彈幕作為一種新型的實時評論工具，源自日本，發展至中國大陸後被廣泛應用到電影、電視、影音網站、廣告、電子書等諸多領域。隨著彈幕經濟的增長，這個源自於ACG（Animation、Comic、Game）產業的小眾文化漸漸走入大眾視野，彈幕背後的生活方式正在被越來越多的人接受，彈幕影音的使用者也呈現爆發式的增長。 本研究以中國大陸的彈幕影音為例，試圖以沈浸理論來探討彈幕觀影行為。本研究希望探討閱聽眾在彈幕觀影中的沈浸過程，以挑戰與技巧、目的來探討閱聽眾的彈幕觀影的沈浸前因，以愉悅感、注意力和時間扭曲來探討彈幕觀影的沈浸經驗，以使用意願來探討彈幕觀影沈浸的結果。 本研究收集有效問卷556份，結果發現彈幕觀影的沈浸包含愉悅感、時間扭曲感兩項，彈幕觀影技巧、彈幕觀影的目的對彈幕觀影沈浸有顯著正向影響；彈幕閱聽眾的沈浸狀態則會顯著正向影響其使用意願。

虛擬實境技術作為一項內容呈現方式，在技術逐漸成熟到現今也逐漸被接枝於新聞內容上，沈浸新聞學（immersive journalism）也隨之誕生，而它最獨特臨場感（presence）特性也使得虛擬實境新聞能帶給閱聽者完全不同以往的閱讀體驗，然而虛擬實境的引進能否為數位化的新聞產業環境帶來利基，是現在新聞媒體仍在觀望的原因。故本研究設計 2（新聞呈現方式：有、無使用虛擬實境）x 2（新聞類型：軟新聞與硬新聞）的實驗架構，讓受測者（N = 121）以佩戴 Google cardboard 以及手機兩種方式體驗《紐約時報》發行的應用程式「NYTVR」裡兩種不同類型的新聞內容，結果發現使用不管哪種新聞類型的內容，使用虛擬實境技術體驗的組別都能夠提升閱聽者在看觀看完刺激物後的購買意願以及情緒，並且臨場感在這個體驗過程中具有中介效果，這些發現不僅為虛擬新聞研究領域帶來實證上的支持，更給予虛擬實境新聞應用於新聞實務上的立足點。

研究背景與目的：大學生睡眠型態呈現睡眠時相延遲、睡眠不足、睡眠品質不佳的狀況，造成身心健康與學業問題。此種睡眠型態，一方面受到生理發展的影響因素，形成內在日夜節律型態偏向夜貓型的情形，二方面為社會與心理的影響因素，隨著年齡增加，家長對於孩子生活監控程度降低，特別是邁入大學以後，生活自主權增加，大學生有更多的自由安排自己的生活與睡眠時間，而大學生生活時間的安排與規劃，影響著夜晚的睡眠。現今科技可日新月異，科技產品的使用，包括看電視、打電腦與使用手機，成為大學生生活中不可或缺的活動之一。其中，電腦與上網為休閒活動時重要的角色。過去研究發現大學生一天使用電腦約3至5小時以上，國外調查睡前活動的研究發現約42.4％的大學生睡前使用電腦，而睡前使用電腦使得就寢時間延遲，形成總睡眠時數減少，睡眠不足造成白天的疲倦感增加，除此之外也有可能影響入睡時間與睡眠品質，因此本研究目的希望找出電腦使用對於睡眠影響的因素，減少電腦使用對大學生睡眠作息造成的影響。本研究根據訪談的結果及過去的文獻彙整，假設電腦使用使得沈浸狀態（flow）與激發狀態（arousal）較高，進而影響睡眠，包括就寢時間較晚、入睡時間較長、睡眠品質不佳、總睡眠時數不足、週末較晚起床補眠的狀況。 研究方法：本研究為瞭解個體電腦使用的沈浸與激發狀態變化對睡眠的影響，採受試者內設計，以重複測量的方式進行研究，測量受試者一週使用電腦的型態與睡眠之關係。受試者需符合睡前4小時內使用電腦1小時以上的習慣，排除任何生理、心理、睡眠疾患與極端日夜型態者（circadian type），並排除使用非法或影響睡眠的藥物。研究共募集國立與私立大學共76名學生，研究一週間請受試者於睡前填寫電腦使用型態問卷與與沈浸量表、激發狀態量表與睡眠日誌。資料回收後進行階層線性分析。階層一分析個人內每天電腦使用的沈浸程度、生理激發程度與認知激發程度是否可預測各睡眠變項，階層二分析個人間的日夜節律型態與焦慮特質調節沉浸、生理激發與認知激發程度與睡眠變項的關係。 研究結果：本研究發現每人每天睡前4小時電腦使用的內容，包括遊戲類、人際互動類與娛樂活動類的沈浸程度皆比文書作業的沈浸程度來得高，就受試者內的比較而言，當晚上電腦使用的沈浸程度越高，當晚的就寢時間提早、入睡時間減少、總睡眠時數增加與提升睡眠品質。而睡前4小時電腦使用時間長度可預測認知激發程度，但認知激發並無法預測睡眠變項；另外，不論睡前電腦使用內容或總時間無法預測生理激發，但晚上電腦使用後的生理激發程度越高，當晚的就寢時間越晚且總睡眠時數越少。此外，認知激發與生理激發的關係為正相關。在階層二個人間調節變項的分析，由於沈浸程度對睡眠變項的預測，以及生理激發程度對就寢時間與總睡眠時數的預測，皆未有尚未解釋的部分，因此在研究模型中無需再加入調節變項。 研究討論：研究結果發現沈浸程度在睡前電腦使用對睡眠影響的過程中扮演正向角色，但若睡前從事虛擬角色的線上遊戲，雖然沈浸程度偏高，但就寢時間偏晚且總睡眠時數較少；此外睡前的電腦使用時間越長，認知激發越高，而認知激發與生理激發呈現正相關，因此有可能認知激發程度提高，使生理激發程度也越高，而生理激發程度越高，導致就寢時間較晚，總睡眠時數較少。建議睡前選擇電腦使用內容並控制使用時間，以減少電腦使用對睡眠的不良影響。 / OBJECTIVE: College students tend to delay their sleep phase and have high prevalence of sleep problems, such as poor sleep quality and insufficient sleep. Many factors may be associated with the sleep patterns. First, delay sleep phase in college students may be affected by a natural tendency of delayed endogenous circadian phase in during puberty. Second, psychosocial and behavioral factors, such as late evening social events and computer use, may also contribute to these sleep patterns. Among these, computer use has been shown to be associated with poor sleep in previous studies. However, it’s unclear that what mechanisms through which computer use has an impact on sleep in college students. The goal of this study is to identify the underlying factors that mediate the effect of computer use to sleep. According to our pilot study in which college students were interviewed for their computer-use habits and sleep pattern, we hypothesize that mental flow, physical arousal and cognitive arousal are the factors mediating the impacts of computer use to sleep patterns characterize college students, including delayed sleep phase, longer sleep onset latency, insufficient sleep and poor sleep quality. METHOD: Seventy-six college students who are habitual computer users (using computer at least one hour before sleep every day) participated in the study. They were required to complete a set of questionnaires everyday for one week, including the computer-use questionnaire, the Flow Scale, and the Pre-Sleep Arousal Scale. Hieratical Linear Model was conducted to analyze within-individual level (level one) and between- individual level (level two). In our study, within-individual levels were mental flow, physical arousal and cognitive arousal that mediated the impacts of computer use to sleep patterns when college students used computer before sleep every night. In addition, between- individual levels in our study were various circadian types and anxious trait between college students. They may moderate the impacts of mental flow, physical arousal and cognitive arousal to sleep patterns in college students. RESULT: The results showed within-individual level that contents of computer using, including play on-line games, interpersonal interaction, and entertainment, could predict increased flow level. Higher flow level in turn predicted earlier bedtime, shorter sleep latency, more sleep duration and better sleep quality. In addition, physical arousal was not affected by computer use, but had a negative impact on sleep. Higher physical arousal level was able to predict later bedtime and shorter sleep duration. Computer-use time during the four hours prior to bedtime was associated with pre-sleep cognitive arousal. Cognitive arousal did not show significant association with any sleep variables, however. Furthermore, there was a positive relationship between cognitive arousal and physical arousal. In addition, because the results of between- individual levels showed that the mental flow, physical arousal and cognitive arousal completely explained sleep patterns, there was no need to add between- individual moderations. CONCLUSION: Our study showed that flow level while engaging in computer use may have positive effect on sleep. However, playing on-line games before sleep, although may lead to higher flow level, were associated with later bedtime and shorter sleep duration. Also, the more time spending on computer before sleep, the higher the cognitive arousal. Higher cognitive arousal level may be associated with higher physical arousal level. And, higher physical arousal level lead to later bedtime and shorter sleep duration. The results suggested that in order to prevent the negative impacts of computer-use among college students, they should reduce computer using time and avoid on-line games before sleep. Future study can develop intervention program based on current findings to prevent college students from the negative impacts of computer.

Woodruff (1997) 認為企業若沒有由建構滿意度的顧客價值 (customer value) 中做更為深入的了解，則顧客的意見將無法完全引導管理者研擬競爭策略。因此Woodruff (1997) 進而主張顧客價值才是企業當前最主要競爭優勢的來源，而非滿意度而已。Peterson (1995) 的研究指出，透過經驗所給予顧客的價值，是促使顧客願意與廠商進一步建立起長久關係的驅動因子。Spiegelman (2000) 也建議：「廠商若希望能夠將第一次消費的造訪者，進而轉變成為重複購買的顧客，則廠商必須在消費體驗的過程中不斷的傳遞價值」。換言之，體驗價值 (experiential value) 可視為提高顧客忠誠的最佳利器 (Stoel, Wickliffe and Lee, 2004)。再者，Babin, Darden, and Griffin (1994) 認為體驗價值是消費經驗後的主要結果，若研究者沒有確實掌握所知覺的體驗價值為何，則無法清楚了解消費者的消費經驗全貌。然而體驗價值雖然對於行銷研究與管理者相當重要，但有關消費者體驗價值的探討，主要都限制在網路購物的人機互動情境。關於線上人際互動情境中，將可能產生何種體驗價值，學者就較少進行探究 (Mathwick, 2002)。 Pine and Gilmore (1999) 在體驗經濟《Experiential Economics》一書中曾指出，消費者之所以能夠形成難忘且有價值的體驗，關鍵乃在於廠商是否能夠設計一個使消費者完全融入，且如同身歷其境的感受。換言之，引發消費者沈浸當下的消費情境，成為企業傳遞體驗價值給消費者的關鍵因素所在。但很遺憾的，沈浸與體驗價值的關係，過去研究較少深入的分析與探討，而且先前的研究情境卻也僅限於線上搜尋的人機互動經驗 (Mathwick and Rigdon, 2004)，忽略了人際互動的體驗才是消費者最為渴望的經驗 (Spiegelman, 2000)。因此在今日人們相當渴望透過網路進行人際接觸的同時，研究更應深入討論與分析，在線上人際互動情境中，沈浸 (flow) 與各種體驗價值 (experiential value) 的關係。 基於上述，研究將選擇以同時具有人際與人機互動的線上遊戲為主要研究情境，藉此以補足缺乏探討人際與人機互動共有的沈浸經驗之缺口，同時也進一步將沈浸概念擴大其應用的範疇，不在只限於人與特定事物間的沈浸研究。 在研究方法方面，有鑑於人際與人機互動的沈浸概念必須重新被審視並釐清 (Huang, 2006)，並且考量消費者的沈浸狀態是具有動態且連續發生一段時間的特性，同時也由於沈浸本身是一種內心深處的感受，若訪談過程中沒有任何憑據可提供受訪者作事後的回憶，則受訪者將很難描述沈浸當時發生的所有感受與心路歷程 (Csikszentmihalyi and Csikszentmihalyi, 1988)，基於此，本研究選擇能夠幫助受訪者回憶起所有心路歷程的自發性導引法 (auto-driving) (Belk and Kozinets, 2005)，為探討沈浸的質化研究方法，如此才能藉此重新釐清在人際與人機互動情境中，消費者沈浸的定義與構面。更進一步的，由於能夠更清楚掌握沈浸的本質，也才能夠使得研究者可以更為明確的探究出引發沈浸產生的人際與人機互動因子以及在沈浸歷程中消費者所知覺體驗價值類型。 經訪談17位受訪者後，不僅探究出在人際與人機互動媒體情境下的沈浸成份，同時也依據沈浸所包含的特質，對沈浸下了如下的定義：「所謂沈浸是指消費者產生情境推想、整合思維、具象化、情緒感染與情緒激發的狀態。」其中「情境推想」，是消費者猜想線上其他消費者的想法、意圖與動向所釀成；「整合思維」是來自消費者多方思索回應他人策略與執行策略效果所促成；「具象化」則是消費者將線上他人的表情或揣摩想像內容予以圖像化所形塑而成的狀態；「情緒感染」的狀態則源自消費者感同他人情緒的效果；「情緒激發」是來自消費者將個人內心的情緒，藉由肢體或表情予以宣洩的狀態。另外，從消費者的自我沈浸描述內容中，研究得知，前述這五種沈浸成份，其彼此間存有因果關係，而這樣的研究結果，是先前沈浸研究未能確實發現的。 其中，情緒激發的形成深受情緒感染與整合思維兩元素直接影響，又具象化分別會直接影響情緒感染與整合思維兩成份的產生。最後情境推想也會分別引發具象化、整合思維、情緒感染等沈浸成份的產生。 更進一步，研究也根據訪談內容結果得知，影響沈浸的前置因子可分為人際與人機互動兩種類型的因素，其中在人際因素方面，包含具有二元對立目標設定的競玩，以及源自環境成份所營造出探索趣味的嬉玩。同時人際與人機互動情境中所引發的人際互動競爭性與親和性，可營造出競玩氣氛。而人際互動中的不確定性與嘲弄則是構成嬉玩的主要成份。最後影響消費者沈浸經驗的人機互動因子包括驚奇性、因果性、生動性等三種因素。因此這將可以給予研究者與管理者，在設計吸引消費者融入購買或消費情境時的重要參考。 另外，訪談內容的結果也顯示，人際與人機互動的沈浸經驗不只是一種最佳互動消費經驗 (Novak and Hoffman, 1996; Privette and Bundrick, 1987)，同時它包含了許多豐富且多元的體驗價值 (Mathwick, et al., 2001)。換言之，當消費者全心投入在情境推想、整合思維、具象化、情緒感染與情緒激發等任一種活動時，都可以知覺到成就感、尊重、趣味性等這三種價值類型。隨著沈浸經驗本身具有多元且豐富的體驗價值，使得參與線上人際互動的消費者，更因此希望再次透過如此的經驗獲取價值。也因此，沈浸經驗提高了消費者願意再次經歷的意圖。 在經過第二階段研究中一系列沈浸量表的發展與檢測分析後，研究結果顯示，所發展的沈浸量表與體驗量表不論是在探索性因素分析、一階驗證性因素分析與二階驗證性因素分析都具備很好的信度與效度。不僅如此，藉由結構方程式模式的統計分析，研究進一步驗證五種沈浸成份間、沈浸與體驗價值間、五種沈浸成份與體驗價值間、沈浸與持續消費行為間以及五種沈浸成份與持續消費行為間的因果關係。而研究結果顯示，不僅整體模式的配適度達評鑑要求的水準，同時每一個變數彼此間的因果關係也都達顯著。 / Woodruff (1977) maintains that customers’ opinions can lead managers to plan competitive strategies if enterprises can have a profound understanding and analysis of customer value, which influences on the formation of customer satisfaction. Accordingly, it’s Woodruff’s contention that the primary source of competitive edge among present-day enterprises isn’t just concerned with “customer satisfaction”, but with “customer value” as well. According to Peterson’s research (1995), the experience-based value bestowed upon customers is an impetus to customers’ desire to develop a long-term relationship with enterprises. Furthermore, Spiegelman (2000) also suggests that enterprises have to incessantly deliver value to customers in their consumption process if enterprises expect to turn customers purchasing for the first time into those making purchase over and over again afterwards. In other words, experiential value can be seen as the best way to enhance customer loyalty (Stoel, Wickliffe, and Lee 2004). What’s more, Babin, Darden, and Griffin (1994) think that experiential value is the major result of consumption experience, and that observers won’t get a clear, and panoramic picture of the consumption experience in case they can’t tell exactly what the perceived experiential value is. However, though experiential value plays an integral part between managers and marketing research, discussions on customer’s experiential value are primarily confined to the online-shopping machine-interactivity context. As for online person-interactivity context, scholars are to put less emphasis on what experiential value can be generated and delivered to customers under such a context (Mathwick 2002). According to the book Experiential Economics, written by Pine and Gilmore (1999), Pine and Gilmore indicate that whether enterprises can create a context for customers to fully engage themselves in and live vicarious with is the key to the formation of customer’s unforgettable and valuable experience. To put it differently, the consumption environment which can prompt customer’s flow in the present plays a pivotal role in helping enterprises deliver experiential value to their customers. However, it’s a pity that in-depth discussions and analyses of the relationship between experiential value and flow are usually overlooked by previous researchers. Besides, previous research context is limited to online-searching machine-interactivity experience (Mathwick and Rigdon 2004), neglecting that person-interactivity experience is the one that customers yearn for the most (Spiegelman 2000). Therefore, in-depth discussions and analyses of the relationship between various experiential values and flow in online person-interactivity context should be made, especially in modern times when people are desperate for personal exposure through the Internet. As aforementioned, by choosing online games with both person-interactivity and machine-interactivity as the primary research context, the research aims to make up for the lack of discussions on flow experience shared by person-interactivity and machine-interactivity contexts. In the meanwhile, the research puts concepts of flow to a wide range of use, not just in the flow research between people and specific things. As far as the research method is concerned, the research takes the facts into consideration that concepts of flow in person-interactivity and machine-interactivity contexts need re-examining and clarifying (Huang 2006), and that customer’s flow conditions are dynamic and capable of lasting for a period of time consecutively. Moreover, since the flow is a feeling in the recesses of mind, interviewees might have a hard time delineating the course of thought developments and feelings in the flow experience if there are no reminders in the interview process for them to recall afterwards (Csikszentmihalyi and Csikszentmihalyi 1988). Considering the above-mentioned factors, the research chooses a method called “auto-driving”, able to help interviewees recall their courses of thought developments and feelings in their consumption process (Belk and Kozinets 2005), as the qualitative research method for exploring what flow is. In this way, the definition and dimensions of customer flow experience in person-interactivity and machine-interactivity contexts are likely to be clarified. What’s more, researchers can get a more explicit picture of what types of experiential value that customers would perceive in the process of flow are due to the fact that such a method will better the understanding of the essence of flow. Furthermore, once the elements of the flow have been confirmed, the researcher may also clearly ascertain what kinds of the person-interactivity and machine-interactivity factors triggering flow. After the first phase interviews, the present study not only finds out components of flow, but also defines flow as consumers’ state of inferring thinking, integrated thinking, visualization, emotional contagion and emotional arousal. Besides, according to the content of consumers’ self-description about flow experience, the study also found that causal relationships among the five elements of flow which were not found in the previous flow studies. For example, emotional contagion and integrated thinking influenced simultaneously emotional arousal and visualization influenced simultaneously emotional contagion and integrated thinking. Finally, inferring thinking directly influenced visualization, integrated thinking, and emotional contagion. Furthermore, the researcher identifies both person-interactivity and machine-interactivity factors that influence flow in base on the result of interviewees’ description about flow experience. Also, there are ludus which comes from the design of winning or losing and paidia which comes from exploring interesting content in person-interactivity factors. At the same time, the elements of ludus are competition and affiliation, and the elements of paidia are uncertainty and teasing. Finally, the machine-interactivity factors which influence flow include surprise, causality, and vividness. These findings would be practical implications for design consumers’ flow experience in further for the researcher and the management in service marketing. The result of the interviews also proves that flow experience with both person-interactivity and machine-interactivity is not only the optimal interactive consuming experience (Novak and Hoffman, 1996; Privette and Bundrick, 1987), but also delivers the multiple and rich experiential value to consumers. (Mathwick, et al., 2001). On the other word, consumers would perceive experiential value of achievement, reputation, and playfulness when they act with total involvement in one of the inferring thinking, integrated thinking, visualization, emotional contagion, or emotional arousal. Because of flow experience including multiple and rich experiential value, consumers in online person-interactivity would like to have the same flow experience in order to perceive these experiential value again. Therefore, flow experience positively influences re-patronage intention. The result of flow scale development and detection and analysis in the second step of this study shows flow scale is validity and reliability base on the exploratory factor analysis, the first confirmatory factor analysis and the second confirmatory factor analysis. The study further confirms causal relationships among the five elements of flow, causal relationships between flow and experiential value, causal relationships among the five elements of flow and experiential values, causal relationships between flow and re-patronage intention, and causal relationships among the five elements of flow and re-patronage intention by the statistic analysis of structure equation modeling.