Skalierbare Softwaresysteme – Design, Betrieb und Optimierungspotenziale (German Edition)


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Hence, BYOD is the allowed subset of consumerization [2]. The increasing number of organizations that allow private IT and introduce BYOD programs leads to more and more employees that use the same device for both work and private life [8]. This behavior is termed dual use [9].

On the one hand, dual use can simplify communication as employees are able to work wherever and whenever necessary [8]. On the other hand, dual use of one device can have negative implications such as work overload, job related stress, and work life conflicts [8, 9]. Employees who do not want to expose themselves to these negative effects can instead use two separate devices for work and private life. In our study, we use the term multihoming to describe this behavior.

To add to existing research, we use multihoming as an analogy to examine how the preference for one or two devices influences the decision to take part in a BYOD program. To understand this decision process, our research question is: RQ: Which factors influence the preference to use just one device instead of separating work and private life by using two devices? To answer our RQ, we conducted a web-based survey with participants.

Based on a hypothetical scenario, respondents had to choose between two devices vs. We apply covariance based structural equation modelling CB-SEM to assess the effect of inconvenience of two devices, work life conflict concerns, perceived privacy risks and perceived financial risks on the two aforementioned tradeoffs. Validating previous qualitative research [2, 15, 16], our analysis implicates that the inconvenience of two devices drives employees to dual use one device.

Moreover, we find that privacy concerns drive employees to multihome.

The remainder of our paper is structured as follows. In Section 2, we introduce the related work. In Section 3, we develop our research model and hypotheses. In Section 4, we describe the research methodology. In Section 5, we present our data analysis and results. In Section 6, we conclude with a discussion and implications of our work. According to consumerization and BYOD literature, the relative performance advantage over a corporate device is one major reason for employees to use a privately owned device. As employees think that their private device is more useful or will increase their performance, they are more likely to use it for work [10 12, 14, 17].

Another reason to choose a privately owned device over a corporate device, can be more intuitive handling and better ease of use [10, 12]. Generally speaking, employees satisfaction decreases, if they have to use inferior technology for work [1, 2, 15]. Furthermore, the results of Dernbecher et al.

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In addition, related research suggests that early adopters and highly innovative employees rather use private IT for work in comparison to late adopters and laggards [12]. The same applies for employees with high degrees of self-efficacy [18]. On the contrary, concerns about possible risks while using private IT for work, are a potential inhibitor of BYOD and consumerization [13]. In the domain of economics multihoming is described as [ ] agents purchas[ing] two competing products in order to reap maximal network benefits [19].

Playing on different game platforms [20, 21] or holding different credit cards [20, 19] are popular examples for multihoming in the economics literature.

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Some games are released exclusively for one gaming platform; some shops only accept a certain credit card. One way to overcome these incompatibilities is to multihome. However, multihoming multiplies costs for initial purchases, operating, and managing effort [19]. We take multihoming as an analogy to analyze the reasons for one or two devices. While work and private life is incompatible for certain employees, multihoming is useful to strictly separate work and private life by using two distinct devices [9, 16].

In accordance with this analogy, qualitative consumerization literature shows that managing, carrying, and charging two separate devices is inconvenient and annoying [2, 15, 16].

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For those employees who find that work and private life is compatible, dual use of one device is a possibility to avoid additional effort and costs. However, dual use bears the risk of work interfering with private life, leading to a potential work life conflict [2, 9, 16]. This conflict in turn can induce job stress leading which intensifies the resistance to use only one device for both work and private life [8, 22, 23]. Dual use might also blur the boundaries between work and private life. This creates concerns that private information might be revealed to the employer, or private data may be lost, because the employer has to remotely wipe a device [13, 24].

On the other hand, the company also allows employees to bring their private device and use it for work BYOD. Although not every employee is faced with this many options in real life, we intentionally designed this scenario in a way that participants have to consider the two possible alternatives to using two separate devices.

Hence, we assessed participants decisions concerning the two following trade-offs: Multihoming against dual use of a company owned device Corp Multihoming against dual use of a private device Priv To this end, our research model has two distinct dependent variables. We operationalized both variables as a trade-off between multihoming and the respective dual use variant the corresponding scales are described in section 4. According to the theory of reasoned action [25] and the theory of planned behavior [26], intention is supposed [ ] to capture the motivational factors that influence a behavior [26].

In our study, we suggest ITU1D captures the factors that influence the decision to use one device instead of two. This is, amongst other things, because of time-consuming tasks such as synchronizing, charging, and carrying. Multihoming occurs usually because of insufficient compatibility or required redundancy [19, 27].

As users want to overcome incompatibilities and benefit from separate networks they have to multihome, leading to multiple investments and increasing effort [19]. We suggest the need for separation of work and private life is comparable to incompatibility. Consequently, using multiple devices for different purposes also causes an increase in maintenance and handling efforts.

Thus, we propose that employees that perceive high degrees of inconvenience in handling two devices INCON , will have the general intention to dual use. Moreover, they will rather decide in favor of one company or one private device over multihoming: H2 a - c. This especially affects employees that are concerned that dual use will make them neglect their family and friends [9, 16].

One strategy to strengthen the boundaries is to multihome, because employees are able to lay aside or turn of one of the devices [30]. In a study with participants, Yun et al.


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Using one device for both work and private life might entail that personal information such as location, photos, and s are revealed to the employer [24]. In addition, the loss of private As employers usually use mobile device management systems on both company and private devices, they are able to remotely wipe data on a smartphone in case of a compromised device [13]. Further, Lebek et al. Thus, we assume that perceived privacy risk PR negatively affects the decision to dual use one device for both work and private life. This is because employees expose their privately purchased device to additional risks when using it for work.

To test this relationship quantitatively, we hypothesize: H5. Research Model 4 Research Methodology 4. While initially began to fill out the survey, only completed it.

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We further deleted 18 observations of participants, who failed to answer an attention question. Table 1 displays the sociodemographic data of our study sample. In order to fulfill this job, you will need a smartphone to make phone calls, to write s or to browse the web. Your company offers you the following: You can use your own smartphone for work. If you do this, the company will pay your phone contract. You could also get a company smartphone for work and use it for private purposes. To control for the multitude of possible devices provided by an employer and possible brand preferences, participants were offered one out of four different smartphones as a hypothetical company smartphone.

Applying randomization, we presented participants an iphone 4 The devices represent the current models of the two market leaders at the time we conducted the survey and one model each from an older generation. Based on this scenario, participants had to decide whether they would dual use the company owned device, dual use their private device BYOD or multihome. We assessed participants decision using two distinct six-point scales. The first ranged from 1 I would use two separate smartphones to 6 I would only use my personal smartphone Priv.

The second ranged from 1 , I would use two separate smartphones to 6 I would only use the company smartphone Corp. In addition, we asked participants to rate the quality of their private device in comparison to the corresponding company smartphone on a sevenpoint scale from 1 Strictly worse to 7 Strictly better with 4 as My smartphone has the same quality. All latent constructs are measured on seven-point Likert scales using reflective items. Table 2 illustrates that the measures of the latent constructs exceed the recommended threshold of 0. Thus, we do not see any reliability issues.

Moreover, Table 2 shows that the latent constructs have sufficient convergent validity, as the average variance extracted AVE of all constructs is higher than 0. Discriminant validity was assessed using the Fornell-Larker criterion.

The results are listed in Table 2 and confirm that discriminant validity is not an issue, as the square root of the AVE of each construct exceeds the correlations with any other construct [37]. Table 2. Off diagonal elements are the correlations among latent constructs. Because all our survey data is self-reported, we controlled for common method bias using Harman s single factor test [38]. We conducted a principal factor analysis without rotation. The analysis extracted five factors, while the first factor accounts for As the analysis did extract a single factor and no general factor accounts for the majority of the variance, a common method bias is unlikely [38].

Our research model explains Thus, H2a, H2b, and H2c are supported by our results. We cannot H4b and H4c are not supported by our analysis. In addition, our results do not show a significant effect of FR on Priv. Thus, we cannot confirm H5. Table 3.

Skalierbare Softwaresysteme – Design, Betrieb und Optimierungspotenziale (German Edition) Skalierbare Softwaresysteme – Design, Betrieb und Optimierungspotenziale (German Edition)
Skalierbare Softwaresysteme – Design, Betrieb und Optimierungspotenziale (German Edition) Skalierbare Softwaresysteme – Design, Betrieb und Optimierungspotenziale (German Edition)
Skalierbare Softwaresysteme – Design, Betrieb und Optimierungspotenziale (German Edition) Skalierbare Softwaresysteme – Design, Betrieb und Optimierungspotenziale (German Edition)
Skalierbare Softwaresysteme – Design, Betrieb und Optimierungspotenziale (German Edition) Skalierbare Softwaresysteme – Design, Betrieb und Optimierungspotenziale (German Edition)
Skalierbare Softwaresysteme – Design, Betrieb und Optimierungspotenziale (German Edition) Skalierbare Softwaresysteme – Design, Betrieb und Optimierungspotenziale (German Edition)

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