Frequent (flier) frustration and the dark side of word‐of‐web: exploring online dysfunctional behavior in online feedback forums

Purpose Following the perspective of frustration theory customer frustration incidents lead to frustration behavior such as protest (negative word‐of‐mouth). On the internet customers can express their emotions verbally and non‐verbally in numerous web‐based review platforms. The purpose of this study is to investigate online dysfunctional customer behavior, in particular negative “word‐of‐web” (WOW) in online feedback forums, among customers who participate in frequent‐flier programs in the airline industry. Design/methodology/approach The study employs a variation of the critical incident technique (CIT) referred to as the critical internet feedback technique (CIFT). Qualitative data of customer reviews of 13 different frequent‐flier programs posted on the internet were collected and analyzed with regard to frustration incidents, verbal and non‐verbal emotional effects and types of dysfunctional word‐of‐web customer behavior. The sample includes 141 negative customer reviews based on non‐recommendations and low program ratings. Findings Problems with loyalty programs evoke negative emotions that are expressed in a spectrum of verbal and non‐verbal negative electronic word‐of‐mouth. Online dysfunctional behavior can vary widely from low ratings and non‐recommendations to voicing switching intentions to even stronger forms such as manipulation of others and revenge intentions. Research limitations/implications Results have to be viewed carefully due to methodological challenges with regard to the measurement of emotions, in particular the accuracy of self‐report techniques and the quality of online data. Generalization of the results is limited because the study utilizes data from only one industry. Further research is needed with regard to the exact differentiation of frustration from related constructs. In addition, large‐scale quantitative studies are necessary to specify and test the relationships between frustration incidents and subsequent dysfunctional customer behavior expressed in negative word‐of‐web. Practical implications The study yields important implications for the monitoring of the perceived quality of loyalty programs. Management can obtain valuable information about program‐related and/or relationship‐related frustration incidents that lead to online dysfunctional customer behavior. A proactive response strategy should be developed to deal with severe cases, such as sabotage plans. Originality/value This study contributes to knowledge regarding the limited research of online dysfunctional customer behavior as well as frustration incidents of loyalty programs. Also, the article presents a theoretical “customer frustration‐defection” framework that describes different levels of online dysfunctional behavior in relation to the level of frustration sensation that customers have experienced. The framework extends the existing perspective of the “customer satisfaction‐loyalty” framework developed by Heskett et al.

The global distribution of Banana bunchy top virus reveals little evidence for frequent recent, human-mediated long distance dispersal events

Banana bunchy top virus (BBTV; family Nanoviridae, genus Babuvirus) is a multi-component single-stranded DNA virus, which infects banana plants in many regions of the world, often resulting in large-scale crop losses. Weanalyzed 171 banana leaf samples from fourteen countries and recovered, cloned, and sequenced 855 complete BBTV components including ninety-four full genomes. Importantly, full genomes were determined from eight countries, where previously no full genomes were available (Samoa, Burundi, Republic of Congo, Democratic Republic of Congo, Egypt, Indonesia, the Philippines, and the USA [HI]). Accounting for recombination and genome component reassortment, we examined the geographic structuring of global BBTV populations to reveal that BBTV likely originated in Southeast Asia, that the current global hotspots of BBTV diversity are Southeast Asia/Far East and India, and that BBTV populations circulating elsewhere in the world have all potentially originated from infrequent introductions. Most importantly, we find that rather than the current global BBTV distribution being due to increases in human-mediated movements of bananas over the past few decades, it is more consistent with a pattern of infrequent introductions of the virus to different parts of the world over the past 1,000 years.

FreeS: A fast algorithm to discover frequent free subtrees using a novel canonical form

Web data can often be represented in free tree form; however, free tree mining methods seldom exist. In this paper, a computationally fast algorithm FreeS is presented to discover all frequently occurring free subtrees in a database of labelled free trees. FreeS is designed using an optimal canonical form, BOCF that can uniquely represent free trees even during the presence of isomorphism. To avoid enumeration of false positive candidates, it utilises the enumeration approach based on a tree-structure guided scheme. This paper presents lemmas that introduce conditions to conform the generation of free tree candidates during enumeration. Empirical study using both real and synthetic datasets shows that FreeS is scalable and significantly outperforms (i.e. few orders of magnitude faster than) the state-of-the-art frequent free tree mining algorithms, HybridTreeMiner and FreeTreeMiner.

Long-term frequent prescribed fire decreases surface soil carbon and nitrogen pools in a wet sclerophyll forest of Southeast Queensland, Australia

Prescribed fire is one of the most widely-used management tools for reducing fuel loads in managed forests. However the long-term effects of repeated prescribed fires on soil carbon (C) and nitrogen (N) pools are poorly understood. This study aimed to investigate how different fire frequency regimes influence C and N pools in the surface soils (0–10 cm). A prescribed fire field experiment in a wet sclerophyll forest established in 1972 in southeast Queensland was used in this study. The fire frequency regimes included long unburnt (NB), burnt every 2 years (2yrB) and burnt every 4 years (4yrB), with four replications. Compared with the NB treatment, the 2yrB treatment lowered soil total C by 44%, total N by 54%, HCl hydrolysable C and N by 48% and 59%, KMnO4 oxidizable C by 81%, microbial biomass C and N by 42% and 33%, cumulative CO2–C by 28%, NaOCl-non-oxidizable C and N by 41% and 51%, and charcoal-C by 17%, respectively. The 4yrB and NB treatments showed no significant differences for these soil C and N pools. All soil labile, biologically active and recalcitrant and total C and N pools were correlated positively with each other and with soil moisture content, but negatively correlated with soil pH. The C:N ratios of different C and N pools were greater in the burned treatments than in the NB treatments. This study has highlighted that the prescribed burning at four year interval is a more sustainable management practice for this subtropical forest ecosystem.

Web-based active learning and frequent feedback: Engaging first-year university students

Web-based technology is particularly well-suited to promoting active student involvement in the processes of learning. All students enrolled in a first-year educational psychology unit were required to complete ten weekly online quizzes, ten weekly student-generated questions and ten weekly student answers to those questions. Results of an online survey of participating students strongly support the viability and perceived benefits of such an instructional approach. Although students reported that the 30 assessments were useful and reasonable, the most common theme to emerge from the professional reflections of participating lecturers was that the marking of questions and answers was unmanageable.

Analysis of Puumala hantavirus in a bank vole population in northern Finland: evidence for co-circulation of two genetic lineages and frequent reassortment between strains

"In this study, for the first time, two distinct genetic lineages of Puumala virus (PUUV) were found within a small sampling area and within a single host genetic lineage (Ural mtDNA) at Pallasjarvi, northern Finland. Lung tissue samples of 171 bank voles (Myodes glareolus) trapped in September 1998 were screened for the presence of PUUV nucleocapsid antigen and 25 were found to be positive. Partial sequences of the PUUV small (S), medium (M) and large (L) genome segments were recovered from these samples using RT-PCR. Phylogenetic analysis revealed two genetic groups of PUUV sequences that belonged to the Finnish and north Scandinavian lineages. This presented a unique opportunity to study inter-lineage reassortment in PUUV; indeed, 32% of the studied bank voles appeared to carry reassortant virus genomes. Thus, the frequency of inter-lineage reassortment in PUUV was comparable to that of intra-lineage reassortment observed previously (Razzauti, M., Plyusnina, A., Henttonen, H. & Plyusnin, A. (2008). J Gen Virol 89, 1649-1660). Of six possible reassortant S/M/L combinations, only two were found at Pallasjarvi and, notably, in all reassortants, both S and L segments originated from the same genetic lineage, suggesting a non-random pattern for the reassortment. These findings are discussed in connection to PUUV evolution in Fermoscandia."

Stream-Close: Fast mining of Closed Frequent Itemsets in high speed data streams

With the emergence of large-volume and high-speed streaming data, the recent techniques for stream mining of CFIpsilas (closed frequent itemsets) will become inefficient. When concept drift occurs at a slow rate in high speed data streams, the rate of change of information across different sliding windows will be negligible. So, the user wonpsilat be devoid of change in information if we slide window by multiple transactions at a time. Therefore, we propose a novel approach for mining CFIpsilas cumulatively by making sliding width(ges1) over high speed data streams. However, it is nontrivial to mine CFIpsilas cumulatively over stream, because such growth may lead to the generation of exponential number of candidates for closure checking. In this study, we develop an efficient algorithm, stream-close, for mining CFIpsilas over stream by exploring some interesting properties. Our performance study reveals that stream-close achieves good scalability and has promising results.

A fast algorithm for finding frequent episodes in event streams

Frequent episode discovery is a popular framework for mining data available as a long sequence of events. An episode is essentially a short ordered sequence of event types and the frequency of an episode is some suitable measure of how often the episode occurs in the data sequence. Recently,we proposed a new frequency measure for episodes based on the notion of non-overlapped occurrences of episodes in the event sequence, and showed that, such a definition, in addition to yielding computationally efficient algorithms, has some important theoretical properties in connecting frequent episode discovery with HMM learning. This paper presents some new algorithms for frequent episode discovery under this non-overlapped occurrences-based frequency definition. The algorithms presented here are better (by a factor of N, where N denotes the size of episodes being discovered) in terms of both time and space complexities when compared to existing methods for frequent episode discovery. We show through some simulation experiments, that our algorithms are very efficient. The new algorithms presented here have arguably the least possible orders of spaceand time complexities for the task of frequent episode discovery.

A unified view of the Apriori-based algorithms for frequent episode discovery

Frequent episode discovery framework is a popular framework in temporal data mining with many applications. Over the years, many different notions of frequencies of episodes have been proposed along with different algorithms for episode discovery. In this paper, we present a unified view of all the apriori-based discoverymethods for serial episodes under these different notions of frequencies. Specifically, we present a unified view of the various frequency counting algorithms. We propose a generic counting algorithm such that all current algorithms are special cases of it. This unified view allows one to gain insights into different frequencies, and we present quantitative relationships among different frequencies.Our unified view also helps in obtaining correctness proofs for various counting algorithms as we show here. It also aids in understanding and obtaining the anti-monotonicity properties satisfied by the various frequencies, the properties exploited by the candidate generation step of any apriori-based method. We also point out how our unified view of counting helps to consider generalization of the algorithm to count episodes with general partial orders.

Fast algorithms for frequent episode discovery in event sequences

In this paper we consider the process of discovering frequent episodes in event sequences. The most computationally intensive part of this process is that of counting the frequencies of a set of candidate episodes. We present two new frequency counting algorithms for speeding up this part. These, referred to as non-overlapping and non-inteleaved frequency counts, are based on directly counting suitable subsets of the occurrences of an episode. Hence they are different from the frequency counts of Mannila et al [1], where they count the number of windows in which the episode occurs. Our new frequency counts offer a speed-up factor of 7 or more on real and synthetic datasets. We also show how the new frequency counts can be used when the events in episodes have time-durations as well.

Generalized frequent episodes in Event Sequences

Discovering patterns in temporal data is an important task in Data Mining. A successful method for this was proposed by Mannila et al. [1] in 1997. In their framework, mining for temporal patterns in a database of sequences of events is done by discovering the so called frequent episodes. These episodes characterize interesting collections of events occurring relatively close to each other in some partial order. However, in this framework(and in many others for finding patterns in event sequences), the ordering of events in an event sequence is the only allowed temporal information. But there are many applications where the events are not instantaneous; they have time durations. Interesting episodesthat we want to discover may need to contain information regarding event durations etc. In this paper we extend Mannila et al.’s framework to tackle such issues. In our generalized formulation, episodes are defined so that much more temporal information about events can be incorporated into the structure of an episode. This significantly enhances the expressive capability of the rules that can be discovered in the frequent episode framework. We also present algorithms for discovering such generalized frequent episodes.

A unified view of the apriori-based algorithms for frequent episode discovery

Frequent episode discovery framework is a popular framework in temporal data mining with many applications. Over the years, many different notions of frequencies of episodes have been proposed along with different algorithms for episode discovery. In this paper, we present a unified view of all the apriori-based discovery methods for serial episodes under these different notions of frequencies. Specifically, we present a unified view of the various frequency counting algorithms. We propose a generic counting algorithm such that all current algorithms are special cases of it. This unified view allows one to gain insights into different frequencies, and we present quantitative relationships among different frequencies. Our unified view also helps in obtaining correctness proofs for various counting algorithms as we show here. It also aids in understanding and obtaining the anti-monotonicity properties satisfied by the various frequencies, the properties exploited by the candidate generation step of any apriori-based method. We also point out how our unified view of counting helps to consider generalization of the algorithm to count episodes with general partial orders.