First Person Shooter Games

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Chapter Introduction

Background

The gaming industry is a critical player in the entertainment and software sector. According to Li et al. (2018), a market research forecast asserted that by 2022 the global market of video games would increase up to 174 billion US dollars. As of March 2019, the retail revenue of the U.S. video game industry totalled 1.2 billion U.S. dollars (Statista 2019). Shooter games alone share a significant portion of the video game market sales, an example being, in 2016 shooter games held the top three positions of best-selling games in the United States (Lee and Chang 2018). Based on genre breakdown, shooter games composed 25.9 per cent of US sales in 2017, making it the most sold genre of the year (Statista 2018). Due to the ever-increasing interest in first-person-shooter (FPS) games, it has become imperative to improve the overall game experience for users, especially for video games that are Internet-based.

No longer are most games confined to being single-player games, especially shooter games. Before the massive proliferation of Internet-based multiplayer games, specific games were able to extend beyond single player confinement but always on a small level (Oliveira and Henderson 2003). This limited number of players is due to supporting architecture being peer-to-peer, relying on replication of the game on a database (Oliveira and Henderson 2003). With the evolution of multiplayer Internet-based games, architectures that are client-server based have become the norm and are commonly associated with mechanisms that are responsible for sharing the experience went from the client to the server (Oliveira and Henderson 2003).

First-person shooters (FPS) genres in the gaming industry encompass all genres that allow the user to experience a first-person perspective within a small environment. The primary objective of the game is to eliminate all other players in the environment while avoiding this outcome for the user themself (Lee and Chang 2017; Chen, Yang and Chen 2013; Oliveira and Henderson 2003). In such game genres, the user has the choice of roaming the environment on their own or being part of a team with the objective of eliminating the opposing team. This causes the game environment to be very fast-paced, with users having the expectations of having real-time interactivity (Oliveira and Henderson 2003).

This experience is often hindered by ‘lag’ from time to time caused by the Internet’s non-QoS-guaranteed architecture (Tseng et al. 2011). Therefore, developers and network engineers have been striving to improve the quality of experience (QoE) for players in terms of the performance and reliability of the game and its network systems. Although there have been many efforts made, the Interest still does not provide QoS with gamers ultimately suffering from lag. The definition of lag is often time subjective, as it varies based on the study. The current study uses the definition provided by Tseng et al. (2011, p. 1) who defines it as “a phenomenon when a game fails to respond to users’ commands or update the screen in a timely fashion.”

Many games attempt to provide lag compensation in order to reduce the impact of network latency or lag. However, players still suffer from specific amounts of lag in the game as it takes some time for the server to be notified with a message from the client (Lee and Chang 2018; 2017; Tseng et al. 2011). Thus, even if a player fires on time, their target may have already moved. That is why games provide lag compensation by having the server store historical information of every player that is on its servers playing (Lee and Chang 2018).

Hence, when a player that is shooting their target fires at another player, the server rolls back all the players in the server according to the latency of the shooter (Lee and Chang 2018). It is only through this method that the accuracy of the shot is measured using this game world. Unfortunately, this method is more in favour of the shooter than the victim, as the server bases its decision on the game world according to the shooter’s perspective when the shot is fired. This is known as “traditional lag compensation” (TLC) (Lee and Chang 2018). Traditional lag compensation is not fair enough to provide all players with a dynamic playing environment that ensures that the game environment is treated fairly by the servers. Therefore, it has become critical to find novel ways to provide players with lag compensation in Internet-based games, especially in the genre of a first-person shooter.

Problem Statement

As discussed in length in the previous section, traditional lag compensation presents an issue for less lagged players as they may be “shot behind corners” (SBC) by those players that are amply lagged. The phenomenon of SBC is existent in even AAA-rated games like Far Cry 5, Battlefield 4, Doom, and Destiny 2. Battlefield 4 had experienced the most extensive of gameplay issues on lag compensation since its launch in 2013 (DICE 2014). Ultimately this resulted in the developers stopping the development of future content (DICE 2014). According to Lee and Chang (2018), the incident faced in Battlefield 4 gave way to the occurrence of “conditional lag compensation” or CLC. This has been implemented in Battlefield games and its refrains the server from performing a lag compensation for players with a ping that is higher than 250ms (Lee and Chang 2017; 2018). This can be even lower if the player in the game is moving fast. Other games have followed suit such as Overwatch with a ping limit of 250ms and Call of Duty: Infinite Warfare with a ping limit of 500ms (Lee and Chang 2017; 2018).

However, ping limits are still an inadequate approach for lag compensation and lag in general. The method is inadequate as pings are different for different players over time, this causes it to be extremely difficult to approximate how much a shot should be led. Players that are in an FPS environment with a higher ping than the limit are not lag compensation and that is why they may need to aim ahead of their victims in order to hit time (Lee and Chang 2017; 2018). Hence, it has become critical to develop an advanced and more efficient lag compensation algorithm that enables fairness in the game for all players. In order to do so, it is imperative to obtain an understanding of how lag may be perceived and reacted by gamers to improve the quality of the experience.

Research Questions

In order to tackle the issue of game latency in first-person-shooter games, it is essential to gain insight into how lag is perceived by players and their reaction to it. This insight can provide the researcher with information that will aid in developing a lag compensation algorithm that ensures optimal levels of fairness. In order to conduct the research, the following research questions have been developed:

How do FPS players perceive lag?
How do FPS players react to a lag in the game environment?

The research questions are developed to tackle the core issues at hand while keeping in mind that player satisfaction is crucial in developing an algorithm that provides fair lag compensation. The research questions will act as the guidelines for conducting the research.

Research Aims & Objectives

The primary aim of the study is to uncover acceptable latency limits (lag limit) according to different first-person shooter game movement speeds. This will allow the researcher to build a multiplayer first-person shooter game that implements a lag compensation algorithm. The algorithm will be tested using different game movement speeds which are hypothesised to be a contributor factor in order to produce an acceptable lag limit. In order to achieve this, aim the following objectives have been developed to be used as guidance for the current research:

Examine the issues that FPS players face in a game environment when experiencing lag and lag compensation.
Analyse how different game movement speeds may impact lag compensation.
Investigate what can be considered an acceptable limit to latency based on varying game movement speeds.
Assess the perceptions and reactions of game players in first-person shooter environments with regards to lag and lag compensation.
Build a multiplayer first-person shooter game with a lag compensation algorithm that enables fairness in a gameplay environment.

The developed objectives are made with the primary aim of the research in view and ensuring that the current research adds to existing literature while also presenting a novel idea.

Significance of Research

The current research is significant on many levels. Firstly, the research looks to examine lag that is often found with Internet-based applications that use a client/server architecture. The research may uncover crucial information and provide solutions to issues that go beyond first-person shooter games and the gaming industry as a whole. The research is also significant because it seeks to provide a solution to a critical matter that impacts consumers of the gaming and entertainment industry. As addressed previously, the gaming and entertainment industry is a massive industry that has experienced increasing revenues with each year. Hence, this issue with game lag and lag compensation is an imperative factor that may impact the success of future games, particularly those in the first-person shooter genre.

The current research is unique in that it attempts to comprehend the perception of lag, and its reaction by players in the first-person shooter environment. It attempts to understand the level of satisfaction towards fairness using a lag compensation algorithm based on different movement speeds in the game. Previous studies such as Lee and Chang (2017; 2018) attempted to do so by implementing aspects of traditional lag compensation and neglecting conditional lag compensation. The current study builds on the ideas of Lee and Chang (2017) by implementing a different type of algorithm that takes into account the movement speeds of players in order to aid in lag compensation. Furthermore, the study also researches in-depth the perceptions that players of first-person shooters may have towards lag and lag compensation. The research is significant in that it builds on the principles of conditional lag compensation.

Dissertation Outline

Chapter 1; Introduction – the chapter provides insight into the purpose of the research as well as outlining the research questions, aims and objectives that will be used to obtain data, conduct an analysis and draw conclusions. The chapter provides sufficient background information and significance of the research which argues the need for the research in the first place.

Chapter 2; Literature Review – the chapter will provide an in-depth discussion on the available literature on lag compensation in first-person shooter games. The literature review will also divulge into current solutions that academics have developed to aid in the fairness of lag compensation and lag in general. The chapter will also provide context into the current research in terms of providing adequate premises to argue for improved lag compensation. The purpose of the chapter is to ensure that all published literature is reviewed which tackles the research questions.

Chapter 3; Methodology – the chapter will provide detailed processes that were used in gathering data and how that data will be analysed. The chapter will also provide a detailed discussion on how the lag compensation algorithm will be built concerning game speeds. The purpose of the chapter is to detail each of the steps taken in developing a research approach that can be replicated by other researchers in order to further knowledge of this issue.

Chapter 4; Analysis and Findings – the chapter will present the findings of the study; as conducted using the research approach from the previous chapter. The chapter will provide results in a detailed fashion in order to scrutinise the collected data in order to draw meaningful conclusions.

Chapter 5; Discussion and Conclusion – the chapter will be the final chapter of the research. It will summarise the entire thesis and provide conclusive arguments for the current research. The chapter will discuss if the research questions, aims and objectives were achieved. It will provide a discussion on the limitations of the research and recommendations for future studies to improve on the work presented in the current study.

Conclusion

Multiplayer allow players from different geographical locations to interact will one another in a small environment. With the adamant and revolution of the Internet, Internet-based games of grown with a majority of games now being played using a client/server architecture. A common issue found within these games is the network transmission delays which hinder the game experience. In first-person shooter games, which is often the cause for distress in many players, players experience a game lag which results in lag compensation. The chapter has detailed the issues of lag and lag compensation and the need to provide an adequate solution. Therefore, research will be conducted to obtain a better understanding of lag compensation in first-person shooter games in order to devise an algorithm that ensures lag compensation on a fair basis. The chapter also provides the research questions, aim, and objective that will be tackled in the current research.

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Chapter - Literature Review

Introduction

The literature review chapter provides an in-depth study of available resources on the subject area. The purpose of the chapter is to collect and analyse available academic literature on lag compensation for online gaming, specifically first-person-shooter games. In addition, the literature chapter of the current study will analyse published works pertaining to the aim of the research (Tseng et al. 2011, p. 1–6). To conduct the literature review, a thematic approach was implemented in which concepts and themes that are relevant to the research with regards to latency found in Internet games were examined. By assessing the plethora of literature, the present research was able to develop a theoretical basis to formulate research methods that will be presented in the succeeding chapter. The material for literature review was selected based on the following inclusion criteria:

Academic research articles that study latency/lag occurrence and compensation methods used on Internet gaming (i.e. client/server architecture).
The literature needs to be based on the gaming and entertainment industry, without geographic location limits.
The research material included was published between the years 2005 and 2019 for analysis.
White papers and industry/company publications were included in the analysis of present research as they provide industry-specific knowledge that may not be available in academic papers.

The structure of the literature review is divided into sections based on the major theme of the study. The academic literature presented was reviewed for its implementation of research methods for acquiring data, knowledge on the subject area, and key findings. For the present study, it was imperative to utilise research papers that overlapped into the computer sciences field in order to identify research that presented gaps in the research or contributed to the expanding body of knowledge.

Overview of Lag and Lag Compensation

Research has focused greatly on the impact of various influence factors on the quality of a game environment which includes system, context, and human factors (Amiri et al. 2016, p. 71). The current study looks to analyse such factors, particularly system influence factors that are required in cloud gaming and Internet gaming in general which demands a high level of quality service on the network. To ensure the quality of service it is imperative to examine factors such as packet loss and delay as it greatly impacts the perceived quality by players in the gaming environment (Cai et al. 2016, p.7618). The present section of the literature review focuses on latency, also termed as response delay (RD) or lag, for game genres, especially first-person-shooter games (Wen and Hsiao 2014, p. 4).

Gaming in general is naturally very interactive which makes it necessary to meet the challenge of delay requirements. This means delivering user control inputs (i.e. keyboard strokes, and mouse movement) to the game server and uninterrupted presentation of gaming content to the players in the environment (i.e. constant video stream). Brun et al. (2006, p. 1200) have found that conventional methods for decreasing the effects of poor network conditions and constant jitter cannot be applied in cloud gaming (Bernier 2001, p. 13). Huang et al. (2014, p. 10:1–15) find that lag compensation techniques found in traditional gaming like client-side prediction do not apply in cloud gaming. This is because in cloud gaming the client is decoding and rendering the stream that it has gotten from the server. Various studies have researched the impact of lag caused by heterogeneous and capricious network conditions on the end-user in cloud gaming.

Silvar et al. (2014), Clincy et al. (2013), Jarschel et al. (2013) have reported that delay and packet loss have a substantial effect on cloud gaming quality of experience. It is often caused by network congestion stemming from network jitter delay and size of queue exceeding resulting in packet loss (Amiri et al. 2016, p. 403). Such an experience from delay and jitter effect the uplink time between the player and the server when sending input events, while downlink is also affected in terms of transmission of game scenes that do eventually become displayed on the screen (Beyer et al. 2015, p. 1–2). Academics such as Claypool and Claypool (2006, p. 43) have shown that high network delay disrupts the interaction between the players and server which results in a negative experience for players.

It is imperative to consider that not all cloud games or traditional networked games are delicate to latency (Huang et al. 2014, p. 10:1–13). Cai et al. (2016, p. 7617) have noted that real-time strategy (RTS) games are unaffected by lag as high as 1000 ms. But Li et al. (2018, p. 113) contend that first-person-shooter games are more prone to lag with delays that can reach over 100ms which are considered unacceptable as users are shooting at moving targets.

The effects of lag are known to be based on two action characteristics; mainly precision and deadline. Li et al. (2018) defined precision as the accuracy of the actions when the deadline is defined as timeliness of the events taking place. Hence, games that have higher and closer deadlines are considered to be more prone to latency. This is the reason that Li et al. (2018) argues that first-person-shooter players demand greater emphasis to be placed on deadline and precision.

The client-server relationship in cloud gaming is defined by the client sending control events to the server over their network, with the server then executing the input commands and delivering a stream in the form of audio or visual back to the client (Lee and Chang 2017, p. 3). In the end, the client receives and decodes the output which is then portrayed on the screen. The entire process of a round trip delay is termed as response delay (Amiri et al. 2016, p. 73). The insertion of lag compensation in this process is a technique that tries to equalise the lag received for all players in a cloud gaming environment (Jarschel et al. 2011, p. 334).

Sources of Latency/Lag in Multiplayer Network Games

Latency or lag is thought to be inevitable in multiplayer games as it takes time for the data packets to travel between clients and its server, while also taking the journey vice versa (Lee and Chang 2015, p. 1). In order to minimise the impact on the game experience, several techniques are used even though they may result in more inconsistencies or new sources of lag. However, several authors Claypool and Claypool (2006) have noticed that several games have the tendency to perform better with lag compensation processes aside from these inconsistences. Notwithstanding, such evidence is scarce to make such an assessment as there is a lack in research of statistical comparisons for player performance with and without the inclusion of lag compensation methods.

Due to the complexity of the phenomenon, it is essential to comprehend the sources of lag that gamers may face when playing multiplayer games. One such source is known as tick rate; it determines the frequency of the game server to process incoming information, update the game state, and send updates to clients. Lee and Chang (2015) noted that tick rates among games are different based on the game genre and complexities in the game. It is found that a low tick rate in first-person-shooter games is known to contribute to various issues including incorrect damage registration, kill trading, and one-hit-kill bugs (Lee and Chang 2015, p. 11:1–11:3).

Kill trading is defined as two players firing at each other with both ending up dying even when one is already dead but still shoots at the other due to lag (Chen, Yang and Chen 2013, p. 4:1–4:6). One-hit kill bugs are defined as a player being killed by only one shot which is not a headshot (Li et al. 2018, p. 105). In the gaming community, low tick rates in first-person-shooter games are nicknamed netcode (Lee and Chang 2015, p. 1). Lee and Chang (2015) assert that Battlefields 3 and 4 have very low tick rates at launch, many gaming communities are divided on the influence of high tick rates. Vertical Synchronisation is also known to be a source of latency. It ensures that only one complete frame gets displayed at a certain time, by making the graphic progressing unit (GPU) wait for the monitor to finish the current refresh cycle before executing the next frame (Lee and Chang 2018, p. 285).

However, this may cause it to take longer than one refresh cycle for the GPU to execute the frame causing the last frame to appear again on the next refresh cycle because the monitor is waiting for the GPU; thus an input lag is created (Savery and Graham 2013, p. 271–287). This lag is created through vertical synchronisation which was introduced to aid in display rendering. Normally, to display the game to players a GPU executes the frames at a dynamic rate which has the tendency to fluctuate according to its ability and the complexity of the game world (Bernier 2001, p. 13). It should be noted that the refresh rate of a monitor is fixed (Tseng et al. 2011, p. 1–6). In instances when the GPU’s rendering rate is higher than the monitor’s refresh rate, various portions of two or more frames may be displayed simultaneously which causes screen tearing, to fix this issue vertical synchronisation was introduced, but as discussed causes its own sort of lag (Claypool and Finkel 2014, p. 3).

To counter these sources of lag various methods have been employed in the multiplayer game-verse. Lag compensation has been discussed extensively in the previous subsection. Another countermeasure that is used for latency is interpolation and extrapolation. Without the inclusion of interpolation, gamers may experience the view of a moving entity in one position for several frames before it is seen to teleport itself to a new location (Lee and Chang 2018, p. 286). When a player fires in such circumstances results in a miss. The process of interpolation uses two snapshots as opposed to one to interpolate entities’ positions and game animations between them (Clincy and Wilgor 2013, p. 473–476). Lee and Chang (2015, p.2) believe that interpolation in first-person-shooter games increases shooting accuracy.

Previous Research in Lag in Multiplayer Networked Games

There is abundant literature present that tackles the research of lag in multiplayer networked games. Academic research into the topic has continuously reiterated that first-person-shooter games are a genre that is least tolerant of latency in the game environment. According to academics such as Claypool and Claypool (2006, p. 43) overall lag for Internet connections can be diverse from hundreds of milliseconds to one second. It was asserted in their research that not all player actions are indiscriminately sensitive to lag, instead, it depends greatly on the game itself and its genre (Claypool and Claypool 2006).

Claypool and Claypool (2006, p. 44) went on to document and categorise different player actions based on the precision and time that is needed to complete the action. It was concluded from the research that games with a first-person perspective (FPP), like FPS and racing games, are more sensitive to the influence of lag. While games under the genre of role-playing games (PRG) and sports have medium lag sensitivity, and real-time strategy games and simulation games have the lowest effect from lag. The research by Claypool and Claypool (2006) also brought forth the idea that a lag of 100ms significantly decreases the hit accuracy in first-person-shooter games.

Other academics like Brun et al. (2006, p. 1201) also agree with the assertion that first-person-shooter games have the highest responsiveness and consistency requirements compared to others. Other academics believe that lag causes extensive issues for network-based multiplayer games (Zander, Leeder and Armitage 2005, p. 117–124). Pantel and Wolf (2002, p. 27) had used 12 participants in their research to determine the acceptable presentation of delay in racing games. The results of the study show that such games are able to tolerate up to 50ms of delay but they need to avoid a delay of 100ms. It was hypothesised that a presentation delay of 100ms or more is acceptable for first-person-shooter games.

Various studies in this field have focused on first-person-shooter games. A unique study for this topic was Armitage (2003, p. 137–141) who hosted public servers for the game Quake III in order to record the lag information of each of the players. The study had found that the lag tolerance of 9,522 players was approximately 150ms to 180ms. A similar method was employed by Henderson (2001, p. 7) who hosted a public server for Half-Life and studied player sensitivities from observed player behaviour. It was estimated that the study included 16969 players and observed that players had the tendency to stop playing on the server when their latency had exceeded anywhere from 225ms to 250ms.

However, Armitage and Zander (2004, p. 8) argued that there is a challenge in using public servers to access the effects of lag on the experienced quality as it is very difficult to obtain opinions from players. Studies such as those conducted by Beigbeder et al. (2004, p. 150) ran over 200 experiments, with four players each, using the Unreal Tournament 2003. The results of the study showed that hit accuracy, in addition to the number of kills and deaths had decreased when latency had reached 100ms. Quax et al. (2004, p. 154) used 12 players to participate in varying scenarios of the same game, the scenarios simulated a combination of varying jitter and delay. Researchers then recorded the number of kills and deaths of each player, players were also asked to rate the network quality and its effect on their performance after each scenario. The results of the study show that a delay as low as 60ms is considered to be enough to impact each player’s performance (Quax et al. 2004, p. 152–156).

A study conducted by Kaiser et al. (2009) did not involve human participants and attempted to correlate lag and jitter with player experience using bots. In the study, four bots mimicked the behaviour of real users who were connected to a dedicated server and played a series of matches in Quake III. The performance metric used in the research was the time between kills. It was found that a lag of 50ms is enough to degrade player performance (Kaiser et al. 2009, p. 5).

Armitage and Zander (2004, p. 8) had also used Quake III with Halo to conduct a usability trial. The researchers had six Quake III players and eight Halo players participate in four different trials, each of the players had randomly chosen delay and packet loss. The researchers then asked the players to rate the perceived quality and state of whether the players would stay or leave the game under the conditions (Zander, 2004). It was found that the perceived quality of the game experience drops from good at 300ms delay for Quake III, and 200ms for Halo.

Conclusion

The current chapter analyses the various studies that have been found in the literature to examine latency in multiplayer network games. The chapter discusses the various aspects involved in sources of lag and techniques for lag compensation. The literature review has found that extensive studies have been conducted research on first-person-shooter games to better understand the ping rate at which satisfaction in the game environment is felt by players. The chapter discusses the methodology process of the previous research. It is found that a great deal of research on this phenomenon is conducted on public servers. However, researchers have found that using public servers makes it difficult to obtain opinions of players in terms of their satisfaction or other factors that have influenced the quality of the game. The literature studied was thematically reviewed in order to provide the present research with updated information and categorise the themes of each study to better comprehend the circumstances of lag and its compensation.

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Research Methodology

Introduction

The methodology chapter provides an in-depth discussion of the research methods that are employed in the current study. The purpose of the current chapter is to detail the major steps taken in developing the research method of the current study which then can be replicated by other researchers. The chapter discusses aspects of the methods employed from philosophical assumptions to techniques used for data collection and analysis. The chapter also provides a review of research methods design appropriateness, a discussion of the population being studied and its sample included in the current study.

The methodology chapter’s primary concern is with providing a logical explanation using rigorous arguments that infers that a reasonable and coherent method for research has been implemented. According to Haswell and DaSilva (1982), the methodology of research is primarily concerned with the ‘logic of justification’. Hammersley (2010) further adds that the choice of methods implemented in research should be based on the goals and circumstances of the research that is being pursued. Research methods are also derived from philosophical and methodological commitments that are also discussed in detail in the current. The research methodology discussed us guided by the following procedures as illustrated by Fig. 3.1.

Research Philosophy

The current study’s research design is based on positivism philosophy. Such an epistemological belief focuses on objective knowledge that can be acquired through methods that focus on collecting empirical data. Positivism recognizes only that which can be verified scientifically or is capable of logical proof while rejecting subjective proofs. Levin (1988) found that positivists hold a belief that reality is stable; because it is stable it can be observed and described using an objective perspective without directly interfering with the phenomena that are being researched. Hirschhiem (1985) provided an agreement to the ideas of Levin (1988) by asserting that the specific research phenomena should be isolated with any observations made being repeatable. The process involved the manipulation of reality by varying a single independent variable in order to identify symmetries within and to form relationships between constructive elements. Alavi and Carlson (1992) using the revision of 902 IS research articles concluded that all empirical studies were indeed positivist in their research approach.

The overriding concern in the current research is that it should be relevant to the research question, as developed in chapter one, and rigorous in its definition of variables into measurable factors. Keeping in view the current discussion, it is obvious that the positivist philosophy will fulfil the requirements laid out in the current study i.e. understanding the imperative factors that impact player perceptions of lag which in turn may impact their satisfaction of gaming. The various other elements involved in the research approach are discussed at length in the following sections.

Research Strategy

There are a vast number of methodologies identified in the literature and extensively discussed by Galliers (1991). Galliers (1991) developed a taxonomy of research methodologies reporting that positivist research includes one or a combination of laboratory experiments, field experiments, surveys, case studies, theorem proofs, simulation, and forecasting. Keeping in view the requirements of the current study, surveys will be the most suitable strategy in conducting research. Surveys allow researchers to obtain data about viewpoints, situations and practice at a certain point in time using questionnaires and even interviews.

Implementing quantitative analytical techniques then allow researchers to make inferences from the data based on existing relationships. Lam et al. (2001) point out that the use of surveys allows a researcher to study a plethora of variables at one time which is often not possible in laboratory or field experiments. On the other hand, Jaselskis and Recarte Suazo (1994) argues that surveys have a key weakness – it becomes difficult to recognize insights that are related to the causes or processes that are involved in the measured phenomena. There is also the issue of several source biases like an innate nature of respondents to be prone to self-reflection. This is caused by the point in time a survey is conducted and by the researcher through their specific choice of designing the survey.

Research Design

The current study is based completely on a quantitative research design which is derived from its philosophical foundation and requirements of research. Kuhn (2012) argued that the quantitative research approach allows a researcher to interpret observations for the sole purpose of uncovering meanings and patterns of a relationship which further allows for the classification of the phenomena. The nature of the design of the current study is best described as a descriptive design. Within quantitative research, the descriptive design looks to describe the current status of a variable phenomenon with data collection being observational in nature. The current study embodies a cross-sectional design which stems from its use of a survey questionnaire for data collection.

Data Collection

Population Sample

Development of the research method used in the current study entails examining traits or parameters of gamers that may experience a lag in a closed environment. The population used in the current study is a group of individual units that entail some form of commonality that is useful to the study. The current study uses a representative sample of the population to draw data from in order for the results of the study to be generalized to the population as a whole. The population from which the present study draws upon is students from Abertay University that are avid gamers. As of 2017, the student population of Abertay totalled 3,824 (Abertay, 2017). Of the total population, a total of 10 (N=10) students were randomly selected from the student populace to join in the present research. Only ten students were required for the present study after analysing the requirements of the research. The sample size range is recommended and used in Maqsoom et al. (2017), Basir (2015), and Bageis and Fortune (2009). All ten of the students were invited to take part in the multi-player first-person shooter game which was free for all.

Research Instrument

Game Programme

The game selected to be used for the current study is free for all designed by the researcher. It should be noted that no teams were allowed during game time, in which all players were asked to play for themselves. The project structure of the game environment used a classic first-person shooter game with a server and a client application. For the research to take place according to the objectives of the study the server application is an authoritative server that takes full control over the game in order to disable the ability of players to cheat. The client used in the current study is a unity game with an embedded server. The following set-up was used for the client which includes steps of;

Creating a new Unity project
Project Settings under Player, the scripting runtime version is set to .Net 4. x equivalent.
Time set is put to Fixed Timestep of 0.024 to 100
Download of game
Creation of a basic folder structure
Creation of main scenes under folder.

The server was set up also in the same manner as described above. The next step was to create basic setups of client basics and login systems. It began with implementing a function to connect to a server and send a simple login request. This process included the creation of “GlobalManagers” and “GlobalScripts”. Then a Unity Client component was added to the game object with the values of the Unity Client being set as illustrated in the image below.

[INSERT IMAGE- Unity Client Script]

Next came the step of implementing the GlobalManager. In order to make the Global Manager accessible worldwide and present in all the scenes the following code was added;

[INSERT IMAGE OF CODE GLOBAL MANAGER HERE]

[INSERT THE BASICS OF CODING, WHICH INCLUDES YOUR CLIENT-SERVER DESIGN AND MORE INFORMATION ON THE GAME AS IT WAS UNAVAIABLE]

Questionnaire

The current study based on its philosophical foundations that justify an empirical research strategy uses a survey as a data collection tool for carrying out the study’s research. A survey is defined as a “means for gathering information about the characteristics, actions, or opinions of a large group of people” (Pinsonneault and Kraemer, 1993, p. 77). Salant & Dillman (1994) have argued that surveys can be used to assess needs, evaluate demands, and examine impacts. The term itself is used in various ways but most generally refer to the selection of a relatively large sample of people from a pre-defined population of interest followed by the collection of small amounts of data from these individuals. The information obtained from a sample is then used to make inferences about the wider population (Kelley, Clark, Brown, and Sitzia, 2003).

Denscombe (1998) argues surveys are designed to provide a ‘snapshot of how things are at a specific time’. Kelley et al. (2003) note that no attempt is made to control conditions or manipulate variables; with surveys not assigning participants into groups. Surveys are best suitable within descriptive studies but can also be incorporated into exploring facets of conditions, or looking for explanations and providing data for testing a set of hypotheses. Denscombe (1998) asserts that it is important for researchers to recognize that the survey approach is a research strategy and not a research method. Kelley et al. (2003) highlight in their study the various advantages and disadvantages of using surveys in research which are illustrated in the table below.

Table 3‑1- Advantages and Disadvantages of Survey Research Approach (Source; Kelley et al., 2003)

Evaluation of Survey Research
Advantages	Disadvantages
1. The data is empirical in that it produces data from real-world observations.	1. Possibility of neglecting the significance of data if the researcher focuses a great deal on the range of coverage in that it excludes an adequate account of implications of specific data sets that may be relevant to the problem or theories.
2. The depth of coverage of numerous individuals or circumstances means that it is more likely than other approaches to obtain data of a representative sample and as a result can be generalized to the wider population.	2. Produced data lacks specific details or breadth of the topic that is being investigated.
3. The strategy produces a large amount of data in a short amount of time and is relatively low cost. Hence, researchers with a short amount of time can use the strategy which assists in planning and delivering strong end results.	3. It is difficult to control acquiring a high response rate to a survey especially when it is conducted through the post, face-to-face, and over the telephone.

The International Organization for Standardization (2012) indicates that research findings can be impacted by their wording, question order, and other aspects of the questionnaire design. Hence, it becomes extremely imperative to follow guidelines to develop a questionnaire that is sound and contain questions that are specifically constructed to accomplish research objectives. The present study uses a questionnaire designed by Lee and Chang (2017). The questionnaire (Appendix I) is divided into four sections:

Section 1: Player Demographics
Section 2: Perceptions of Lag
Section 3: Reactions to Lag
Section 4: Solutions to Lag

The sections are multiple-choice questions in which the participating individuals are asked to tick off one answer unless otherwise instructed. The players were asked to fill out their individual questionnaires after they had participated in the multi-shooter game that was developed by the researchers. The purpose of conducting the questionnaire was to better comprehend the various issues or factors that impacted the participant in their perception of lag within a first-person shooter game.

Data Analysis

Reliability Analysis

The data analysis procedures implemented on the obtained data first begin with a reliability and normality test. Reliability of items is necessary for evaluating assessments and questionnaires (Tavakol and Dennick 2011). It is often thought of as mandatory in research to estimate the quantity of alpha in order to increase the validity and accuracy of interpretations of results for a given data set. Researchers such as Gliem and Gliem (2003) have asserted the importance of measuring Cronbach’s alpha, especially when using Likert scales, which is implemented in the current study. DeVeillis (2012), Georgy and Mallery (2003), and Kline (2000) have provided what are known as ‘rules of thumb’ when interpreting alpha’ and checking for internal consistency; α ≥ 0.9 – excellent; 0.9 ≥ α ≥ 0.8- good; 0.8 ≥ α ≥ 0.7- acceptable; 0.7 ≥ α ≥ 0.6 questionable; 0.6 ≥ α ≥ 0.5- poor; and 0.5 ≥ α- unacceptable. Normality testing is conducted by analyzing skewness and kurtosis.

Normality Analysis

Like the reliability test of Cronbach’s alpha, normality testing is also conducted using IBM’s SPSS statistical package tool. Among the vast majority of normality tests, skewness and kurtosis (K-S) testing are the most commonly used. Elliott (2007) describes the procedure for conducting K-S through SPSS “explore” procedure: Analyze → Descriptive Statistics → Explore → Plots → Normality plots with tests. The purpose of the K-S analysis is to compare scores in a sample to a normally distributed set of scores that have the same mean and standard deviation (Ghasemi and Zahediasl, 2012). The null hypothesis often quoted in these tests is simply that the “sample distribution is normal” (Oztuna, Elhan, and Tuccar, 2006). Under the circumstances that the test is significant, the distribution is non-normal (Ghasemi and Zahediasl, 2012; Oztuna et al., 2006).

Relative Importance Index

The current study will also rank criteria of lag perception for the present study’s participants using the Relative Importance Index (RII). According to Johnson and LeBrton (2004), RII is helpful in finding the contribution of specific variables to an entire system or phenomena. In order to empirically ascertain the factors that contribute to the perceptions of lag/latency and its compensation in first-person shooter games, the Relative Importance Index (RII) will be used. The Relative Importance Index is a class of relative importance analyses. Johnson and LeBreton (2004) argue that RII is used for finding the contribution of a specific variable makes to the prediction of a requirement variable on its own and in combination with other predictor variables.

The relative importance Index is calculated by the following;

Somiah et al. (2015) explain that; is the weighting given to each risk factor by the respondents and ranges from 1 to 5; is the higher response integer being 5, and is the total number of respondents.

Conclusion

The chapter provides an in-depth discussion of the specific methods used in formulating the research methodology of the current study. To summarize, the current study is of quantitative design derived from the epistemological foundations of positivism. Positivists argue for the use of objectivity in research. Thus, empirical strategies are used to develop premises for some observed phenomena. The current study uses a sample of 10 respondents randomly selected through Abertay University. Furthermore, a questionnaire was used as developed by Lee and Chang (2015) which identified 33 constructs that may influence the perception of players with regards to in-game latency. The questionnaire was tested on a sample of 10 respondents after they had experienced a free for a first-person shooter game. The current study analyzes the collected data through a step-by-step statistical procedure being with testing research instrument reliability through Cronbach’s Alpha. Further tests include testing for normality through K-S and finally through the Relative Importance Index (RII). The following chapter will present the findings of the study through implementing the research methods discussed above.

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Chapter Analysis and Findings

Introduction

The chapter presents the results of the current study through the implementation of the methodology, discussed in the preceding chapter. The findings are organized and presented in the current chapter by first discussing the demographic results from the questionnaire followed by a discussion of the internal consistency of the data and lastly the Relative Importance Index (RII) The data was analyzed using these various techniques as described in chapter three. The purpose of the current chapter is to present the result of the statistical analysis from the acquired data in order to begin developing premises that would later be used in the final chapter.

Demographic Results

Response Rate

As previously discussed in chapter 3, a total of 10 participants were invited from the student body of Abertay University. These students were asked to participate in a free for all game play and then required to answer questions from a questionnaire. However, only eight of the participants were able to participate in game play and answer the questionnaire. Therefore, a total of eight participants were included in the present study.

Respondent Characteristics

Each of the respondents was presented with questions that asked to describe characteristics of themselves, these questions compose a portion of the demographic characteristics of the present study. Respondents were asked to indicate their gender and age group. The data is presented in table 4-1. The table indicates that 62.5 per cent of the sample were males while 37.5 per cent of the samples were female. Furthermore, a major age group that was included in the study according to the demographic results is between the ages of 18 to 28 years old, with 75 per cent of respondents indicating as such.

Table 4‑1: Participants Demographic Results

Participants Demographic Results
		Frequency	Percentage (%)
Gender	Male	5	62.5
Gender	Female	3	37.5
Age	18-28 years	6	75.0
	29-30 years	1	12.5
	30+ years	1	12.5
Single-Player Game Experience	3-5 years	2	25.0
	5-10 years	1	12.5
	10+ years	5	62.5
Online FPS and MMORPGs Game Experience	0-1 years	2	25.0
	1-3 years	1	12.5
	3-5 years	1	12.5
	5-10 years	1	12.5
	10+ years	3	37.5
Online Game Preferences	role play	4	17.4
	FPS	6	26.1
	RTS	8	34.8
	Causal Games	5	21.7
Frequency of Game Play	Rarely	1	12.5
	Intermittently	1	12.5
	0-2 hours a day	1	12.5
	3-5 hours a day	3	37.5
	6+ hours a day	2	25.0
Game Play Time	before 9 AM	1	12.5
	2 PM-6 PM	2	25.0
	after 10 PM	5	62.5

Respondents in the study were also asked to indicate their characteristics of themselves with regards to being an avid game. This included indicating how many years they have spent playing single player online. The results of the demographic analysis show that 62.5 per cent of the current sample had more than ten years of experience in playing single-player games. In addition to this, 25 per cent of the sample indicate that they spent three to five years playing single-player games.

While only 12.5 per cent have indicated that their single-player game experience is between five and ten years. Respondents were asked to indicate their experience of playing online games such as first-person shooter and MMORPGs. The sample indicated that 37.5 per cent of participants had over ten years of experience playing online games such as FPS and MMORPGs. Only 25 per cent of the participants indicated that they had one or less than one year of playing online games while 12.5 per cent indicated 1 to 3 years’ experience, 12.5 per cent indicated 3 to 5 years of experience, and 12.5 per cent of participants indicated 5 to 10 years of experience.

Respondents were also asked to indicate their preference of age genre in order to better comprehend the sample. Respondents were allowed to choose multiple answers to better characterize their preferences in question five. The results of the question indicate that 34.8 per cent of the sample prefers real-time-strategy games, while 26.1 per cent preferred FPS games, 21.7 per cent preferred casual games, and 17.4 per cent enjoyed role-playing games. Another key characteristic of the demographic is that 37.5 per cent of the sample indicated that it played games at a frequency of 3 to 5 hours a day, while 62.5 per cent of the population indicated that it prefers to play games after 10 PM.

Respondents Knowledge of Gaming and Internet Usage

A second portion of the demographic questions section asked respondents to indicate answers of their internet which are used to play games online. The results of these questions are illustrated in table 4-2. Respondents were asked to indicate their internet connection that is used to play online games. 25 per cent of respondents indicated that they use ADSL, while other connections of Cable Modem, FTTB, and 3G were equally distributed amongst respondents, indicated in the table with percentages being 12.5. Respondents also indicated what gateway connected is used by their computer with 62. 5 per cent indicating that an Ethernet is used. For the gateway connection to the internet, 50.0 per cent of respondents indicated that FTTB was used. A majority of respondents (37.5%) indicated that they have an upload/download bandwidth of 20Mbps/2Mbps.

Table 4‑2- Knowledge of Internet and Game Environment

Participant Internet Usage/Game Environment Knowledge
		Frequency	Percentage (%)
Internet Connection	ADSL	2	25.0
	Cable Modem	1	12.5
	FTTB	1	12.5
	3G	1	12.5
	Not Aware	1	12.5
	Others	2	25.0
CPU Gateway Connection	Ethernet	5	62.5
	WiFi	2	25.0
	Not Aware	1	12.5
Gateway Connection to Internet	ADSL	1	12.5
	Cable Modem	1	12.5
	FTTB	4	50.0
	Not Aware	2	25.0
Upload/Download Bandwidth	8 Mbps / 640 Kbps	1	12.5
	3 Mbps / 768 Kbps	1	12.5
	20 Mbps / 2 Mbps	3	37.5
	Not Aware	1	12.5
	Others	2	25.0

Reliability Analysis

Cronbach’s alpha (α) was used as a measure to assess the reliability of the questionnaire’s scale items. The purpose of using Cronbach’s alpha was to measure the extent to which measures in the questionnaire are consistent in measuring its concepts. Each of the themes measured in the questionnaire included: perceptions of lag, reactions to lag, and solutions to lag. The resulting α coefficients fall between a range of 0 and 1 which provides the overall assessment of the measure’s reliability. With high covariance among the items α will begin to approach 1, the higher the α coefficient the more it is concluded that the items have a shared covariance and most like measure the same underlying concept. The α measured for each of the set of items in the questionnaire were separately tested for α coefficients using IBM SPSS v.24.

The reliability statistics produced for each of the set of items in the questionnaire are presented in the table

Table 4‑3: Results of Cronbach's Alpha

Reliability Statistics
Scale	Cronbach's Alpha	Cronbach's Alpha Based on Standardized Items	N of Items
Perceptions of Lag	.836	.842	9
Reactions to Lag	.770	.773	7
Solutions to Lag	.804	.802	5

The scale of perceptions of lag produces an α coefficient of 0.836 which is categorized as being ‘good’ according to Georgy and Mallery (2003) who stated that 0.9 ≥ α ≥ 0.8 is considered good. Reactions to the lag scale produced an α of 0.770 which is placed in the ‘acceptable’ category based on the rules of thumb asserted by DeVeillis (2012), Georgy and Mallery (2003), and Line (2000). The scale items of solutions to lag had produced an α of 0.804 which is again placed in the ‘good’ category of α. However other notable studies like Gliem and Gliem (2003), Tayakol and Dennick (2011), and DeVeillis (2012) have attributed it to be acceptable under certain conditions. One of the most probable reasons for a slighter α value for interpersonal conflict can be attributed to the few amount of items that composed the section of the questionnaire.

Normality Analysis

The data collected from the questionnaire was then analyzed for normality. In order to test for normality, the following hypotheses were developed:

H₀: The sampled population is normally distributed.

H₁: The sampled population is not normally distributed.

To test these hypotheses, the one-sample Kolmogorov-Smirnov (K-S) test for normality was conducted using IBM’s SPSS. The results of the test are displayed in the following tables for each of the measure items. These measures included perceptions of lag, reactions to lag, and solutions to lag. The results show that the population is normally distributed as shown by the values produced by the K-S statistic and the significance value being greater than 0.05 (Sig.>0.05). In a normal distribution, the significance value must be greater than 0.05.

Hence, it can be concluded that there is not enough evidence to reject the claims that the sampled population is normally distributed. Therefore, the data in the study came from a normally distributed population. Therefore, it is required to reject the H₁ hypothesis and accept H₀. The tables were organized based on the item measures and broken down into parts in order to present them coherently.

Table 4‑4: Normality Results of Perception of Lag

One-Sample Kolmogorov-Smirnov Test
		Q.13	Q. 14	Q. 15	Q. 16	Q. 17	Q. 18	Q. 19	Q. 20	Q. 21
N		8	8	8	8	8	8	8	8	8
Normal Parameters^a,b	Mean	3.73	3.71	3.62	3.58	3.64	3.58	3.73	3.29	3.53
Normal Parameters^a,b	Std. Deviation	0.863	0.869	0.834	0.941	0.883	0.965	0.889	0.869	0.815
Most Extreme Differences	Absolute	0.310	0.364	0.341	0.273	0.279	0.269	0.240	0.238	0.317
	Positive	0.223	0.259	0.236	0.194	0.210	0.175	0.182	0.186	0.217
	Negative	-0.310	-0.364	-0.341	-0.273	-0.279	-0.269	-0.240	-0.238	-0.317
Test Statistic		0.310	0.364	0.341	0.273	0.279	0.269	0.240	0.238	0.317
Asymp. Sig. (2-tailed)		.120^c	.250^c	.111^c	.200^c	.2!0^c	.230^c	.220^c	.200^c	.270^c
a. Test distribution is Normal.
b. Calculated from data.
c. Lilliefors Significance Correction.

Table 4‑5: Normality Results of Reaction to Lag

One-Sample Kolmogorov-Smirnov Test
		Q. 22	Q. 23	Q. 24	Q. 25	Q. 26	Q. 27	Q. 28
N		8	8	8	8	8	8	8
Normal Parameters^a,b	Mean	2.51	2.76	2.71	2.67	2.56	3.89	3.91
Normal Parameters^a,b	Std. Deviation	1.058	1.090	1.308	1.108	1.139	1.071	0.949
Most Extreme Differences	Absolute	0.263	0.245	0.240	0.215	0.198	0.228	0.248
	Positive	0.263	0.245	0.240	0.215	0.198	0.175	0.174
	Negative	-0.159	-0.206	-0.193	-0.175	-0.141	-0.228	-0.248
Test Statistic		0.263	0.245	0.240	0.215	0.198	0.228	0.248
Asymp. Sig. (2-tailed)		.000^c	.000^c	.000^c	.000^c	.000^c	.000^c	.000^c
a. Test distribution is Normal.
b. Calculated from data.
c. Lilliefors Significance Correction.

Table 4‑6: Normality Results of Solutions to Lag

One-Sample Kolmogorov-Smirnov Test
		Q. 29	Q. 30	Q. 31	Q. 32	Q. 33
N		8	8	8	8	8
Normal Parameters^a,b	Mean	3.82	4.02	4.24	3.76	4.09
Normal Parameters^a,b	Std. Deviation	0.886	0.753	0.773	0.933	0.900
Most Extreme Differences	Absolute	0.335	0.310	0.258	0.314	0.238
	Positive	0.243	0.267	0.202	0.219	0.162
	Negative	-0.335	-0.310	-0.258	-0.314	-0.238
Test Statistic		0.335	0.310	0.258	0.314	0.238
Asymp. Sig. (2-tailed)		.000^c	.000^c	.000^c	.000^c	.000^c
a. Test distribution is Normal.
b. Calculated from data.
c. Lilliefors Significance Correction.

Relative Importance Index Analysis

The purpose of the RII analysis in the current study is to rank the prevalent themes or factors that may influence a specific phenomenon (LeBrton, 2004). The purpose of using RII in the current study was to examine the importance of specific criteria that is a factor of perceiving and dealing with lag/latency in an FPS game environment. LeBrton (2004) emphasizes that RII is an imperative measure when attempting to look for the contribution of specific variables to an entire system.

The current study measures RIIs by categorizing the items on the question according to their main category of criteria preferences being – lag perception, response to lag, and solutions to lag. Once the RII value was produced the factor was ranked amongst its group to identify the most important criteria in its category. For the current study, importance is given to criteria that are ranked in the top three of each category based on the opinions of the participants.

The first category for which the relative importance index was calculated is perceptions of lag. Each of the factors that make up this category was calculated for relative importance index (RII) and then ranked according to the values produced. Values of RII once rounded to three significant figures were then ranked many of which were ranked into the same value category.

As seen in the table below, the perception of lag related to the number of avatars in a game is ranked the first based on the respondent answers. This factor was explored in question 18 and produced an RII value of 0.625. The next ranking factor which produced an RII of 0.615 is the general cause of lag as indicated by the participants. This factor was explored in question 16 of the questionnaire. Table 4-8 is a supplementary table that describes the descriptive statistics of the question. According to the results, 25 per cent of clients believed that lag occurred from the game client, while another 25 per cent believe it is caused by access link of bandwidth to PC.

Table 4‑7: RII Results of Perceptions to Lag

Question	Factor	RII	Rank
Q18	Lag Related to No. of Avatars	0.625	1
Q16	Cause of Lag	0.615	2
Q13	Lag Frequency	0.594	3
Q17	Lag Related to Game Play	0.563	4
Q19	Playing more than 1 Online game	0.563	4
Q20	Lag Consequence of Specific Games	0.563	4
Q14	Seriousness of Lag	0.500	5
Q15	Duration of Lag	0.475	6
Q21	Decisive Factors of Lag	0.417	7

Table 4‑8: Question 16 Results

Opinion on the Causes of Lag Encountered
		Responses		Percent of Cases
		N	Percent	Percent of Cases
Q16^a	Your PC	2	16.7%	25.0%
	Access Link Bandwidth of PC	3	25.0%	37.5%
	Game Client	3	25.0%	37.5%
	Equipment of Game Savers	2	16.7%	25.0%
	Don't Know	2	16.7%	25.0%
Total		12	100.0%	150.0%
a. Dichotomy group tabulated at value 1.

Lastly, the third-ranked factor the influence respondent’s perception of lag was lag frequency, which produced an RII of 0.594.

The next category of questions within the study focused on the analysis of opinions based on what they believed was to be the most important reaction to lag. Based on the collection of answers obtained, the RII analysis produced the following results that are presented in Tables 4-9. The top three factors that respondents believed were most important amongst the group were ‘lag impact on the game play’ which ranked at 1 with an RII value of 0.750. The item of 2^nd rank went to ‘shared experience of lag’ which is described as seeking other players or friends to speak about the reaction to lag. The variable was ranked as 2^nd with an RII of 0.656.

Also ranking 2^nd due to the identical RII value was ‘quitting the game due to lag’ as a reaction to facing latency in the first person short game. Ranked 3^rd in this particular study is the factor of checking with friends and other players to see if they had faced the same issue of lag when they were playing the free all first-person shooter game. This variable was ranked as such due to its RII score of 0.625.

Table 4‑9: RII Results of Reactions to Lag

Question	Factor	RII	Rank
Q26	Lag Impact on Gameplay	0.750	1
Q25	Shared Experience of Lag	0.656	2
Q27	Quitting Game Due to Lag	0.656	2
Q24	Checking with Others on Lag	0.625	3
Q28	Complaining about Lag	0.613	4
Q22	General Reaction to Lag	0.521	5
Q23	Retrying to Log-in after Lag	0.469	6

The third and final category of factors analyzed was solutions to lag. This category analyzed the factors which players used when trying to fix the issue of lag on their own. The factor that ranked the 1^st with the highest RII values was no solution but the constant emphasis of lag being the greatest issue with online gaming (i.e. issue of lag online). The variable had ranked a score of 0.688.

Table 4‑10: RII Results of Solutions to Lag

Question	Factor	RII	Rank
Q33	Issue of Lag Online	0.688	1
Q31	Software to Diagnose Lag	0.625	2
Q30	Tools to Diagnosis root cause of Lag	0.563	3
Q29	Solving Lag	0.552	4
Q32	Software to Mitigate Lag	0.500	5

The next greatest factor that was seen as being influential for findings solutions to lag was ‘software to diagnose lag’ this factor includes the belief that there is software available to fix the issue of lag in online games. This variable had ranked 2^nd with an RII of 0.625 as seen in table 4-10. The third highest-ranking variable in this study was ‘tools to the diagnosis root cause of lag’. This variable includes tools such as ping and tracer router to resolve the issue of lag. It is seen as a solution with an RII rank of 0.563.

Although the various solutions available for lag were not ranked tremendously on the RII scale. They need to be addressed as they focus on the various ways that gamers believe that lag can be fixed during an FPS game session. Table 4-11 illustrates the various solutions that the sample believed is best used to fix lag during game play.

Table 4‑11: Results of Question 29: Methods Adopted to Solve Lag

Methods Adopted to Solve Lag
		Responses		Per cent of Cases
		N	Per cent	Per cent of Cases
Q29^a	Upgrade PC	3	21.4%	37.5%
	Upgrade access link	3	21.4%	37.5%
	Optimize TCP/IP parameters	1	7.1%	12.5%
	Use proxy server	2	14.3%	25.0%
	Upgrade gateway	3	21.4%	37.5%
	Switch to other ISP	1	7.1%	12.5%
	None	1	7.1%	12.5%
Total		14	100.0%	175.0%
a. Dichotomy group tabulated at value 1.

The table illustrates the frequencies of the sample in which participants indicated their preference of solution to fix any lag issues. The top solution three solutions that participants preferred were upgrading to a better PC, with a 21.4 per cent preference. This means, that it is preferred to change the entire gaming equipment for online gaming. Furthermore, 21.4 per cent of participants believe that the access link for online gaming needs to be upgraded in order to reduce or eliminate lag. Lastly, 21.4 per cent of participants believed that upgrading the gateway for online gaming would be a solution for lag. Only 14.3 per cent of participants believed that a proxy server can be used to reduce or eliminate lag within the gaming environment.

Conclusion

The findings of the data were conducted using data analysis that followed various steps. The first step in the data analysis included testing the questionnaire items reliability using Cronbach’s alpha. It was concluded that inter-item reliability existed in the questionnaire that was developed as alpha values were greater than 0.8 falling under the ‘good’ category. After which normality was tested using K-S testing. Hypotheses were developed to test if the population under study had a normal distribution. The value indicated that indeed, the population was normally distributed with significance values all above 0.05 for each of the items on the questionnaire.

The data analysis concludes with the use of the Relative Importance Index (RII). It was found that for perceptions of lag, the top-ranking factors were ‘lag being related to a number of avatars’ in the game, ‘general causes of lag’, and ‘lag frequency’. The top-ranking of factors for reaction to lag included ‘general lag impact on gameplay’, ‘sharing experience of lag’, quitting the game due to lag’, and checking with others on the same experience of lag’. Lastly, the solutions to lag category had the most important factors as general ‘issue of lag online’ with no solution, ‘software diagnosis of lag’, and ‘tools to diagnose the root cause of lag’ mainly ping and tracer router.

Chapter Discussion and Conclusion

Discussion

The present study had the aim to answer the following research question:

How do FPS players perceive lag?
How do FPS players react to a lag in the game environment?

Using the following objective for the current study:

Examine the issues that FPS players face in a game environment when experiencing lag and lag compensation.
Analyse how different game movement speeds may impact lag compensation.
Investigate what can be considered an acceptable limit to latency based on varying game movement speeds.
Assess the perceptions and reactions of game players in first-person shooter environments with regards to lag and lag compensation.
Build a multiplayer first-person shooter game with a lag compensation algorithm that enables fairness in a game play environment.

Based on the analysis conducted in the previous chapter, the current study was able to answer the research question and the aim of the study. The current study has presented results using quantitative research methods from the positivism school of philosophical epistemology. The use of this research approach has been justified in chapter three of the current study. Based on the philosophical assumptions for conducting the study, it was obvious that a questionnaire need to be developed to ask respondents what they deemed to be important in terms of their perception, reaction and solutions to lag. Hence, using the literature review that was thoroughly conducted, a questionnaire was developed that consisted of 32 items measuring the preferences, reactions and solutions to lag, which was adapted from Lee and Change (2016). The assessment of these criteria was organized into five categories developed from the extensive research conducted in the literature review, being – perception of lag, reaction to lag, and solution to lag.

The questionnaire was planned to be distributed to 10 students in Abertay University who would participate in the free for all, FPS game which was adjusted by the research for lag and speed movement. Of the ten students selected on a random basis, only 8 had participated in the study. The data collected came from eight samples in the study for which a data analysis took place.

The data collected was then coded and organized into IBM’s SPSS v.24 that was used to analyze the data for reliability, normality, and frequency of responses. Both reliability and normality were found in the data, making the analysis produced more reliable for discussion and to draw conclusions on. The data then proceeded for relative importance index (RII). The analysis had provided a great deal of insight and aligned to an extent with the descriptive statistics produced from the questionnaire responses. The RII analysis conducted also concludes that the responses obtained from the participants are coherent which strengthens the questionnaire’s Cronbach alpha measures.

According to the RII results the top factor that influenced the perception of lag was the number of avatars in the game environment. It had ranked with a value of 0.625. This observation is similar to that found in Tseng et al. (2011), which found that lag is strongly (34.5%) and moderately (26.6%) related to the number of avatars on screen. Tseng et al. (2011) had found other factors that were not similar to the results of the present study, primarily their study found that players also associated lag with the time of day that they may be playing an FPS game or other multiply player online games.

Other academics have also reported similar results in which players of first-person shooter games and other online games have no means of fighting the lag that they are facing. This is most likely due to players lacking technical knowledge of competing against lag. The survey results of the current study allow us to conclude that gamers of online multiply player games, specifically first-person shooter games, are looking for solutions that can help in minimizing or outright eliminating lag from their game play experiences. One of the key points that were raised in the solution to lag was a software diagnostic tool that can automatically detect the root cause of lag for gamers and also propose a solution to that issue. This inference is also shared by Tseng et al. (2011).

Conclusion

The present study attempts to thoroughly evaluate the perception, reactions, and solutions to latency in a first-person shooter game that was adjusted for lag and movement speed. The purpose of this was to comprehend a player’s responsiveness to the lag and movement speed in a gaming environment to better understand their perceptions of latency. It should be noted that participants were not made aware of the changes that would be made to lag and movement speed in order to gauge unbiased results. It was found that players in the free for all FPS game had varied perceptions of latency. They had attributed it to various factors. Hence, those factors were ranked and analyzed to see which had the most influence on their perceptions.

In summary, the research does show that lag experience is frustrating to players in a game environment. They perceive lag as being a severe hindrance to their game experience and believe it to be caused by numerous reasons. On a descriptive level, the major cause was seen as the player’s own PC equipment. This is the reason that one of the major solutions to fixing lag was to upgrade the equipment a player was using. It was also found that lag increased the frustrations of the players to seek diagnostic tools for fixing lag.

This solution was ranked as number one in the RII scores making it critical for academics and industrialists of this sector to focus on producing a diagnostic tool that may aid gamers. It is obvious from the study that gamers define lag differently. The results of the study showed that the sample has varying definitions for lag. From the data obtained it is obvious that players have very little knowledge about the technicalities associated with lag/latency and its compensation in a game. Nonetheless, it has become extremely important to educate players about latency so that they may be part of the solution to mitigate or eliminate lag. It is concluded from the findings of the study that greater research into the satisfaction of gamers with regard to lag is greatly needed. The literature review of the current study had yielded few results in this realm. However, the current study attempts to fill in the gaps in the literature with this regard.

Limitation of Current Research

The present research was able to pursue and achieve its objectives and answer its research questions. However, the research was severely limited in several ways. One of the severest limitations was the sample size of the current study. It was only able to include eight samples. The limitation of the sample also extends to its geographic restriction, only students at Abertay University were included in the same. This limited sample size restricts the study from becoming generalized to suit wider populations. Hence the limited results coming from the present study. Another major factor that limited the present study was time. Time management was a severe issue in the research for the researcher especially. It was difficult to conduct an appropriate cross-sectional study that enabled the incorporation of wider scenarios of lag/latency in a first-person shooter game. Gamers were only allowed about five minutes to experience the changes in lag.

Recommendations for Further Research

Future researchers of this field need to focus on developing diagnostic tools that can help gamers with identifying the path between the client and server which is generating. Future research not only needs to focus on the assessment of such as tool in the practical sense but also how this may impact the player’s sense of satisfaction in the game; along with their perception, reaction and solutions to lag. Further research is needed in this field making it crucial to overcome the limitations that were faced in the present study. Therefore, the following research recommendations are made for future researchers to pick up on where the current research had left off.

Future research work in the area needs to have a greater sample size that is more diverse. This means included gamers of different age groups, ethnicities, and socio-economic statuses. A greater diversity of demographics can aid researchers in understanding how the gaming consumer perceives lag. This will also aid in making customized solutions.
It is recommended that future research use a mixed methods research approach in which quantitative and qualitative data is obtained and analyzed. The merger of both these techniques may lead to greater quality of analysis allowing researchers to produce more meaningful results.
Future researchers should develop and test a diagnostic tool that measures game clients, servers, and the network paths between the client and server.
It is necessary for related works to pursue a greater comprehension of how gamers define and understand lag; their interpretation of it, which will allow solution providers to implement improved solutions to mitigate or eliminate the nuisance of lag/latency.

References

Amiri, M, Malik, KPS, Osman, HA and Shirmohammadi, S 2016, Game-Aware Resource Manager for Home Gateways, 2016 IEEE International Symposium on Multimedia (ISM), pp. 403–404.

Amiri, M, Osman, HA, Shirmohammadi, S and Abdallah, M 2016, Toward Delay-Efficient Game-Aware Data Centers for Cloud Gaming, ACM Trans. Multimedia Comput. Commun. Appl., 12, (5s), pp. 71:1–71:19.

Armitage, G 2003, An experimental estimation of latency sensitivity in multiplayer Quake 3, The 11th IEEE International Conference on Networks, 2003. ICON2003., pp. 137–141.

Beigbeder, T, Coughlan, R, Lusher, C, Plunkett, J, Agu, E and Claypool, M 2004, The Effects of Loss and Latency on User Performance in Unreal Tournament 2003®, Proceedings of 3rd ACM SIGCOMM Workshop on Network and System Support for Games, ACM, New York, NY, USA, pp. 144–151.

Bernier, YW 2001, Latency Compensating Methods in Client/Server In-game Protocol Design and Optimization, p. 13.

Beyer, J, Varbelow, R, Antons, J and Zander, S 2015, A method for feedback delay measurement using a low-cost Arduino microcontroller: Lesson learned: Delay influenced by video bitrate and game-level, 2015 Seventh International Workshop on Quality of Multimedia Experience (QoMEX), pp. 1–2.

Brun, J, Safaei, F and Boustead, P 2006, Fairness and playability in online multiplayer games, CCNC 2006. 2006 3rd IEEE Consumer Communications and Networking Conference, 2006., pp. 1199–1203.

Cai, W, Shea, R, Huang, C-Y, Chen, K-T, Liu, J, Leung, VCM and Hsu, C-H 2016, A Survey on Cloud Gaming: Future of Computer Games, IEEE Access, 4, pp. 7605–7620.

Chen, D-Y, Yang, H-T and Chen, K-T 2013, Dude, the Source of Lags Is on Your Computer, Proceedings of Annual Workshop on Network and Systems Support for Games, IEEE Press, Piscataway, NJ, USA, pp. 4:1–4:6.

Claypool, M and Claypool, K 2006, Latency and Player Actions in Online Games, Commun. ACM, 49, (11), pp. 40–45.

Claypool, M and Finkel, D 2014, The effects of latency on player performance in cloud-based games, 2014 13th Annual Workshop on Network and Systems Support for Games, pp. 1–6.

Clincy, V and Wilgor, B 2013, Subjective Evaluation of Latency and Packet Loss in a Cloud-Based Game, 2013 10th International Conference on Information Technology: New Generations, IEEE, Las Vegas, NV, USA, pp. 473–476.

DICE 2014, New Update Addressing “Netcode” Rolling Out, Battlelog, viewed 11 May, 2019, <http://battlelog.battlefield.com/bf4/news/view/new-netcode-update-rolling-out/>.

Henderson, T 2001, Latency and User Behaviour on a Multiplayer Game Server, in J Crowcroft & M Hofmann (eds), Networked Group Communication, Springer Berlin Heidelberg, pp. 1–13.

Huang, C-Y, Chen, K-T, Chen, D-Y, Hsu, H-J and Hsu, C-H 2014, GamingAnywhere: The First Open Source Cloud Gaming System, ACM Trans. Multimedia Comput. Commun. Appl., 10, (1s), pp. 10:1–10:25.

Jarschel, M, Schlosser, D, Scheuring, S and Hoßfeld, T 2011, An Evaluation of QoE in Cloud Gaming Based on Subjective Tests, 2011 Fifth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, pp. 330–335.

Jarschel, M, Schlosser, D, Scheuring, S and Hoßfeld, T 2013, Gaming in the clouds: QoE and the users’ perspective, Mathematical and Computer Modelling, 57, (11), pp. 2883–2894.

Kaiser, A, Maggiorini, D, Achir, N and Boussetta, K 2009, On the Objective Evaluation of Real-Time Networked Games, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference, pp. 1–5.

Lee, SWK and Chang, RKC 2018a, Enhancing the Experience of Multiplayer Shooter Games via Advanced Lag Compensation, Proceedings of the 9th ACM Multimedia Systems Conference, ACM, New York, NY, USA, pp. 284–293.

Lee, SWK and Chang, RKC 2018b, Enhancing the Experience of Multiplayer Shooter Games via Advanced Lag Compensation, Proceedings of the 9th ACM Multimedia Systems Conference, ACM, New York, NY, USA, pp. 284–293.

Lee, SWK and Chang, RKC 2017, On ‘shot around a corner’ in first-person shooter games, 2017 15th Annual Workshop on Network and Systems Support for Games (NetGames), pp. 1–6.

Lee, W and Chang, RKC 2015, Evaluation of lag-related configurations in first-person shooter games, 2015 International Workshop on Network and Systems Support for Games (NetGames), pp. 1–3.

Lee, W-K and Chang, RKC 2015, Evaluation of Lag-related Configurations in First-person Shooter Games, Proceedings of the 2015 International Workshop on Network and Systems Support for Games, IEEE Press, Piscataway, NJ, USA, pp. 11:1–11:3.

Li, Z, Melvin, H, Bruzgiene, R, Pocta, P, Skorin-Kapov, L and Zgank, A 2018, Lag Compensation for First-Person Shooter Games in Cloud Gaming, in I Ganchev, RD van der Mei & H van den Berg (eds), Autonomous Control for a Reliable Internet of Services: Methods, Models, Approaches, Techniques, Algorithms, and Tools, Springer International Publishing, Cham, pp. 104–127.

Oliveira, M and Henderson, T 2003, What Online Gamers Really Think of the Internet?, Proceedings of the 2Nd Workshop on Network and System Support for Games, ACM, New York, NY, USA, pp. 185–193.

Pantel, L and Wolf, LC 2002, On the Impact of Delay on Real-time Multiplayer Games, Proceedings of the 12th International Workshop on Network and Operating Systems Support for Digital Audio and Video, ACM, New York, NY, USA, pp. 23–29.

Quax, P, Monsieurs, P, Lamotte, W, De Vleeschauwer, D and Degrande, N 2004, Objective and Subjective Evaluation of the Influence of Small Amounts of Delay and Jitter on a Recent First Person Shooter Game, Proceedings of 3rd ACM SIGCOMM Workshop on Network and System Support for Games, ACM, New York, NY, USA, pp. 152–156.

Savery, C and Graham, TCN 2013, Timelines: simplifying the programming of lag compensation for the next generation of networked games, Multimedia Systems, 19, (3), pp. 271–287.

Slivar, I, Suznjevic, M, Skorin-Kapov, L and Matijasevic, M 2014, Empirical QoE study of in-home streaming of online games, 2014 13th Annual Workshop on Network and Systems Support for Games, IEEE, Nagoya, Japan, pp. 1–6.

Statista 2019, U.S. monthly video game industry revenue 2019 | Statistic, Statista, viewed 10 May, 2019, <https://www.statista.com/statistics/201093/revenue-of-the-us-video-game-industry/>.

Statista 2018, U.S. most popular video game genres 2017, Statista, viewed 10 May, 2019, <https://www.statista.com/statistics/189592/breakdown-of-us-video-game-sales-2009-by-genre/>.

Tseng, P-H, Wang, N-C, Lin, R-M and Chen, K-T 2011, On the battle between lag and online gamers, 2011 IEEE International Workshop Technical Committee on Communications Quality and Reliability (CQR), IEEE, Naples, FL, USA, pp. 1–6.

Wen, Z and Hsiao, H 2014, QoE-driven performance analysis of cloud gaming services, 2014 IEEE 16th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6.

Zander, S and Armitage, G 2004, Empirically Measuring the QoS Sensitivity of Interactive Online Game Players, p. 8.

Zander, S, Leeder, I and Armitage, G 2005, Achieving Fairness in Multiplayer Network Games Through Automated Latency Balancing, Proceedings of the 2005 ACM SIGCHI International Conference on Advances in Computer Entertainment Technology, ACM, New York, NY, USA, pp. 117–124

Appendix Questionnaire

Section 1: Player Demographics

Q1	What is your gender? (A) Man (B) Woman
Q2	What is your age? (A) 0-12 (B) 13-17 (C) 18-28 (D) 29-39 (E) 40+
Q3	How many years have you played single-player games including PC and video games? (A) 0-1 (B) 1-3 (C) 3-5 (D) 5-10 (E) 10+
Q4	How many years have you played online games such as FPS games and MMORPGs? (A) 0-1 (B) 1-3 (C) 3-5 (D) 5-10 (E) 10+
	Which type of online games do you like to play? (multiple-choices)
	(A) Role playing (B) Real-time strategy (C) First-person shooter (D) Car racing (E) Casual games
	How often do you play online games?
	(A) Rarely (B) Intermittently (C) 0-2 hours a day (D) 3-5 hours a day (E) 6+ hours a day
	What time do you usually play online games?
	(A) before 9 AM (B) 9 AM-12 noon (C) 12 noon-2 PM (D) 2 PM-6 PM (E) 6 PM-10 PM (F) after 10 PM
Q8	What online games do you play now? (Write down their names)
Q9	Which Internet service provider do you use? (A) ADSL (B) Cable Modem (C) FTTB (D) 3G (E) Not Aware (F) Others (Write down the names)
Q10	How does your computer connect to the gateway? (A) Ethernet (B) WiFi (C) 3G (D) Don't know
Q11	How does the gateway connect to the Internet? (A) TANet (B) ADSL (C) Cable Modem (D) FTTB (E) 3G (F) Don't know
	What is the downlink and uplink network bandwidth of your Internet access?
	(A) 2 Mbps / 512 Kbps (B) 4 Mbps / 1 Mbps (C) 8 Mbps / 640 Kbps (D) 3 Mbps / 768 Kbps (E) 10 Mbps / 2 Mbps (F) 20 Mbps / 2 Mbps (G) Don't know (H) Others

Section 2: Perceptions of Lag

Q13	How often do you encounter lag during gameplay? (A) Rarely (B) Occasionally (C) Frequently (D) Always
Q14	How serious is the lag in general? (A) Slightly (B) Moderately (C) Seriously but tolerably (D) Intolerably
Q15	How long does the lag last? (A) Instantly (B) In a few seconds (C) In a few minutes (D) Intermittently (E) Constantly
	In your opinion, what causes the lag you encountered? (multiple-choice)
	(1) Your PC (2) Access link bandwidth of your PC (3) Game client (4) Equipment of game servers (5) Access link bandwidth of game servers (6) Internet core bandwidth (7) Don't know
Q17	To what degree do you feel lag is related to the time of game play? (A) None (B) Weak (C) Moderate (D) Strong
	To what degree do you feel lag is related to the number of avatars on the screen?
	(A) None (B) Weak (C) Moderate (D) Strong
Q19	Do you play more than one online games? (A) Yes (B) No
	If you play multiple games, to what degree do you feel lag is the consequence of particular game(s)?
	(A) None (B) Slight (C) Moderate (D) Strong
	In your opinion, what is the most decisive factor making different levels of lag in different games?
	(A) Game software (B) Equipment of game servers (C) Access link bandwidth of game servers (D) Don't know

Section 3: Reactions to Lag

22.	How do you generally react to lag?
	(A) Ignore them (B) Suffer and continue playing (C) Log out and retry immediately (D) Log out and retry later (E) Reconnect to the Internet and retry immediately (F) Reboot your PC
23.	(Following Q22) If you choose to log out and retry later, how long do you wait before retrying?
	(A) A few minutes (B) Half an hour (C) An hour (D) Next available time
Q24	Do you check with your friends in the game when you encounter lag? (A) Yes (B) No
	When you encounter lag, to what degree do you find your friends in the game also suffer the same problems?
	(A) None (B) Weak (C) Moderate (D) Strong
Q26	In general, to what degree does lag affect your gameplay? (A) None (B) Weak (C) Moderate (D) Strong
Q27	When you quit an online game, to what degree do you think lag is the main cause? (A) None (B) Weak (C) Moderate (D) Strong
28	Where or to whom do you usually complain about lag? (multiple-choice)
	(A) Internet forums (B) Your ISP (C) Game company (D) No public/formal complaints (E) Others

Section 4: Solutions to Lag

29	Which of the following methods have you ever adopted to solve lag? (multiple-choice)
	(A) Upgrade PC (B) Upgrade access link (C) Optimize TCP/IP parameters (D) Use proxy server (E) Upgrade gateway
	(F) Switch to other ISP (G) None (H) Others
30	Do you use tools such as ping and traceroute to diagnose the root of lag?
	(A) Never (B) Seldom (C) Often (D) Always
Q31	If there is a software that can diagnose the root cause of lag, would you download and use it? (A) Yes (B) No
Q32	If there is a software that can mitigate lag, would you download and use it? (A) Yes (B) No
Q33	In general, is lag an issue for you in online game playing? (A) Yes (B) No

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