	Selection of	System's ca-	New products
	of trans-	through	through com-	tablished	most viable	pacity to cre-	and customer
	formation	competition;	petition; com-	governance	regions	ate tangible	needs incorpo-
	and	large num-	petition for	processes be-	through	and intangible	rate the next
	change	ber of com-	limited re-	tween net-	global com-	value constel-	round of inno-
		petitive fac-	sources; 'The	work parties;	petition; 'The	lations beyond	vations; 'The
		tors; 'The	strongest ben-	'The best	strongest col-	product com-	dominant
		best fit	efiter wins.,	networker	labora-	ponents. Abil-	player attract-
		wins.'		wins.'	tor/adapter	ity to generate	ing contribu-
					wins.'	value from in-	tors, such as
						tangible re-	platform
						sources.	player wins.'
						'The best ne-
						gotiator wins'
	Applica-	Explaining	Explaining	Explaining	Explaining	Explaining	Explaining
	bility	success and	success and	evolution and	success of	success and	simultaneous
		viability of	viability of	success of in-	geographic	failures of	evolution/dis-
		individual	populations of	ter-organiza-	regions	companies and	ruption of
		companies	companies	tional net-		new products.	markets and
				works		Predictive	networks
						analysis.
	Existing	Games in-	Supercell's	Collaboration	Silicon Val-	Hard disk	Apple, Ama-
	Business	dustry	Clash of Clans	between	ley; Seattle	manufacturer	zon, Face-
	cases		or King's	game compa-	region	in 'Innovators	book. Alibaba
			Candy Crush	nies and		dilemma'.
			Saga	movie stu-
				dios

Table 3 .

 3 Applicability of different concepts to describe the emerging ecosystem for digital real-estate and facility services

	Concept	Applicability	Description
	Industry	Partial explanation power.	The program focuses on real estate and digital-
		Omits part of the system.	ization industries. The current stage of the af-
			fairs cannot be described accurately with the in-
			dustry concept.
	Population	Problems due to the ontological	Population does not apply to the Program ex-
		differences of the domains vs.	cept of strong and dominant players such as big
		biology and social sciences	firms or set up of coordination.
	Inter-	Leaves out the heterogeneity of	The players form a network and sub-networks.
	organizational	the actors i.e. organizations, for	However, there is no strong mutual dependency
	network	instance motives.	(at the early stage of the program).
	Cluster	Partial explanation power. Ge-	The constellation of the players involved has no
		ography is not relevant.	clear geographical dimension. The players rep-
			resent different businesses (real estate, ICT)
	Value network	Good explanation power. Does	The dynamics of a value networks are to a cer-
		not accrue in explaining the sys-	tain degree visible.
		tem.	Participants collaborate and negotiate of value
			constellation. Interactions in the value network
			create collective consciousness.
	Business ecosys-	Problems due to the ontological	Co-opetition between players. A dominant
	tem	differences of the domains vs.	player is missing.
		biology and social sciences