Background: The Swedish retail pharmacy industry is a dynamic industry that has undergone many changes throughout the past decades. Presently, the pharmacy industry in Sweden consists of five nationwide pharmacy chains (Apoteket AB, Apotek Hjärtat, Kronans Apotek, Apoteksgruppen, and LloydsApotek), three strictly e-commerce pharmacy companies (Apotea, Meds, and Apohem), and 40 private players.    Problem: Due to digitalization, retail industries are nowadays dealing with customers that have different expectations and exhibit changed shopping behaviours. The retail pharmacy industry in Sweden has not been an exception to the trend of having to go through a digital transformation based on society changes and changed customer behaviour.    Purpose: The purpose of this paper is to research how the Swedish retail pharmacy companies have adapted to meet customer expectations and created value for the customer, as a result of digitalization.   Method: A qualitative approach with seven semi-structured interviews have been conducted on company representatives from the Swedish retail pharmacy industry.     Results: Despite the Swedish retail pharmacy industry being controlled by regulations put in place by the state, they have successfully managed to restructure their operations through their digitalization process. This has been done with the customer primarily in mind, through meeting their expectations and continuing to create value.

Abstract Purpose: The purpose of this study is to explore last mile delivery practices among large and medium Swedish omni-channel (OC) retailers and e-tailers. Also, the study aims at analyzing what similarities and differences exist between the two types of retailers. Method: A literature review was carried out in order to gain more knowledge about the topic and develop a theoretical framework, used for analyzing data. Also, a quantitative study was conducted through electronic surveys (secondary data) and website observations (primary data). The 100 retailers were large and medium OC retailers and e-tailers and were selected based on a probability sample.  Findings: The study has shown that there are plenty of similarities and differences between the two kinds of retailers. Some of the delivery practices and options that OC retailers and e-tailers perform similarly are unattended HD (unattended home delivery), attached C&C (click and collect), drop shipping, free solitary C&C (click and collect). There are also differences between retailers when it comes to attended HD, time slot, eco delivery or LTL-Courier (Less Than Truckload-courier). E-tailers offer more competitive remote deliveries with more free remote delivery options. OC retailers leverage on both store network and remote delivery services. Thus, OC retailers can learn how to cope with e-commerce growth and adapt their delivery services as e-tailers do. Limitations: This study has a geographical focus on Swedish OC retailers and e-tailers. The data that was collected has been limited to the logistics variables: delivery mode, velocity, time slot, slot price differentiation, delivery fee, eco delivery, picking location, delivery area and transport service. Theoretical implications: This study is an important step towards contributing to academic theoretical literature regarding last mile delivery practices. Based on the previous frameworks, new logistical variables were added, such as more velocities, delivery fee, eco delivery and drop shipping. These contributions were helpful in the process of exploring characteristics about retailers and how they differ from each other. Managerial implications: This research is valuable for managers and retailers in order to find the best logistical strategy. It could be beneficial for OC retailers who face challenges that e-commerce brings and compete with e-tailers. Keywords: Last-mile delivery, Retailing, Omni-channel, E-tailers, E-commerce. Paper type: Research paper

In this dissertation, new digital image processing methods for hyperdimensional imagery are developed and experimentally tested on remotely sensed Earth observations and medical imagery. The high dimensionality of the imagery is either inherent due to the type of measurements forming the image, as with imagery obtained with hyperspectral sensors, or the result of preprocessing and feature extraction, as with synthetic aperture radar imagery and digital mammography. In the first study, two omni-directional adaptations of gray level co-occurrence matrix analysis are developed and experimentally evaluated. The adaptations are based on a previously developed rubber band straightening transform that has been used for analysis of segmented masses in digital mammograms. The new methods are beneficial because they can be applied to imagery where the region of interest is either poorly segmented or not segmented. The methods are based on the concept of extracting circular windows s around each pixel in the image which are radially resampled to derive rectangular images. The images derived from the resampling are then suitable for standard GLCM techniques. The methods were applied to both remotely sensed synthetic aperture radar imagery, for the purpose of automated detection of landslides on earthen levees, and to digital mammograms, for the purpose of automated classification of masses as either benign or malignant. Experimental results show the newly developed methods to be valuable for texture feature extraction and classification of un-segmented objects. In the second study, a new technique of using spatial information in spectral band grouping for remotely sensed hyperspectral imagery is developed and experimentally tested. The technique involves clustering the spectral bands based on similarity of spatial features extracted from each band. The newly developed technique is evaluated in automated classification systems that utilize a single classifier and systems that utilize multiple classifiers combined with decision fusion. The systems are experimentally tested on remotely sensed imagery for agricultural applications. The spatial-spectral band grouping approach is compared to uniform band windowing and spectral only band grouping. The results show that the spatial-spectral band grouping method significantly outperforms both of the comparison methods, particularly when using multiple classifiers with decision fusion.

In today’s world, it is not enough for companies to consider any one social media channel in isolation. Instead, they must provide their customers with a unified experience across channels and consider interdependencies between channels. Most marketing research that examines user generated content is focused on a single channel and is limited to the English language. This thesis analyses Swedish language content collected from eight different social media platforms: Facebook, YouTube, Instagram, TikTok, Twitter, Tripadvisor, Trustpilot, and Google Reviews. The platforms were compared pairwise by the prevalence of positive, negative, and neutral sentiment in comments and reviews about the theme park Liseberg. The sentiment was predicted using a lexical approach where each word in a wordlist was assigned a weight to denote positive or negative sentiment associated with the word. The study found that there is a statistically significant difference between the positivity, negativity, and neutrality expressed by users on the different social media channels. There was no difference in sentiment between YouTube and Instagram comments, but there were differences in at least one of the three sentiment categories for all other pairwise comparisons of platforms. Having an understanding of the attitudes towards the brand in different channels can support marketers in determining their optimal mix of social media channels. These results are also of interest to researchers who should take the differences between social media platforms into consideration when designing studies around user generated content.

<p>Included in licentiate thesis in manuscript form.</p>

Purpose - The purpose of this thesis is to first describe and secondly to create a deeper understanding of what purpose a store has in omnichannel retailing. It is important to clarify that the study is to be of use for companies as to help them understand their customers' perspective. And how to adapt their omnichannel strategy when one of their channels gets affected. This is to see if a new model is viable in a post pandemic world. Methodology - The study applied a qualitative method where semi-structured interviews was held with employees and with consumers. Findings - The stores’ main purpose during the pandemic has been to act as a hybrid warehouse where you can view the product before fully purchasing it or to utilise BOPIS. The store has also had to change and adapt accordingly. Ultimately the store has proven to stand firm and showed that it still has a place in the market. Practical Implications - The current pandemic has made it more important for retailers to identify when showrooming or reverse showrooming is used. Therefore, a more in-depth analysis on the different approaches to minimise the usage of showrooming and reverse showrooming is necessary. As our findings from data and literature both cases suggest that price and product evaluation are the main reasons why consumers are showrooming. We can draw the conclusion that showrooming, and reverse showrooming is used mainly because of the price and evaluation of the product. Retailers should therefore introduce sales strategies to gain customers in the competitive market and minimise the risk for customers to use showrooming or reverse showrooming. Contribution - This study contributes to the young research area by assessing the effects that showrooming and reverse showrooming may have on retailers. And how to adapt their omnichannel strategy when one of their channels gets affected.

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Robots are being used more and more in today’s society. These robots need to be mobile and have a good understanding of their surroundings. This bachelor’s thesis in mechatronics aims to see how a mobile robot can be constructed, and how it can best map its surroundings. The robot was built to have three omni wheels to allow it to move freely in the plane and stepper motors to provide accurate movement. Ultrasonic sensors were placed around the robot to be used as a tool to determine its surroundings. The brain of the robot was an Arduino UNO, which with the help of an ESP-01, communicated with aserver over Wi-Fi. The server received the data from the ultrasonic senors and drew a map on a web page. Multiple test were made to evaluate the different systems. The robot moved really well and with high precision after some tweaking. The ultrasonic sensors were also very precise and the communication between the robot and the server worked very well. All the different systems were combined to make the robot move autonomously. The robot could navigate by itself and avoid obstacles. Although the mapping worked from a technical point of view, it was hard to read and could be done better. / Robotar är något som används mer och mer i dagens moderna samhälle. Dessa robotar behöver vara mobila och haen god uppfattning om miljön de befinner sig i. Detta kandidatexamensarbete inom mekatronik ska undersöka hur en mobil robot kan byggas, och hur den kan kartlägga miljönden befinner sig i. Roboten som konstruerades hade tre omnihjul för att kunna röra sig fritt längs markplanet och stegmotorer för precis drift. Ultraljudsensorer placerades runt om roboten för att ge den en uppfattning av omgivningen. Hjärnan i roboten var en Arduino UNO som med hjälp av en ESP-01 kommunicerade över Wi-Fi till en server. Servern tog emotsensordata från roboten och ritade upp det som en karta ien webbläsare. Det utfördes tester för att utvärdera de olika delsystemen. Driften på roboten fungerade utmärkt med god precisionefter några iterationer. Ultraljudsensorerna hade också godprecision och kommunikationen mellan roboten och servern fungerade mycket bra. De olika delsystemen kombinerades för att ge roboten självkörning. Roboten kunde navigera själv och undvika hinder. Trots att kartan fungeradeur ett tekniskt perspektiv så var den svårtydd och kunde förbättrats.

This research examines the relationship between online and offline sales in an omnichannel sales environment centered around the selling of parking spaces. This dissertation consists of a pilot study followed by an expanded study. The studies delved into the effects of e-commerce additions upon traditional brick and mortar revenue channels. The parking industry was selected as the backdrop, given the high degree of current relevant concerns in this space around internet sales-related cannibalization concerns against physical stores, and given the author’s substantial access to relevant research data. Data was collected for an intervention group and a control group from a leading parking management firm with thousands of parking garages located across North America. In the pilot study, and using panel data gathered from the firm, we sought to examine the effects of an online intervention to existing aggregate revenues through the implementation of a new 3rd party e-commerce sales channel (Parkwhiz.com). Under the intervention group, data was collected on 1.7 million revenue transactions over an approximately two-year period, from 2016 through 2018 across 15 parking properties in New York City. The control group consisted of 493,950 revenue transaction entries, also spanning a roughly two-year period from 2016 through 2018, and across 28 different parking properties throughout the City of New York. A fixed-effect model was used for analyzing the data, which came with unforeseen challenges of balance, outlier concerns, and sample size. Ultimately, insignificant results were observed, but these were attributed mainly to difficulties in data structure and sample size (N = 386) of daily revenue observations. Despite those challenges, individual summary statistics showed potential strength in the primary hypothesis, and this motivated further examination. In the expanded study, an adapted approach from the pilot was used to correct for a majority of its shortcomings in the data structure, sample size, balance, and modeling. Further, several moderating components were incorporated to test practice relevant relationships between revenue, the competitive landscape, and online search sessions. Using the same primary hypothesis from the pilot study, the expanded work provided for 15 intervention group properties and 15 control group properties in New York City, with a balanced dataset of 90 days pre-intervention and 90 days post-intervention for each property examined in the year 2018 or 2019. The original hypothesis (H1A) evaluates whether or not an online intervention increases total revenue at a given location. Additional hypotheses (H1B) evaluating whether offline revenue sources are affected by the online intervention, (H2) moderation of revenue by the volume of competition using the same online channel, (H3) moderation of revenue by the volume of parking locations in the marketplace (zip code area) regardless of selling online or offline, and (H4) revenue predicted by the volume of online searches for parking occurring in the marketplace. Sample sizes ranged from N = 3,491 to 5,098 across our various regression models. Our overall H1 outcomes (across four different regression models) showed strong statistical significance with p-values less than 0.001, and moderate R^2 scores between 37%-47% for the online ParkWhiz intervention. Online intervention increases revenue per parking space in the range of $1.171 to $1.196 in the experiment. The results provide support for the proposition that adding an online sales channel to an existing body of physical parking facilities is additive, non-cannibalistic and overall productive for the business. Our H2 and H3 study outcomes were inconclusive, as the moderators were not significant. The tests of the moderating effects in H2 and H3 provided no practical results, other than perhaps anecdotal perception to supplement the other findings. The testing of H4 did show significance in the importance of the assessment of online search demand in a given zip code as an amplifier of the effect of online intervention on parking revenue. Search volume is positively related to a change in the net new revenues. Overall, the analysis generated learnings valuable for future researchers to expand upon through better data gathering, statistical models, and analysis. In totality, the desired contribution of this body of research is to provide today’s brick and mortar business manager with strategic insights into the conditions needed to make healthy e-commerce decisions, based on observable market conditions, in an omnichannel environment that combines online and offline models for maximum aggregate revenue growth. Avoidance or minimization of cannibalization between existing channels and new channels can ensure success. Our work demonstrates several critical aspects of the phenomena of successful online and offline channel cohabitation with practical conclusions for the strategic decision-maker to use in reaching that equilibrium, and leaves a discernible path for future researchers to supplement our efforts with additional moderating variables. Keywords: Omnichannel, cannibalization; externality; brick and mortar; platform; two-sided marketplace; e-commerce; parking; retail; online/offline; distribution; multi-channel; cross-channel; offline-to-online service platform, channel addition, mobile apps. / Business Administration/Strategic Management

yes / This paper describes a technique for automatic recognition of off-line printed Arabic text using Hidden Markov Models. In this work different sizes of overlapping and non-overlapping hierarchical windows are used to generate 16 features from each vertical sliding strip. Eight different Arabic fonts were used for testing (viz. Arial, Tahoma, Akhbar, Thuluth, Naskh, Simplified Arabic, Andalus, and Traditional Arabic). It was experimentally proven that different fonts have their highest recognition rates at different numbers of states (5 or 7) and codebook sizes (128 or 256). Arabic text is cursive, and each character may have up to four different shapes based on its location in a word. This research work considered each shape as a different class, resulting in a total of 126 classes (compared to 28 Arabic letters). The achieved average recognition rates were between 98.08% and 99.89% for the eight experimental fonts. The main contributions of this work are the novel hierarchical sliding window technique using only 16 features for each sliding window, considering each shape of Arabic characters as a separate class, bypassing the need for segmenting Arabic text, and its applicability to other languages.