Mobile App Usage and its Implications for Service Management – Empirical Findings from German Public Transport

Schmitz, Christoph; Bartsch, Silke; Meyer, Anton

doi:10.1016/j.sbspro.2016.05.492

Cited by 36 publications

(22 citation statements)

References 43 publications

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“…(Walker et al, 2002;Howard & Worboys, 2003;Lopez-Bonilla & Lopez-Bonilla, 2014) found that control is one of the factors that influence customers to use ATMs. Control is the core factor of interaction between the human and the technology (Collier & Barnes, 2015;Schmitz, Bartsch, & Meyer, 2016). In the study conducted by (Mohammed et al, 2016) result found that control factor did not have an impact on the customer hedonic value.…”

Section: Discussionmentioning

confidence: 99%

Relationship Marketing and Information Technology’s Impact on Customer Satisfaction and Commitment

Sleiman¹,

Cai²,

Lan³

et al. 2021

OJBM

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The rapid growth of the technology industry has changed the condition of relationship marketing. The business has simultaneously become more comfortable and more complicated. It is easier because one can send and receive business information in the click of a button, it is complicated because the new technologies bring new demand and challenges to the companies. The use of self-service technology is widespread in China. The purpose of this paper is to show the impact of information technology on relationship marketing and the factors that lead to customer satisfaction and commitment to SSTs usage. SPSS was used for the study. The finding of this study shows that trust is a significant factor for achieving customer satisfaction and commitment, followed by perceived convenience. Customer satisfaction has the highest relationship with customer commitment. This study may help the bank administration to improve the (ATM) self-service technology in a way that will bring more satisfaction to the customer, which will result in commitment.

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Section: Discussionmentioning

confidence: 99%

Relationship Marketing and Information Technology’s Impact on Customer Satisfaction and Commitment

Sleiman¹,

Cai²,

Lan³

et al. 2021

OJBM

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“…However, it can still be improved by extension used in mobile application. As [20] stated that the usage of mobile application changing the way human live in terms of increasing in reliability.…”

Section: Discussionmentioning

confidence: 99%

Simple Screening Method of Maize Disease using Machine Learning

Afendi¹

2019

IJITEE

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Plant leaf diseases are significant issue in agriculture field. Some of the common plant leaf diseases are powdery mildew, dark spot and rust. They are a noteworthy wellspring of an immense number of dollar worth of setbacks to farmers on a yearly premise. Plant breeders frequently need to screen countless number of plant leaves to find the stage of diseases of their crops to perform an early treatments. Therefore, a robust method for field screening is needed in order to spare the farmers and the environment as well. Inappropriate used of treatments such as impulsive pesticides can imperil the environment. Hence, this paper present a simple and efficient machine learning method which is Fuzzy C-Means algorithm to screen leaf disease severity in maize. Fuzzy C-Means is a new algorithm and very efficient to be used in object detection. Therefore, it is applicable to detect disease spot in plant leaf and measure the diseases severity. This field screening method help the farmer to identify the progression of the diseases in their crops quicker and easier than the other field screening techniques.

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“…The original model, presented in Figure 1, was applied to the context of information technology use at work. Previously, a slightly different version of the UTAUT, namely the Technology Acceptance Model (TAM), was applied to transit mobile payment app acceptance (Di Pietro et al, 2015), to understand intentions to use transit apps (Schmitz et al, 2016), and quantify the effect of information on transit use (Kaplan et al, 2017). While the TAM fundamental constructs do not fully reflect the influence of technological and contextual factors underlying user acceptance, the UTAUT has been augmented with various human (internal) and policy (external) factors that may further influence the adoption decision (Venkatesh et al, 2016;Panagiotopoulos and Dimitrakopoulos, 2018).…”

Section: The Proposed Behavioral Frameworkmentioning

confidence: 99%

“…In the literature, travel information is mainly treated as a travel resource streaming from the operators as providers to transit passengers as consumers. Molin and Timmermans (2006), Dickinson et al (2015), Schmitz et al (2016), Khoo andAsitha (2016), andVelazquez et al (2018) focused on the motivation for smartphone app adoption and use. Farag and Lyons (2012) addressed the choice to use pre-trip travel information for long-distance leisure and business trips as a function of information quality, social norms, travel behavior, and attitudes.…”

Section: Introductionmentioning

confidence: 99%

Obtaining transit information from users of a collaborative transit app: Platform-based and individual-related motivators

Sarker

Kaplan

Anderson

et al. 2019

Transportation Research Part C: Emerging Technologies

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The traditional practice of transit information provision considers operators as active communicators of information, while transit users are considered as passive, only receiving information. Encouraging reciprocity and active involvement of users by enabling them to share information may increase transit information quality and ridership. Nowadays, active user participation is starting to take shape with the development of new apps with commercial market potential. This study focuses on willingness to share travel information as part of daily routine transit app use. The applied behavioral framework is the Unified Theory of Acceptance and Use of Technology adapted to the context of information sharing. The empirical analysis consists of estimating a structural equation model on a data set including 1,369 people from Innsbruck and Copenhagen as cities differing in size and general social trust. The results show that the most important motivational factors for information sharing are pro-sharing social norms and self-actualization weighted against effort expectancy, which is more closely related to the logistic effort of using the platform than to network familiarity. Trust in the information provided and social network engagement are secondary motivational factors, with perceived information quality and need of communication being less influential. Greater transit use and interest in level-of-service and real-time information are correlated with greater information sharing motivation. Women and generation Z had higher motivation for information sharing as well as people who reside in Denmark, a country with high social trust.

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Mobile App Usage and its Implications for Service Management – Empirical Findings from German Public Transport

Cited by 36 publications

References 43 publications

Relationship Marketing and Information Technology’s Impact on Customer Satisfaction and Commitment

Relationship Marketing and Information Technology’s Impact on Customer Satisfaction and Commitment

Simple Screening Method of Maize Disease using Machine Learning

Obtaining transit information from users of a collaborative transit app: Platform-based and individual-related motivators

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